Global Manufacturing » solar energy technology

Advanced Materials

Wed, 28 Oct 2009 16:25:58 +0000

Focused studies typically contain:
Chemical information: composition and processing
Measurement methodology: techniques and conditions
Physical characteristics: crystallography data, density
Mechanical properties: elastic moduli, strength, hardness, toughness, creep, wear
Thermal properties: specific heat, diffusivity, conductivity, expansion.

The data typically are provided as a function of temperature. Additionally, data pertinent to specialized applications, such as critical temperatures for high temperature superconductors, may also be contained in the reports.

To access the numeric data on the web, click on the name of the report or its icon.

   Alumina (sintered)                [Printed report: out of stock]

   Silicon Carbide (sintered)        [Printed report: available]

   Titanium Diboride                   [Printed report: available]

   Y:123 High-Tc Superconductor      [Printed report: available]

Topical Studies for Specific Properties

Topical studies typically contain data on a particular property for a broad spectrum of materials. Chemical compositions, measurement methods, and measurement conditions are identified, and supplementary data may be included also. (For example, hardness and Young’s elastic modulus might be included in some records of a study on fracture data.)

To access the numeric data on the web, click on the name of the report or its icon.

   Elasticity of Oxide Ceramics          [Printed report: available]

   Toughness Data for Ceramics          [Printed report: available]

   Fracture Data for Oxide Glasses      [Printed report: out of stock]

Green Manufacturing Is A Strategic Priority

Tue, 27 Oct 2009 15:46:20 +0000

In the not-too-distant future, environmentally benign manufacturing will become one of industry’s greatest strategic challenges, not only from an engineering perspective, but from a business and marketing perspective as well.

Many large, multinational companies are cognizant of impending overseas environmental regulations and growing consumer demand for a new generation of environmentally friendly products, and they are beginning to formulate their response. Some have embraced the notion that green products and production techniques are a competitive weapon. But many manufacturers, especially smaller ones in the United States, are far behind in acknowledging and addressing the environmental concerns of governments and consumers, according to a soon-to-be released analysis from the World Technology Evaluation Center at Loyola College in Maryland.

“There is a real serious concern in Japan and Europe and we don’t see the same thing here except for one trend which is for larger multinational companies to attempt to project an image of social responsibility,” says Timothy Gutowski, chairman of the WTEC Environmentally Benign Manufacturing panel and a professor at MIT. “My feeling is that in many cases it’s sincere. They offer their own environmental assessment and metrics which look real, but they might be difficult to confirm from an outside source.”

An example of a company that seems to be headed down the wrong path is General Motors, says Gutowski. Over the summer, the company said it would mass market the Hummer, the gas-guzzling behemoth that is based upon a military design. “I would hope that social responsibility would be a better business strategy because the other one in which they acknowledge their automobiles are bad for the environment is really flirting with the old kind of mold,” says Gutowski. “There is a potential business risk to doing what GM is doing.”

GM’s Hummer and its widespread use could trigger legislation and heated reaction from consumers who are driving smaller, energy efficient vehicles.

Other companies complain about environmental regulations and their inability to meet more stringent requirements. For example, when the European directorate for the environment said it would eliminate brominated flame retardants used in electronics, Siemens said that it was going to be difficult to find a replacement. It wasn’t long thereafter that Sony Corp. said it had a viable alternative and asked how many tons of the new material anybody cared to buy.

In California, most automobile companies are complaining about meeting new clean air emissions limits. Yet Honda and Toyota have developed and produce new engines to meet and exceed requirements. “For many of these companies, it is part of their business strategy,” says Gutowski.

U.S. manufacturers must drop their skepticism of the green movement and begin to address the deleterious impact they and their products have on the environment. The trend lines are alarming, and when Gutowski started as chairman of the WTEC panel looking into the subject, he counted himself as a green movement doubter. “I asked if they really wanted me to chair this panel because I was skeptical myself,” he says. As director of the MIT Laboratory for Manufacturing and Productivity, Gutowski was familiar with trends regarding emissions reductions, and felt there were not serious problems. “I’m a convert,” he says after studying the data.

“There is a master equation for the environment where you multiply the per capita GDP by the impact per unit of GDP,” he explains. The world’s population is projected to increase by a factor of two over the next 50 years and the GDP per capita is likely to go up by a factor of four or five. “To keep things the way they are right now, the environmental impact has to be reduced by something on the order of a factor of 10,” he says. “You have to roll up your sleeves and get to work.”

Looking at it from a global perspective of population growth, total emissions, rainforest depletion and all the other maladies afflicting the earth, “it’s pretty easy to see that in the next 50 years there are going to be major disasters,” but they are probably not going to happen in the United States, he notes. “If you’re a multinational, those will be your customers and your labor source. You don’t want to be the tobacco industry poster boy for manufacturing.”

Industrialists would also be well advised to stop arguing about what they perceive to be as a lack of scientific evidence to support environmental degradation and global warming theories. “There is something going on [with the Earth] and you’d have to be pretty foolish to ignore that there is something going on here,” he says. “It’s like O.J. Simpson. You can’t quite connect the dots…”

No other sector of the economy comes close to the manufacturing sector in generating vast volumes of waste. The Europeans have implemented take-back laws for autos, electronics and appliances. Without comparable take-back laws in the United States, multinational companies are going to be forced to decide if they want to design a separate product for the North American market. I got my car hire Honolulu Airport after recent visit to the manufacturing factory.

To take a look at the slides from WTEC’s upcoming Benign Manufacturing analysis.

As much fresh water has been withdrawn in the last 30 years as in the last three centuries.
Globally, there is a 160 billion cubic meter overdraft of groundwater per year.
The rate of increase in atmospheric carbon dioxide between 1970 and 2000 is nearly double that between 1960 and 1970 (1.5 ppm vs. 0.88 ppm per year).
Carbon emissions have increased by more than 1.5 times since 1970.
The U.S., EU and Japan are by far the world’s biggest producers of solid waste, with the U.S. at about 14 times that of Japan and the EU combined.
Solid waste strategies: In Japan, it’s “Minimize at source”; In Europe, it’s “Producer responsibility”; In the U.S., it’s “There’s always more space.”

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Advanced Control & Optimization

Wed, 21 Oct 2009 16:34:38 +0000

Improve profitability by increasing production and decreasing costs with Honeywell’s modeling and multivariable advanced control and optimization products.

Ongoing advanced control and optimization of plant operations ensures responsiveness to inevitable changes in business demands through close linkages with planning, scheduling, and management functions. Delivered through the unified Experion™ Process Knowledge System (PKS) architecture, Honeywell solutions improve profitability by increasing throughput, reducing costs, increasing yields, and improving product quality.

