Challenges to Innovation in 

Advanced Manufacturing: 

Industry Drivers and R&D Needs

November 3rd and 4th

White Papers Breakouts and Plenaries

SUSTAINABILITY AND ENERGY

PLENARY: Distributed Power-aware Machinery as a Foundation for Next Generation 

Sustainable Manufacturing (Rockwell Automation 1

Innovations in Energy Measurement and Control for Manufacturing Systems (GM) 3

Manufacturing and the Smart Grid (AMT) 5

MODELING AND SIMULATION

PLENARY: Dual Manufacturing: Manufacturing Both Real and Virtual 

Products (NASA MSFC/University of Iowa) 7

Manufacturing Simulation: The Need for Standard Methodologies, Models, and 

Data Interfaces (NIST) 9

NEW MANUFACTURING PROCESSES

PLENARY: Challenges in Net-Shape Manufacturing of Metallic Parts (GE Global Research Center) 11

The Future of Advanced Alloy Manufacturing: Material Modeling (Third Wave Systems) 13

21st Century Methods for Composite Processing (Advanced Processing Technology) 15

MANUFACTURING IT AND INTEROPERABILITY

PLENARY: Product Tolerance Representation: Critical Requirements for Product/Process 17 

Interoperability (NNSA and MetroSage)

Information Models for Machining Interoperability, Optimization, and Simulation (RPI/STEP Tools, Inc.) 19

Personalized Production Paradigm (University of Michigan) 21
ROBOTICS AND AUTOMATION

PLENARY: Ushering in the Next Generation of Factory Robotics and Automation 

(GM/Georgia Institute of Technology) 23

Key Barriers to Rampant Random Bin Picking Retrofit Deployment 

(Braintech Government & Defense, Inc.) 25

REPORT: NIST SUSTAINABILITY WORKSHOP

NIST Sustainability Workshop (NIST) 27
Challenges and Opportunities to 

Realize Sustainable Manufacturing 

Page 1 of 2 

F.M. Discenzo 

R. Pai 

D. Carnahan 

Distributed Power-aware Machinery as a 

Foundation for Next Generation Sustainable Manufacturing 

Dr. Fred M. Discenzo, Dr. Ram Pai, Dan Carnahan, P.E. 

Rockwell Automation 

I. Introduction 

The U.S. manufacturing landscape is changing dramatically. Future markets will favor 

manufacturers that demonstrate responsible behavior with regard to energy usage, waste disposal 

and recycling. To compete, future manufacturers must maximize economic value-add from 

intellectual and physical capital investments, operate as part of a larger ecosystem of linked 

environmentally responsible global customers, suppliers, and partners. True leaders of tomorrow 

will play a global leadership in innovation of novel products and solutions. The transition to 

sustainable manufacturing must be done in the context of increasingly complex manufacturing 

processes and connected processes and enterprises. Organizations that are cognizant of these 

trends and accordingly shape their strategies and execute their tactics will define the winners in 

the next decade. 

II. Critical Drivers 

The events surrounding 9/11 coupled with recent worldwide financial instability, aging 

workforce, volatility and insecurity of world energy supplies, and the need for environmental 

stewardship foreshadow an onslaught of a dramatic shift in values and priorities that is beginning 

to transform how consumers behave and manufacturers operate. Changes in technology, public 

policy, world security, and the financial and energy markets changes are among the factors 

accelerating the change in manufacturing. We see five major drivers that are transforming 

virtually every manufacturing sector in the US. These drivers are: 

No Drivers Expectations 

1 Energy & Waste Effective utilization of resources to reduce waste and energy consumption, 

while optimizing production. 

2 Safety & Security Inherent Security and Safety of human, physical and intellectual capital 

across the connected supply chain. 

3 Social 

Responsibility

Assessment and availability of information on Carbon and GHG emissions 

across the Product Life Cycle. 

4 Harmonized 

Standards

Supply chain integration with availability and automated interpretation of 

digitized global standards across interoperable systems. 

5 Globally Linked 

Enterprise

Global communication supporting a fabric of enterprises capable of 

exchanging and making decisions on information in real-time across the 

globe. 

II. R&D Needs 

Specific developments are needed to efficiently promote the transition to a new manufacturing 

paradigm. There are seven areas of R&D need that will provide the foundation for manufacturing 

success in 2025. These areas are: 

1
Challenges and Opportunities to 

Realize Sustainable Manufacturing 

Page 2 of 2 

F.M. Discenzo 

R. Pai 

D. Carnahan 

No R&D Needs Scope of Development Required 

1 Sensing and measurement Cost effective distributed sensing for energy, waste, process 

fluids, and airborne chemicals. Sensor fusion & wireless-selfpowered sensors coupled with smart sensor networks. 