Advanced Control and Optimization products address all aspects of advanced process control and optimization—from improving regulatory loop control—to globally optimizing the entire process based on a unique layered approach. This model allows new technologies to be easily added at any time to a common platform that meets optimization objectives without compromising future opportunities to improve business performance.

Honeywell has extensive experience in the many hierarchical layers of advanced process control technology.

Unit Manufacturing Processes

Tue, 20 Oct 2009 16:57:05 +0000

What Are Unit Manufacturing Processes?

Manufacturing involves the conversion of raw materials, usually supplied in simple or shapeless forms, into finished products with specific shape, structure, and properties that fulfill given requirements. This conversion into finished products is accomplished using a great variety of processes that apply energy to produce controlled changes in the configuration properties of materials. The energy applied during processing may be mechanical, thermal, electrical, or chemical in nature. The results are meant to satisfy functional requirements that were defined during the product design stage.

In the past, design, materials engineering, and manufacturing were often treated as independent engineering specialties. However, modem manufacturing must be cost-effective and timely. This requires that everyone involved in the entire product life cycle work together concurrently to provide a functional product that can be produced efficiently, can be operated reliably, and is easy to maintain and recycle (Taguchi, 1993). This report identifies a large number of opportunities for improving unit processes. These can be considered as future options for the concurrent engineering teams.

Manufacturing a product or component usually requires the integration of a number of processes. For example, the initial process may involve casting a metal into a mold to produce a desired shape. Next, the casting may be machined with cutting tools to generate surfaces of specified form. Finally, a surface treatment may be employed to improve the durability of the part. Each of these three individual operations—casting, machining, and surface treatment—is a unit manufacturing process. For brevity, in this report they will be referred to as ”unit processes.” They are the individual steps required to produce finished goods by transforming raw material and adding value to the workpiece as it becomes a finished product.

Mass-Change Processes

Mass-change processes are characterized by the removal of material through the use of mechanical, thermal, chemical, or electrical energy.¹ In most instances, the workpiece density is not altered; however, the material microstructure may be modified, particularly at the work surface. Workpiece chemical composition is, in some cases, affected in a small surface region. Mass-change processes are employed in most manufacturing enterprises in intermediate and final processing operations. Workpiece materials span the spectrum of metals, ceramics, polymers, and their composites. High-performance workpiece materials generally are processed by tooling made from higher-strength materials. For example, diamond is used as a tooling coating to process ceramics and ceramic matrix composites.²

Processing costs associated with mass-change processes are directly related to the material properties of the workpiece and to the tolerance and surface finish requirements of the final part. Considerations of operation setup time and cost of fixtures and tooling must be included in the evaluation of the process economics.

Mass-change processes can be grouped into traditional (chip-making) and nontraditional processes. Chip-making processes remove unwanted workpiece material by exploiting shear deformation and fracture mechanisms. The basic. for easy payment to buy products you can through payday loan

¹	Mass-change processes can also add material, such as by laser cladding or a plating operation. However, most of the discussion in this report relates to material removal processes.
²	Care must be taken in using diamond tools for machining metals that are strong carbide formers, such as steel, since the diamond tends to react with such materials if the interface temperature is above about 500 °F.

Architectures for A Self-Sustaining Work Environment

Wed, 14 Oct 2009 17:12:26 +0000

Control system architectures that feature a closed feedback loop involving the process, sensors, controller, and actuators are a step beyond the traditional approach. Control algorithms, such as the PID (proportional, integral, derivative) algorithm, reside in the controller, which is a special type of digital computer. The challenge is to control what is basically a dynamic analog process (e.g., machining) with discrete digital logic. The control architecture must be designed to ensure that the process can always operate optimally under the presence of various uncertainties. Thus there may be multiple layers of feedback control loops (e.g., servo-control loops around the machinery itself, control loops around the tool and the workpiece for fine adjustment of operating condition, etc.).

The design of feedback control algorithms is affected by a number of factors. The key issue arises from the dynamics involved in the process. The controlled variable does not respond instantaneously to the controlling input, which results in a characteristic response curve with dynamic delay. Process models implemented in the controller must manage the dynamics properly. A fixed-parameter controller has difficulty in keeping up with the nonlinear, time-varying behavior of a process. Good control performance at one operating condition can give way to poor performance at another operating condition. These models may be further constrained by the amount of bandwidth for the feedback loop (i.e., the closed-loop response speed) and the product specifications, such as error tolerance.

Control algorithms currently used in manufacturing are commonly simple PID control algorithms that use a low-order transfer function model. This technology is adequate for traditional machining operations in which the machining speed is low. Also, the performance limitation of these PID controllers provides for only a low level of closed-loop performance in unit manufacturing processes, which is reflected in the final product quality level.

Sophisticated control architectures are required for modem unit processes that inherently possess time-varying, nonlinear process dynamics and are high performance in terms of speed and control accuracy (e.g., high-speed machining). For instance, a manufacturing manager recently observed that “a high-speed spindle is worthless unless the machine can feed fast enough to exploit it and the cnc [computer numerical control] is fast enough to keep everything under control” (Coleman, 1992). Advances in control theory, as well as those in microprocessor and digital signal processing technology achieved over the last several decades, can be and should be utilized aggressively to face these new challenges in modern manufacturing.

There are several advanced control methodologies applicable to manufacturing process control. One type of controller adjusts set points as a result of data received from sensor arrays (Hardt, 1993; Ulsoy and Koren, 1993). For example, a model-based adaptive controller¹ could employ an algorithm to compensate for dimensional errors induced by thermal distortion of workpieces.

Adaptive and robust control theory has been an active research topic for the past two decades. The research addresses the problem of how to attain optimum system performance when a process model is not known precisely in advance, the operating conditions are variable, the process parameters vary nonlinearly during operation, etc. The philosophy behind adaptive control theory is that the controller must adapt its control gains so that the overall system remains at or near the optimal condition in spite of varying process dynamics. Adaptive control is a key element to provide flexibility to unit manufacturing processes, which must be responsive to rapidly changing needs of products. On the other hand, the philosophy behind robust control theory is that a fixed-gain controller should be selected, so that the performance of the overall system remains acceptable under variations of process dynamics.

There is significant potential benefit to applying adaptive and robust process controllers. Disturbance observer theory, a robust control methodology, has been shown to be ideally combined with feed-forward control algorithms to provide high accuracy performance for servo-systems, which are essential in high-speed machining. Successful experimental results have been reported for adaptive force control in machining and adaptive weld-pool control in welding.

Important forms of adaptive control are the self-tuning controllers that were developed to overcome the limitations of fixed-point controllers in responding to time-varying process dynamics, variable operating conditions, nonlinear process dynamics, and lack of operator expertise during control-loop commissioning. Self-tuning controllers use process identification algorithms to estimate or track the time variation of key process parameters in real time. Based on these results, control parameters are computed in real time to ensure optimal system performance.