2 Modeling & Simulation Design and operational (i.e. control) models for sustainability 

3 Dynamic link to plant 

manufacturing equipmdent 

and energy sources 

Standards to support dynamic grid interface and linkages to 

plant MES and level 0/1 plant control to drive sustainable 

manufacturing and optimal economic performance. 

4 Knowledge Standardized approach needed for encoding process and 

product information –critical gap now beginning to occur. 

5 Distributed energy & energy 

storage 

Reference implementations based on Smart Grid standards to 

accelerate the adoption of energy aware eq. & processes. 

6 Manufacturing Technology New Pinch and other manufacturing with less energy, smart 

energy-aware machines and controllers, more efficient OEM 

equipment. 

7 Methodologies for agile 

integrated manufacturing 

Vertical and horizontal integration capabilities to support 

demanding requirements for capturing core capabilities and 

integration of those capabilities up and across the supply chain 

Mechatronics standardization and integration. 

The topic area noted as “Methodologies for agile integrated manufacturing” is considered 

foundational and will form the cornerstone for future sustainable manufacturing. It is essential to 

provide a standard framework for distributed plant machinery such as ovens, fryers, boilers, fans, 

pumps, and other process equipment to exchange information on energy and process information 

in real-time and to respond to dynamic information provided by the grid in a timely and 

coordinated manner. This permits unprecedented capabilities for dynamically altering plant 

operations in an effective way to protect productions processes and safeguard machinery and 

personnel while achieving targeted energy usage and manufacturing sustainability objectives. A 

representative framework for smart distributed energy-aware machines is provided by distributed 

agents. This framework, based on a biological analogy, has a rigorous underpinning and has 

shown to provide superior performance in a variety of complex and critical manufacturing 

processes1

. There is a need to explicitly embed standard energy, risk, and economic protocols to 

permit this open, integrated system to dynamically link process equipment with plant scheduling 

and machinery control. As shown in the plant 

diagram multiple distributed processes must be 

coordinated and scheduled in real time to achieve 

new performance levels in energy utilization, 

waste reduction, and sustainable production. The 

scope must include plant facility services, supply 

chain partners, energy providers and customers. 

V. Summary 

Recent events have triggered an irreversible change in manufacturing and necessitated the rapid 

transition to environmentally sustainable and socially responsible manufacturing. The integrated 

enterprise that effectively achieves process and personnel safety, environmental protection, and 

superior energy efficiency will realize faster time to market, lower total cost of ownership, 

excellent asset optimization, effective risk management, and economic excellence. These factors 

will determine the winners in U.S. manufacturing in the next decade.

 

1

 “Intelligent Systems: Architecture, Design, and Control”, A.M.Meystel, J.S.Albus, John Wiley & Sons, Inc., New York, 2002 

2
Innovations in Energy Measurement and Control for Manufacturing Systems

Jorge Arinez, Stephan Biller

Manufacturing Systems Research Lab

General Motors Research & Development Center

Warren, MI U.S.A. 48090

jorge.arinez@gm.com, stephan.biller@gm.com

Abstract

This white paper discusses the need for innovative technologies based on measurement and

control to make manufacturing systems and equipment more energy efficient. Two objectives

related to technology development are presented. The first is to obtain finer granularity in energy performance information from systems to improve current operations. Secondly, use such

information along with lifecycle analysis to improve the energy efficiency of future designs for

both systems and equipment.

Numerous studies have reported that industrial energy usage represents approximately 30% of the

total U.S. annual energy consumption. Reducing the level of energy consumption serves many interests related to national security, the environment, and the economy. Energy saving technologies

have already been developed which according to some accounts offer U.S. industry the ability to

save 10% in present operations. However, to meet ambitious national energy reduction goals for

industry, further technological innovations are needed.

To identify and develop the requirements for such technologies, we need to first understand the

detailed nature of energy consumption in manufacturing systems. Clearly, the measurement of

electricity and of other utilities exists and is well understood. The utility metering of large areas

of industrial operations provides an overall indication of gross energy usage, however, details of

energy utilization of individual pieces of equipment is not easily obtained and in many cases does

not even exist. As costs and regulatory pressures mount to achieve ever increasing levels of efficiency, energy consumption information at a finer granularity will need to be probed to identify

demand patterns. Given such knowledge, appropriate strategies and technologies may then be more

effectively deployed to address energy reduction opportunities. Also, as the capability to measure

detailed energy utilization grows, insight into sub-system interactions will lead to further efficiency

improvements.