Two entirely different types of self-tuning controllers have been developed—expert systems and process models. An expert system consists of a set of rules that are derived from the knowledge of experienced process engineers and operators. A fuzzy logic controller is a viable candidate to translate human

knowledge to control strategies and algorithms suitable for computer implementation. Advantages of the expert system approach are that it is robust and thus additional rules can be readily added and that a process model is not required. But there are some disadvantages. The expert system usually is developed using a particular controller structure and thus cannot be readily ported to another type of controller. Also, the rule base itself can not be readily analyzed.

The model-based controller uses rigorously defined performance criteria, and hence mathematical analysis of these criteria is possible. It can be adapted for implementation in different controller structures, and it may be used for process diagnostics, such as to locate a failed sensor or actuator. The disadvantages include the chance that the model structure may not match the physical process; for example, an actuator dead zone or backlash could cause underestimation of process gain. Also, rapidly occurring process changes can cause problems if the model execution time is too slow.

The controller of the future will most likely incorporate both the expert system and the model-based technologies. A critical issue is the customization of these modem control algorithms to specific manufacturing applications such that stable performance results.

“Internal state” has been a key concept in modem control, and control theory has been advanced together with estimation theory. Estimation theory provides methodologies to estimate “state,” which may not be directly measured. The estimated state can be utilized for state feedback control as well as for monitoring and failure detection.

Learning control can be used to learn the optimum control input through repeated trials (Dagli, 1994). When unit processes repeat the same task, this control methodology fine tunes the controller’s performance. For example, in injection molding, the piston speed must be controlled so that the flow of molten plastic reaches all parts of the mold and no voids are created. Learning-control algorithms can be combined with simulation models and operational data to evaluate the performance of each trial injection. The time profile of the piston speed is adjusted after every trial until a quality product is produced. The number of trials required depends on the complexity of the process. This type of scheme also has been tested in machining to compensate for low-velocity friction forces. It has been demonstrated that a dozen or so trials are sufficient to construct a compensation signal to remove undesirable glitches, which are visible in the part geometry as irregularities in machining that are caused mainly by static friction.

Intelligent control has received increasing attention over the past few years. Intelligent control systems have the ability, to varying degrees, to find strategies autonomously in an uncertain environment. Intelligent controllers rely on a knowledge base, which may contain experts’ knowledge about operations of unit

processes. The knowledge base may come from process study and modeling and may be updated by a learning mechanism during operation. Intelligent controllers may provide a signal to switch operational modes for a process responding to sensor outputs. The development of strategies for intelligent controllers includes expert systems.

Introduction of a new controller usually requires some modification to other machine functions. For example, in some cases the controlling input (i.e., manipulated variable) for adaptive force control in machining is the tool feedrate. The adjustment of feedrate requires coordination of the machine tool’s servo-controllers as well as its computer numerical control functions. Traditional computer numerical controls generate reference signals for servo-loops after linear and circular interpolation in accordance with the tool feedrate supplied by part programmers. However, this approach will not be feasible if the feedrate is varied in real time.

Process Control

Tue, 13 Oct 2009 16:36:48 +0000

Manufacturing process control and quality assurance are elements of a quality management system, which is the set of policies, procedures, and processes used to ensure the quality of a product or a service. It is widely acknowledged that quality management systems improve the quality of the products and services produced, thereby improving market share, sales growth, sales margins, and competitive advantage, and helping to avoid litigation. Quality control methods in industrial production were first developed by statistician W. Edwards Deming; the adoption of these ideas in post–World War II Japan led to the production of more reliable goods, with fewer defects, than those of the United States and western Europe, spurring the subsequent global success of many Japanese firms.

The International Organization for Standardization has outlined quality principles and the procedures for implementing a quality management system in ISO 9000:2000, ISO 9001:2000 and other documents. These documents have become the gold standards of best practices for ensuring quality and, in many fields, serve as the basis for regulation. Barnes¹ notes that “ISO 9000 guidelines provide a comprehensive model for quality management systems that can make any company competitive.”

An important element of quality assurance is the collection and analysis of data that measure the quality of the raw materials, components, products, and assembly processes. Exponent statisticians can help companies comply with ISO 9001 standards by developing good data collection and analysis techniques during the design, development, and production stages. Specifically, Exponent statisticians are experienced in:

Acceptance sampling
Statistical process control (SPC), including Six Sigma techniques
Troubleshooting studies

Acceptance sampling is conducted to decide whether a batch of product (e.g., supplier components or finished units) is of acceptable quality. Rather than testing 100% of the batch, a random sample of the batch is tested, and a decision about the entire batch is reached from the sample test results. Acceptance sampling was originally developed during World War II to test bullets; since then, numerous military and civilian standards have been developed to encompass various types of quality measurements, and testing and sampling methods. Exponent statisticians are familiar with these standards and can assist clients in evaluating available alternatives—sampling by variables vs. attributes, use of single vs. double or multiple sampling, rectifying vs. non-rectifying with respect to nonconforming items—to determine an appropriate sampling plan.

Statistical Process Control (SPC) is an effective method of monitoring a production process through the use of control charts. By collecting in-process data or random samples of the output at various stages of the production process, one can detect variations or trends in the quality of the materials or processes that may affect the quality of the end product. Because data are gathered during the production process, problems can be detected and prevented much earlier than methods that only look at the quality of the end product. Early detection of problems through SPC can reduce wasted time and resources and may detect defects that other methods would not. Additionally, production processes can be streamlined through the identification of bottlenecks, wait times, and other sources of delay by use of SPC.

Troubleshooting Studies – If a problem is identified in the end-of-the-line product, a troubleshooting study can be conducted to determine whether changes in certain inputs (e.g., raw materials or process characteristics) are associated with the output variables. Such studies involve the analysis of contemporaneous data recorded on production inputs and outputs. Statistical regression techniques or classification methods can detect associations between raw materials or process attributes and end-of-the-line product outcomes. Although these observational studies cannot definitively prove the existence of a cause-and-effect mechanism, results of troubleshooting analyses may suggest potential targets for corrective actions, as well as off-line experiments or further measurements and analyses to confirm the root cause of the manufacturing problem.

Green Manufacturing Technology Wedges

Tue, 13 Oct 2009 16:17:29 +0000

The last blog looked at the complete product life cycle to see the breadth of manufacturing – from material extraction through production and distribution to end of life. We want to drill down a bit more on the production phase of that cycle.

In my lectures discussing green manufacturing I often use a “Google earth view” of manufacturing to zoom in from the enterprise level to the lowest levels on the shop floor – each step increasing the level of detail shown and, consequently, exposing more opportunities for improvement. (In fairness, this was originally the idea of one of my recent grad students – Athulan Vijayaraghavan). It’s a great analogy.
We’ve all used this feature on Google – put in a destination and with rotations and magnification the desired address moves into clearer and clearer view until we are staring at our destination with startling detail.