Broadly speaking, there are three basic ways to reduce energy consumption. The first and most

immediate deals with real-time control. In simple cases, control consists of simply turning a device

or process on or off. In more complex processes, advanced multivariable control algorithms may

be employed. The second approach is to modify or change some fundamental parameter or constraint of a manufacturing process so that a greater efficiency is achieved beyond real-time control.

This approach requires more time as it may involve detailed engineering analysis, optimization,

and validation before a redesigned process is commissioned. The third way to reduce energy has

the longest time horizon since it involves the design of new energy efficient equipment. This last

approach offers the greatest opportunity for achieving large energy savings because all currently

available advances in technology may be integrated into the design of new equipment. Depending

on the manufacturing industry, this opportunity may only occur infrequently so when the occasion

arises, effort must be made to incorporate all existing knowledge into the design of higher efficiency

systems and equipment.

NIST National Workshop on Challenges to Innovation in Advanced Manufacturing: Industry

Drivers and R&D Needs, Gaithersburg, MD, November 3-4, 2009.

3
For all of these three general approaches, information obtained about the system’s energy performance behavior through detailed measurement of sub-systems and individual equipment can yield

improvements in energy efficiency. For each of these paths to energy reduction, there are corresponding technical challenges and barriers which need to be overcome. It is here where advances

in measurement methods and standards are particularly critical as a basic data and information

infrastructure is needed to enable the desired improvements in energy efficiency.

For example, given that there is already throughput, cost, and quality data being collected, how

should real-time energy data be acquired and integrated to provide a meaningful metric of system performance for effective energy-related decisions to be made? This question spans multiple

domains resulting in the need to make correct tradeoffs to achieve energy savings and meet production objectives. Furthermore, since there will be a greater amount of data required to execute

real-time monitoring and control of energy, naturally there will be additional costs to be borne by

manufacturers. In some scenarios, it is quite plausible that deploying new measurement devices

may be cost prohibitive and therefore implicit methods to determine energy consumption will need

to be devised. The specification and subsequent development of low-cost, energy-aware sensors

and actuators will need to occur simultaneously to allow for such pervasive monitoring and energy

control. Also, standards for the design and deployment of optimal sensor networks for such “smart”

energy devices will need to be in place for integration with higher level energy management systems.

Another challenge is the lack of integrated data between energy management systems and lower

level processes. As an example, with high-level energy reporting, detection in the degradation of

energy performance of individual processes is obscured by the large amount of aggregation in energy data which occurs. Therefore, meaningful hierarchical organization and aggregation of energy

data is necessary to identify and isolate faults, leaks, or other process parameter fluctuations which

result in poor energy efficiency. Measurement and diagnostics of the “health” of equipment and

processes is a vital aspect of an efficiently performing system. Processes can only perform efficiently

if they are maintained in a state of continuous calibration where drifts in set-points are prevented.

In addition to improving current operations, consideration must also be given to the performance

of future systems and equipment. For this, lifecycle analysis which uses historical data having the

fine resolution described above must be communicated to system and equipment designers alike.

This data will permit designers to develop systems which can be more easily adjusted to reduce

peak energy requirements and provide overall gains in average consumption. Furthermore, designers will not only have a better knowledge of expected energy efficiency, but will be able to better

model and design the system to achieve even greater savings. Hybrid simulations which not only

model discrete quantities such as throughput, cost, and quality but also incorporate continuous

energy consumption profiles will undoubtedly improve the design and validation of energy-efficient

manufacturing lines.

In conclusion, to obtain transformative changes in the energy consumption of manufacturing systems will require advances in all of the three approaches described. The foundation of detailed

energy performance information which is both reliable and accurate rests on a core infrastructure

of standards and measurement methods.

2

4
Manufacturing and the Smart Grid 

Today there is a growing emphasis on the environment and particularly on energy utilization. 

The main point being addressed here is the future of electrical power demand and the realization 

of a ‘smart power grid.’ As programs are being developed to address the makeup of a smart 

power grid, attention also needs to be placed on tools to assist in coping with changes in power 

consumption requirements when a smart grid poses a demand to change (lower) power usage. 

That is, the requirements to reduce ones draw on the grid to permit power to be allocated to a 

higher demand need. 

One area that will potentially have to react to power demand changes is small- and mid-size 

manufacturing enterprises. Today, tools do not exit to aid a manufacturer in determining how to 

react to a power demand change. There are no smart tools to interface with the smart grid at the 

manufacturing shop level. 

Some large companies are beginning to look at power consumption and are addressing it by 

monitoring the use of power at the equipment asset level. With this, it can be determined what 

assets consume what levels of power as they operate. In turn, it can then be determined which 

may need to be turned off to meet various demand needs. This method, however, is not 

necessarily the most efficient way to run an operation having to maintain a high level of asset 

utilization to maintain a profitable business. While it does provide a relative level of decision 

making capability, it does not carry the level of intelligence required to determine how to 

maximize asset utilization. 