Same thing for manufacturing. We can start at the factory/enterprise level – move in closer to the production floor, then to the machine lines, on to individual machine tool, down to the spindle and table, and then, finally, to the tool-material cutting zone. (I’m a machining person by training so my analogies usually use metal cutting! But, this works with any enterprise to process zoom.) A representation of this zoom is below.The interesting thing is that, if you look more and more closely, you see the many areas for “greening” the process. It’s like a tide pool at the ocean – you stare into it and there seems to be nothing there. But, bit by bit, you see little creatures moving around and, finally, are amazed at how much life is in that little pool.

I like to refer to these opportunities for greening as “technology wedges” after a concept proposed by Pacala and Socolow to address the big gap between the present trajectory and impact of CO2 on the atmosphere (business as usual – BAU) and a sustainable level – and how to close this gap in 50 years. (The full citation is “Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies,” Science 13 August 2004: Vol. 305. no. 5686, pp. 968 – 972.) They reason that, rather than trying to find one “silver bullet” to correct this increasing mismatch between what we need and what we are doing, we should concentrate on “technology wedges” – small advances and improvements that, when added up, have the effect of a large change in the way we do business.

Their wedges include efficiency improvements, carbon capture and storage from power plants, renewable power, etc. The specific wedges they propose are not the main interest here. But, the idea has real merit.

If we look closely along the view from enterprise to cutting tool, as in our zoomed view, we can see many opportunities for green technology wedges. Some are simple improvements. Others are more substantive and may require new technology or, for the entrepreneurs, offer opportunities for new businesses.

A complete discussion of this concept is in a paper I co-authored in 2007 (Technology Wedges for Implementing Green Manufacturing, NAMRI Trans., 35, 2007, pp. 193-200 – if you want a copy send me a note.)

In that paper we proposed a set of rules for applying technology wedges for green manufacturing:

the cost of materials and manufacturing (in terms of energy consumption and Green House Gas (GHG) emissions, etc.) associated with the wedge cannot exceed the savings generated by the implementation of the wedge (or wedges) over the life of the process or system in which it is employed.
the technology must be able to be applied at the lowest possible level in the process chain.
the cost and impact of the technology must be calculable in terms of the basic metrics of the manufacturing system and the environment. That is, cost and impact must be expressible in units of dollars (or euros, yen, yuan, etc), carbon equivalent, global warming gas creation or reduction, joules, cycle time and production rate, quality measures, lead time, working capital and so on relative to present levels of consumption, use, time, etc.
the technology must take into consideration societal concerns along with business and economy, and
there must an accompanying analytical means or design tool so that it can be evaluated at the design stage of the process or system. It must be an integrated approach.

These are general rules that should be considered when evaluating green technology solutions (wedges). Since the paper was written some refinement of these basic rules could be added but, overall, they are applicable.

I’ll include some applications of this approach to manufacturing systems in the future. In the meantime – take a close look at your product life cycle, or production system and processes, to see where green technology wedges can be implemented.We can always start with ID lanyards with logo of save the planet to create awareness.

Micro-electro-mechanical systems (MEMS)

Mon, 12 Oct 2009 16:14:47 +0000

Systems that couple micromechanisms with microelectronics. Such systems are also referred to as microsystems, and the coupling of micromechanisms with microelectronics is also termed micromechatronics. Micromechanics refers to the design and fabrication of micromechanisms that predominantly involve mechanical components with submillimeter dimensions and corresponding tolerances of the order of 1 micrometer or less. The types of systems encompassed by MEMS represent the need for transducers that act between signal and information processing functions, on the one hand, and the mechanical world, on the other. This coupling of a number of engineering areas leads to a highly interdisciplinary field that is commensurately impacting nearly all branches of science and technology in fields such as biology and medicine, telecommunications, automotive engineering, and defense. Ultimately, realization of a “smart” MEMS may be desired for certain applications whereby information processing tasks are integrated with transduction tasks, yielding a device that can autonomously sense and accordingly react to the environment.

Motivating factors behind MEMS include greater independence from packaging shape constraints due to decreased device size. In addition, the advantages of repeatable manufacturing processes as well as economic advantages can follow from batch fabrication schemes such as those used in integrated circuit processing, which has formed the basis for MEMS fabrication. Many technical and manufacturing trade-offs, however, come into play in deciding whether an integrated approach is beneficial. In some cases, the device design with the greatest utility is based on a hybrid approach, where mechanical processing and electronic processing are separated until a final packaging step. Two broad categories of devices follow from the transduction need addressed by MEMS: the input transducer or microsensor, and the output transducer or microactuator.

Microfabrication technology

The development of process tools and materials for MEMS is the pivotal enabler for integration success. A material is chosen and developed for its mechanical attributes and patterned with a process amenable to co-electronic fabrication. Two basic approaches to patterning a material are used. Subtractive techniques pattern via removal of unwanted material, while additive techniques make use of temporary complementary molds within which the resulting structure conforms. Both approaches use a mask to transfer a pattern to the desired material. For batch processes, this step typically occurs via photolithography and may itself entail several steps. The basic process is to apply a photoresist, a light-sensitive material, and use a photomask to selectively expose the photoresist in the desired pattern. A solvent chemically develops the photoresist-patterned image, which then may be used as a mask for further processing.

Subtractive processing is accomplished via chemical etching. Wet etching occurs in the liquid phase, and dry etching or gas-phase etching may occur in a vapor phase or plasma.

A primary microfabrication technology that has been used for most commercial devices is bulk micromachining, which is the process of removing, or etching, substrate material. The important aspect of precision bulk micromachining is etch directionality. The two limiting cases are isotropic, or directionally insensitive, and anisotropic, or directionally dependent, up to the point of being unidirectional.

An alternative processing approach to bulk microfabrication was driven by the desire to reduce the fraction of the substrate area that had to be devoted to the mechanical components, thereby allowing a larger number of device dies per wafer. The approach, termed surface micromachining (SMM), realizes mechanical structures by depositing and patterning mechanical material layers in conjunction with sacrificial spacer material layers.

Applications

A highly successful device that is fabricated with both bulk and surface micromachining is the integrated pressure transducer. The process sequence uses surface micromachining techniques to form a polysilicon-plate-covered cavity. Application areas include air pressure sensing in automobile engines, environmental monitoring, and blood pressure sensing. Similar processing has resulted in the integration of surface-micromachined polysilicon inertial reference proof masses with microelectronic processing, yielding single-chip force-feedback accelerometers.

The use of surface micromachining technology to implement microactuators has resulted in steerable micromirror arrays with as many as 1024 × 768 pixels on a chip. These arrays have revolutionized digital display technology. Further electrostatic microactuator designs are possible and may be extremely intricate, such as a torsional ratcheting actuator fabricated with five polysilicon levels (Fig. 1). These types of devices are suited for a variety of micropositioning applications. Processing based on deep-x-ray lithography has been used to produce precision magnetic microactuators. One such microactuator directly switches a single-mode optical fiber in a 1 × 2 switch configuration

Micro-opto-electro-mechanical systems (MOEMS)

Sat, 10 Oct 2009 16:13:37 +0000

A class of microsystems that combine the functions of optical, mechanical, and electronic components in a single, very small package or assembly. MOEMS devices can vary in size from several micrometers to several millimeters. MOEMS may be thought of as an extension of micro-electro-mechanical systems (MEMS) technology by the provision of some optical functionality. This optical functionality may be in the form of moving optical surfaces such as mirrors or gratings, the integration of guided-wave optics into the device, or the incorporation of optical emitters or detectors into the system. The term may be confused with micro-opto-mechanical systems (MOMS), which more properly refers to microsystems that do not include electronic functions at the microsystem location. MOEMS is a rapidly growing area of research and commercial development with great potential to impact daily life. The basic concept is the miniaturization of combined optical, mechanical, and electronic functions into an integrated assembly, or monolithically integrated substrate, through the use of micromachining processes derived from those used by the microelectronics industry. These processes, utilizing microlithography and various etch (subtractive) or deposition (additive) steps on a planar substrate, enable the production of extremely precise shapes, structures, and patterns in various materials.

The microsystems realized by these techniques can have many unique capabilities. The miniaturization that is realized is useful in itself, allowing the systems to be utilized as sensors or actuators in environments that were not previously accessible, including inside living organisms, in hand-held instruments, or in small spacecraft. The miniaturization also allows for high-speed operation of the system, as the operating speed of mechanical systems is related to their inertial and frictional properties as well as the actuating forces. Optomechanical systems have been historically constrained in this area because of the mass required for stable optical elements and the extremely precise alignment requirements of most opto-mechanical systems, which limits the forces that can be tolerated for rapid motion. In the more integrated forms of MOEMS, the systems are prealigned by the precise fabrication processes, eliminating one of the more expensive aspects of assembling conventional optical systems. The miniaturization along with the scalability of microfabrication processes allows the development of massively parallel opto-mechanical systems, with millions of moving parts, that would not be possible in conventional technologies. MOEMS can incorporate detection and drive electronics in close proximity to provide improvements in signal-to-noise ratio for sensors and simplified interfaces for actuated systems. Ultimately, these electronics may be monolithically integrated in some technologies. Because of the production volumes achievable with micromachining techniques, MOEMS are potentially much less expensive than their conventional counterparts.

DESIGN AUTOMATION REQUIREMENTS TO SUPPORT INTEGRATED PRODUCT DEVELOPMENT

Fri, 09 Oct 2009 18:17:48 +0000

In many companies, product and process design are fragmented and difficult to manage and coordinate. CAD/CAE tools automate the design and development process but, in many cases, cause the rapid proliferation of designs without regard to the impact on the rest of the organization. While these tools are to varying degrees integrated, often these systems will be used to create an item’s geometry on paper to communicate with other functional areas of a company.

One survey indicated that the typical company re-creates an item’s geometry five or more times in such areas as customer proposals or marketing specifications; conceptual design; detail design; finite element analysis; other engineering analysis; detail drafting; fabrication or assembly sketches; workcell device programming; tooling and fixture design; and training and service manuals. Each time part geometry or product design information is independently maintained in a separate system or independently created on paper, another source of redundant design information is created that needs to be managed.

Non-integrated systems also require additional effort to transfer data from one system to another. This allows errors to creep into the process, and data can be mis-handled or lost. Delays are inherent in this process and extra effort is required to coordinate activities.

Technology and information integration represent one dimension of overcoming these traditional problems. Integrated design and manufacturing automation systems and databases are the basis for the Engineering blueprint of the future. This will allow manufacturers to cost-effectively improve product and process design while facilitating the integration of design activities with the production process.

Product and process design will be greatly enhanced through the use of integrated databases and information systems to maintain and optimize use of design information. Product and process design information must be treated more as a corporate-wide resource. This information must be stored and maintained in a logical, consistent, non-redundant and usable manner. There must be a shift to definition-oriented design information that can directly drive downstream processes with little or no human interpretation and planning. Current standards such as IGES need to be improved upon so that this data can be readily accessed and used without regard to technical constraints.

Evolving standards such as the Standard for the Exchange of Product Model Data (STEP) will provide a more complete set of product data in a neutral format. This design information must be distributed to workstations, controllers, and other systems as required for use. Changes to product and process design data must be managed in accord with the company’s data access and configuration management procedures.

By focusing on maintaining product and process design information electronically, paper-based representations of this data can be minimized. As paper drawings are avoided, there will be reduced manual handling and storage of documents, reduced time to access the most current design of a part, and prevention of errors from avoiding the use of outdated drawing information. Design and administrative activities can be streamlined. When design information is maintained electronically, it can be readily analyzed and designs improved so that more mature and producible designs are developed more quickly. Most importantly, this is the basis for definition-oriented designs.

However, maintaining definition-oriented product and process design information electronically requires a number of supporting technologies. Further, these islands of technology must be linked physically, organizationally and electronically to achieve this integration of data. These technologies include the following:

CAD/CAE with solids modeling and features representation as the mechanism for defining and maintaining product design information electronically and extensive analysis and simulation of products early in the development cycle
Product data management to manage product data in digital form, provide configuration management of this data, and facilitate the development process workflow
Automation of process design, process specification and manufacturing planning through definition-oriented design information and tools such as computer-aided process planning (CAPP) and workcell device programming (e.g., NC, robotic, and computer-aided inspection and test equipment)
Communications and data interchange of product design information internally and externally with suppliers and customers

When these technologies and integration concepts are effectively used, they will improve communication of product and process design within the engineering function, across the enterprise and externally with suppliers and customers.

PRODUCT DESIGN

Computer Aided Design and Engineering (CAD/CAE) systems are the key to developing and maintaining product design information electronically. Two dimension (2-D) systems, oriented to drafting, maintain relationships of lines, arcs, and circles electronically. The output of these systems requires human interpretation to obtain meaningful information. However, as CAD systems move toward solids, feature-based and parametric representation of items, this design information can be more meaningfully used without the same level of human interpretation.

Solids Modeling

One basis for this design approach is to maintain a complete three dimensional solid model of each finished and in-process part in a product definition data base. A rigorous representation of each part’s boundaries avoids the ambiguities in a 2-D and 3-D representation of a part. As parts and products become more and more complex, traditional 2-D and 3-D representations, even with hidden line removal, become difficult to understand and interpret. Solids modeling enhances visualization and communication of the design intent. Solids modeling with the evolution toward product or assembly modeling provides a more useful mechanism to represent how parts are put together to form assemblies. Newer solids modeling systems provide a complete definition of part geometry, topology and tolerances. This complete definition coupled with features representation provide information that can be more readily interpreted and used by downstream automated processes in manufacturing.

Features-based representation of design information and parametric design can enhance and augment solids-based representation of the design. Specification of features (e.g., holes, counter-bored holes, threads, pockets, slots, notches, cut-outs, bosses, fillets, chamfers, ribs, flanges, etc.) or design primitives can simplify the creation of the design. Designing based on the geometric abstractions inherent in solid modeling’s constructive solids geometry (CSG) or boundary representation (B-Rep) approaches is a more artificial and difficult to master process than specifying features. With a features-based approach, a part’s design then comprises a basic shape and a hierarchy of features and associated attributes. Feature attributes include dimensions and tolerances. Other information can be associated with individual features to provide design guidance such as design rules, restrictions, producibility indexes, costs, and process requirements.

Product design data such as features, dimensions, tolerances, finishes, etc., can be associated with specific manufacturing processes (e.g., a hole to a drilling operation) and even a specific piece of equipment. This capability will allow design tools to provide producibility guidance directly to the designer while the product or part is being developed. It also re-orients the designer from thinking in terms of just part geometry to also thinking in terms of manufacturing processes and, therefore, costs and quality implications associated with the part design. This type of definition-oriented design information will then facilitate generative process planning and computer-aided manufacturing.

A valuable aspect of modern CAD systems are their ability to not only render the design, but to capture its intent. The concept of capturing design intent is based on incorporating engineering knowledge into a model by establishing and preserving certain geometrical relationships. The wall thickness of a pressure vessel, for example, should be proportional to its surface area, and should remain so even its size changes. Parametric design allows a standard geometry to be specified once for an approved group of parts. By establishing one or more dimensions or characteristics as variable parameters with mathematical relationships to other dimensions, features or characteristics, multiple parts or assemblies can be readily specified by only varying these parameters while maintaining overall geometric and mathematical relationships. This approach provides variation with a minimum of design effort and process capability.

Analysis and Simulation

CAE also provides tools to help improve the maturity of the design by simulating the design and avoiding or minimizing the need to build and test prototypes. Finite element analysis as well as other engineering analysis tools can be used for testing loads and stresses, heat transfer, fluid flow, kinematics, circuit output and timing, and assembly interaction. By simulating important characteristics electronically, the design can be debugged and precious development time reduced. Newer analysis tools go a step further through design optimization capabilities. The designer specifies basic part geometry and geometry details that an optimization program is allowed to manipulate. By then stating design parameters such as weight requirements and required loads, the program will manipulate the part geometry to optimize meeting the requirements.

Reliability and maintainability are increasingly important considerations in product design as customers are increasingly considering the life cycle costs of a product. Tools are emerging to simulate product operating cycles, heat build-up, and wear, all significant factors in product failure. Solids modeling can be used to model accessibility to components and assemblies in products and systems for maintenance purposes and indicate more optimum approaches to designing the product for service and maintenance. As these tools are refined and merged with design tools, the designer will be in a better position to consider the life cycle costs in the design of products.

Knowledge-Based Engineering

Artificial intelligence and, in particular, knowledge-based engineering systems provide a capability to define rules for effective product and process design in an integrated manner. Design rules can include producibility guidance to more effectively mesh the product design with the company’s process capabilities. Expert and knowledge-based engineering systems have already been developed to help configure complex products such as computer systems as well as design products such as turbine engine blades. This configuration guidance can be extended to help design products to order from basic, pre-designed product modules quickly and inexpensively.

Assembly Modeling

Solids modeling, features and attribute relationships can be the basis for more complete product definition. In addition to rigorously defining geometry and topology of individual parts, product assemblies can be defined through solids modeling by defining the:

Instances or occurrences of each part in a hierarchical manner similar to a bill of material structure
The relative location of each instance or occurrence of the part in terms of the part’s x, y and z coordinates relative to the assembly’s base or reference point x, y and z coordinates
For each instance or occurrence of a part, the part’s orientation in relation to the assembly’s orientation
Vectors or axes of rotation to describe movement of parts within assemblies

This approach can yield a complete definition of the product’s geometry and topology at any level in the product structure.

Product Structure

Since the relationship of a product’s parts is a logical one maintained by the information system rather than a fixed physical relationship as represented on a drawing, it is possible to readily maintain more than one relationship. This will allow different views of part relationships in assemblies to correspond to the various departmental needs (e.g., engineering and manufacturing product structures), while maintaining rigor and consistency of the product’s definition through this single data base. Thus, this one logical data base can support product and process design requirements as well as maintain part relationships to serve as a manufacturing bill of materials for MRP II. An integrated approach to developing, organizing and maintaining part and product definition data facilitates the design process, makes design data more readily usable and enhances integration with process requirements.

PRODUCT DATA MANAGEMENT

This product definition data base will not only provide geometric part information, but it can maintain information about the part’s (or assembly’s) various physical properties, functional characteristics, process requirements, cost, producibility, and design guidelines. Use of an integrated product definition data base allows an organization to concentrate its product data management efforts on this data base. Configuration management practices can be super-imposed on top of product data management functions. Access control to each element in the product definition data base can be specified. Read only access can be given to personnel not directly involved with the design, development and planning process. Creation and maintenance access can be given to the individuals responsible for product and process design.

Engineering Changes

Engineering changes can be facilitated with this configuration management and administrative control embedded within the system. CAE/ CAD tools will enable engineering changes to be more thoroughly developed and analyzed to better define change impact. Once a design has been created, it can be checked-out electronically to a workstation for engineering changes. When the changes have been made, it can be returned to the central database and placed in a queue for electronic approval by designated parties. In this manner, a Change Control Board (CCB) can even “convene” and provide individual member’s input electronically. In addition to supporting engineering analysis, information related to procurement, inventory, manufacturing and cost is available for members of the CCB to evaluate, designate the effectivity of the change and determine the disposition of existing items.

PROCESS DESIGN

Product design must logically extend beyond part geometric information, drawings and parts lists. It must include the design or specification of manufacturing processes including:

The specification, design and layout of production equipment and processes
Process plans to define how the product will be manufactured with the given production processes and capabilities
Workcell device programming (e.g., NC, robotic, insertion equipment, coordinate measuring machines, vision and computer-aided test equipment)
Tool and fixture design

These process design, development and workcell device programming tasks need to become linked to part or product design and development. CAD tools can work with part geometry and features to design or specify processes and workcell envelopes. Graphic production simulation tools can be used to test the resulting production system. Product definition, feature specification and group technology classification information drive computer-aided process planning. Part geometry maintained in CAD is also used to develop tool paths, other NC programs, electronic component insertion programs, and photomasks. This geometry also supports tool and fixture design. Functional characteristics, geometry, and specifications stored in the product definition data base can be used to derive automated test equipment programs and coordinate measurement equipment programs.

When product definition information is developed and released, process engineering information must be similarly developed and released in this type of integrated environment. This will assure that when a new part is introduced or an engineering change is made, that the electronic release for production includes the correct process plans, tool requirements and workcell device programs for the latest configuration and process capability. As computer-aided manufacturing technology is utilized and integrated with the product definition data base, part geometry and process information can be passed directly to production process equipment in DNC-fashion. The release procedures will include the electronic release of this information into appropriate libraries to download to workcell controllers or make available to manufacturing personnel on-line as required. The physical drawings, process plans and NC tapes will not need to be manually assembled and coordinated to support manufacture of an item.

DATA INTERCHANGE

Engineering and product definition information must be communicated and used across the enterprise. In addition, the use of Engineering information extends beyond the company’s facility. Engineering needs to exchange product definition and configuration data including part geometry and other textual information with suppliers and customers. Electronic Data Interchange (EDI) is a first step in the commercial world to utilize standards for ordering parts and material, but it lacks interchange of geometric information.

The Initial Graphics Exchange Specification (IGES) is the current standard for exchanging geometry data, but it does not contain all required digital product data and IGES translations may not provide a complete translation of geometric data. More comprehensive standards will be required. The Standard for the Exchange of Product Model Data (STEP) is an effort to establish product data standards related to physical design information (geometry, topology, tolerances, and form features, functional design information, product administrative information, and product life cycle information). Establishing a single product data model with tools to provide ready access to this product data will enable direct use of this data and enable a move away from the traditional engineering drawing and other product documentation. This is intended as a successor to IGES and as a standard for a more complete definition of product information and, thereby, facilitate improved data interchange, communication and interpretation of product data.

These standards will not only allow paper drawings to be replaced with electronic representations, but will serve as a common digital product model to more readily communicate product data throughout the enterprise and to external organizations as required.

TAKE YOUR MANUFACTURING BUSINESS GREEN

Thu, 08 Oct 2009 16:11:28 +0000

As more people are becoming concerned with our environment and the effects that big and little manufacturing companies are having on the environment, you are going to need to make some changes in how you are going about your business. Currently there are already rules and regulations that you must follow to help with our environment, but more companies are finding that just following those rules is not enough, there clients are wanting safer products because that is what their customers are demanding. Therefore, as a manufacturer you are going to have to decide if going green is right for you.

Here are some things that you are going to need to do in order to make your manufacturing green. Keep in mind that some of these tips might not apply to your manufacturing plant and just because they do not apply to your plant doesn’t mean you can make any changes.

Tip one:

One of the first things that you will need to do is to find some alternate means of getting energy for your manufacturing plant. There is plenty of new technology that is available that will allow you to choose different ways of obtaining energy. This doesn’t mean that all of your energy has to come from alternative means, but the less amount of traditional energy sources you use the lower your air pollution will be.

Tip two:

Another thing that you should do is to find ways that your finished product can be recycled. Many products are currently being recycled today such as old cell phones, ink cartridges, soda cans, paper, etc. The best part is that as time goes on more products are becoming a part of the recycling trend. If you can find a way to recycle or reuse your product you are well on your way to doing, your part in keeping your customers happy and the landfills less crowded. Not only should you look into finding ways to recycle your finished product, but you can also look into recycling any wasted product or excess parts that you might have left over.

Manufacturing products often means that you have damaged products that cannot be sent out to your customers or you have unfinished products that will not be useful for anybody. Going green entails looking into ways to reduce the impact those unneeded parts and products will have on the environment.

Tip three:

If you make products that are consumed by other people or are for personal use, you should look into using all natural products. Many products today can be made from natural ingredients for about the same cost. Even if it is, a little more expensive, people are still willing to pay the extra price for products that are free of chemicals. The natural products that you are using can also be used to package your final product, if you need to use packaging at all anymore.

Tip four:

A great way to go green is to use recycled products for your packaging. This will help reduce the amount of waste that is in the landfill and can lower your costs because you are not using new materials each time, and everyone knows that recycled products are cheaper to buy than new products.

Tip five:

Set up recycle bins throughout your manufacturing plant for your employees to use to recycle everyday items, such as plastic bottles, metal, glass, paper, etc. The little things often make a bigger difference than we are aware of. Recycling needs to be done at all levels of your manufacturing plant in order for it to be successful.

Turning your manufacturing plant into a green manufacturing plant is one of the best ways to keep your clients happy, you don’t have to do everything natural even just a few small steps can help save energy and produce less waste and pollution. With more business checks, you can actually lead a greener business.

Green Manufacturing: The Next Great Leap Forward

Sat, 03 Oct 2009 15:44:22 +0000

Regardless of your feelings towards the severity of the situation, there are forces moving to make it more and more expensive and difficult to continue “business as usual” when it comes to manufacturing. This is only one of the motivators for embodying green manufacturing. Reduction of waste (in any form) is desirable. Henry Ford (yes … that Ford) said over 80 years ago in his book “Today and Tomorrow” (1926) that “…we will not so lightly waste material simply because we can reclaim it— for salvage involves labour. The ideal is to have nothing to salvage.” What a great restatement of green manufacturing!

Anyone who has parents who lived through the great depression (the one in the 30s, that is) will have observed their behavior with respect to resources. My dad, who was a just out of high school during the depression, set a high standard for use, reuse, repair, life extension – the whole package. One of his remarks, when observing something that, to me, had little use was “it will come in handy even if we never use it.” He was a tool and die maker, then department supervisor for John Deere Horicon Works in Wisconsin. He got me interested in manufacturing as a kid by taking my brother and I to “the shop” on Saturday morning so he could see what the second shift on Friday night had accomplished in the tool room. They certainly subscribed to Henry’s philosophy and he’d be very upset today to see the throwaway society we’ve evolved into.

I suspect that Henry Ford’s observation was less motivated by his concern for the environment (I won’t touch that one) than with his concern for wasting money. There is nothing wrong with that. In fact, if you review the major leaps forward in manufacturing over the last several hundred years they were prompted by, first, a realization of the cost of something that had been considered “free” or of insignificant value, second, of a way to modify or fix the procedures or system to remove that cost and realize the savings (meaning – convert to money) and, third, reduce the price of the product/increase the quality or functionality of the product manufactured reflecting this savings.

In the case of folks building things off shore, and subject to the whims of exchange rates, etc., they would use this new found savings to insure themselves against currency swings or other export related challenges.

What am I speaking of? Let’s review some of these big leaps.

The evolution of manufacturing in terms of productivity, flexibility, response time, work philosophy or business model and market responsiveness/customer “pull” shows the evidence of tremendous changes from the earliest organized industry or manufacturing in the 1800s up to today. These changes correspond to distinct periods of production. These periods can be characterized as the craft period, mass production period, flexible production period and lean manufacturing period as follows:

Craft production: In the early days of industry, skilled workers, or artisans, worked on a variety of machines to create specially built products with high labor input. Very little mechanization was available, and these artisans were completely on their own in terms of process planning, timing, and techniques used.

Mass production: The late 1800s to early 1900s saw the development of mass production (for example Eli Whitney’s cotton gin, or Henry Ford and the assembly line). Mass production yielded a dramatic reduction in direct labor (at the expense of the craftsman), higher production rates, more control of the process, ability to satisfy larger customer demand, interchangeable parts, and the first elements of automation. The cost per piece dropped substantially as a result. F. W. Taylor was a driver of this also.

Flexible production: In the 1980s, thanks to visionaries like Eugene Merchant in the United States, and Japanese manufacturers like Taiichi Ono and the Toyota Production System (among others) the efficiency of these production systems increased tremendously. Unnecessary operations are done apart from the manufacturing process on the machine, resulting in high levels of machine availability and utilization. This is due, in part, to computers and clever methods of preparing workpieces and tooling for the machine offline, while assuring quality and accuracy.

Lean Manufacturing and “Mass Personalization”: The late 1990s and early 2000s saw the introduction of true response to customer demands. This came because of the ability to manufacture customized products in small quantities with mass production efficiency and short lead times (that is, the time from when an order is placed to when the product is delivered). Strict quality methods ensure the “first part correct,” built on Ohno’s ideas and the quality control methodology taught by W. Edwards Deming and others. These methods eliminated backup stock, inventory, and the cost associated with keeping mounds of parts and products available to cover manufacturing faults or an inability to plan for or respond to customer demand. That is, make it right – don’t stock extras to cover mistakes or out of control processes.

Each of these changes noted above occurred because of a realization that an improved system of manufacturing could be attained if the system was “designed and optimized” based on an understanding of some new criteria. These criteria included more control of the process and standardization introduced by Henry Ford, better use of manufacturing machinery and increased productivity introduced by Taiichi Ohno, and reduced inventory and buffer stocks for quality production, the ideas of Edwards Deming and Taiichi Ohno. And, they all had a monetary value that could be assigned to do the required “cost-benefit” analysis.

What is the next big change? I believe it will be a move to account for the “embedded costs” associated with energy, carbon footprint, re-use and re-manufacturing leading closer to sustainable production. But, how will this be accomplished?

The motivation for this change is the need to include the true cost of producing goods, from the point of resource extraction to the end of life and reuse or recycling, in the cost of the products. This true cost is more than the “value added” through these stages (which one might argue is already included, according to the best of capitalism).

The environmental and social costs associated with the lifecycle need to be added as well. This will not be easy; we don’t purport that all the information or tools needed to do this exist today. We need to incorporate costs for all the embedded energy, materials and other resources, labor, impacts on the environment, and accompanying social requirements and impacts (among others) in the price of the product. Then, when consumers purchase a new computer, automobile, airplane ticket, machine tool, or other goods, they will “see” the true impact of that product reflected in the price. Just like the Ford website listing grams of CO2/km traveled of a vehicle.

Furthermore, on that basis, they can shop around. Today, the cost of recycling or disposal, often covered by local governments or whoever is paid to pick up the trash at the curb every week, is not reflected in the cost of the product. Tomorrow it most likely will be. We better anticipate all of these costs – just as Ohno did when thinking about lean manufacturing.

Those that incorporate “environmental economics” in the design and production of their products are likely to be ahead of their competition.Get evolve fish car emblem and start promoting green.

Production Systems and Modeling Cognition

Thu, 01 Oct 2009 08:30:53 +0000

A “production system” is, at a rough level, a set of simple rules, such as “if X happens, then doY“. The concept of production systems occurs frequently in computer science, and describes a type of rule-based control system for computation. Some of the background for production systems comes from work by Emil Post (1943) on “canonical systems” describing one type of production system.

There has been interest in modeling cognition with production systems. One example is the highly developed ACT-R framework (Anderson, 1983; Anderson & Lebiere, 1998) which is based on production systems, and another is the Soar framework created by Allen Newell (Newell, 1994). Lovett and Anderson (2005) give a good overview of modeling cognition with production systems, and Gray (2005) has examples of many productions system applications. Anderson and Lebiere (1998) even went so far as to call productions one of “the atomic components of thought”.

On one hand, I greatly admire the production system models of cognition. As Anderson and Lebiere (1998) mention, two of the main goals for a theory of cognition are precision andcomplexity. Implementing a model physically (on a computer) requires you to be precise about exactly what you mean in your theories – you can’t implement something which is vaguely defined. And, as the brain is extremely complex, accurate models of cognition will probably have to be complex to accurately describe the workings of the brain, and prodution systems models are typically more complex than most psychological theories (by specifying all of the details). Additionally, many production system models are integrated in the sense that they combine perception, cognition, and action in a single model, which I think is important for understanding the brain at a large scale.

However, lately I’ve been questioning some aspects of production system models, especially in light of other, non-production system models based on more detailed knowledge of the brain (such as Hamker 2005, Itti & Koch, 2001, and Lanyon & Denham, 2009). Such models incorporate specific knowledge of the brain, such as neural connectivity between different brain areas, and firing rate patterns obtained from single-cell neural recordings. Some of these models have been very successful in explaining certain phenomena, and seem to be much more reflective of the underlying neural structure than the relatively abstract production system models.

While traditionally production systems have not incorporated many neural details in their models, more recently Anderson (2007) has strongly recommended using brain imaging to guide model development. One question I have is then – how “generic” is cognition? In other words, are a few basic mechanisms responsible for cognition (as production systems theories might suggest), or does the neural structure of the brain result in a larger variety of mechanisms?

Production systems are also extremely powerful in the sense that they can be made to do almost anything. Post’s (1943) canonical systems are computationally equivalent to Turing machines, and are thus capable of computing effectively anything that can be computed. The production systems used in modeling cognition are typically more limited than Post’s (such as in ACT-R), but are still quite powerful. So another question is then – are production systems too powerful for modeling the brain? Since we could build production system models which do almost any task in almost any way, do production system frameworks themselves really tell us anything about how cognition works?

My current thought is that we’re ultimately going to have to incorporate more details of neural structure into our models of cognition than are found in many existing production system models. I also think that our models will have to be more constrained than most production system models if they are to be informative. But taking a step back, I also think the higher-level aim of many production systems models is excellent – to build relatively complete, well-specified computational models of cognition.