A better concept is to understand how various processes consume power during each segment of 

performing a task (e.g. a machining operation) to permit a change in the process to an alternate 

process plan. This approach will aid manufacturing engineers to develop process alternatives to 

produce product while maintaining a relative high utilization of plant resources. This 

methodology permits a company to optimize production to match power constraints. 

This advanced type of decision capability does not exist today to permit “dynamic” production 

and process planning based upon power demands. To provide this capability, developments are 

required from various new enabling technologies. From technologies providing common data 

acquisition capabilities at the individual process level, to new applications and computing 

capabilities. The task being, the ability to match actual process steps to power usage and provide 

alternate process steps during low demand timeframes. If this is achieved, then various process 

recipes can be formed to meet varying power demands while maintaining sustainable production 

needs. 

In the past this was not possible since data could not effectively be extracted from manufacturing 

equipment to make the necessary correlations to determine what steps consume what amount of 

power. Alternatively, to plan for executing certain manufacturing steps during low electrical 

demand intervals. 

Recently a new standard has been developed, and is being further enhanced, to provide a common 

protocol and communication structure to acquire the necessary data to permit the linking of 

process steps to power usage. This standard is MTConnectSM. A royalty free open standard 

based upon Internet Protocol and XML language (refer to MTConnect.org web site for more 

information). 

10/27/2009 

5
10/27/2009 

With the use of this standard, data can be collected or acquired by applications from discrete 

equipment, using standard networking technologies, to provide the necessary information to 

structure the above goal. This is the enabler to permit innovative technology developments that 

can be utilized in a myriad of ways to structure solutions to meet the future demands that will be 

placed on small- and mid-sized manufacturers by a ‘smart power grid.’ 

Proposal 1: 

Develop software tools and applications that can assist small- and mid-sized manufacturers in 

addressing power requirements requested by a smart grid. 

Program Components: 

1) Develop products and components that permit the adjustment of process 

requirements based upon energy demand loads. 

2) Provide resources to permit enhancements to the MTConnect open standard and tools 

to address new data requirements. 

3) Investigate new computing technologies and concepts that may be utilized for 

implementation. 

4) Additional software development incorporating “cloud computing” through internet 

connections and MTConnect data capture that also includes a customer’s power 

usage, rates, high/low demand time intervals and potential variability of dynamic 

electrical usage during manufacturing processes. 

Proposal 2: 

Develop and promulgate Energy Star criteria for “Industrial Machines” (a new category) for both 

U.S. machine tool builders and their customers. All benefits of the existing Energy Star Program 

would convey that currently exist. This effort would provide a competitive edge to manufacturers 

and users of U.S. machines while in parallel providing energy savings within the manufacturing 

sector. 

Program Components: 

1) Develop products, and components, that are themselves more energy efficient 

(Energy Star), and 

2) Assist manufacturers and users in becoming more energy efficient with their own 

buildings and operations in preparation for “smart grid” connections. 

3) “Industrial Machines” would become a separate and distinct category under the 

Energy Star Program coordinated with EPA and DOE. 

4) Both the “Industrial Machine” manufacturer and the user of the “Industrial Machine” 

are tethered through a “smart grid” for measuring efficiency over an extended 

timeframe. Analysis via “cloud computing” will determine where and how 

additional efficiencies can be realized and improvements for greater energy savings. 

Contact: 

Paul Warndorf 

VP-Technology 

AMT – The Association For Manufacturing Technology 

703-827-5291 

pwarndorf@AMTonline.org 

6
Dual Manufacturing: 

 Manufacturing Both Real and Virtual Products 

Dr. Michael Grieves, NASA MSFC/University of Iowa

Introduction

 Product Lifecycle Management (PLM) is redefining the use of information throughout the product lifecycle 

and specifically, as discussed here, in the manufacturing phase of the product’s lifecycle1

. Product 

manufacturers need to consider manufacturing two products: the physical products that they have always 

produced and the virtual product that is the information about the physical product. This virtual product can 

provide manufacturers with a new source of value. 

Information Mirroring Model and Virtual Products

PLM depends on the conceptual idea of real and virtual products. Before the advent of computer systems that 

could handle the massive amounts of information about a product, the only practical way to have information 

about a product was to physically possess the product itself. 

If a quality inspector wanted to check the dimensions on a batch of components, then the components were 

physically shipped to the inspector. (In many firms, inspection of the received product at the firm’s site is still 

the primary quality control practice.) While blueprints were available on the “as-designed” component or 

product, “as-built” information on each instance of the component or product that was built from that design 

rarely, if ever, existed. 

All products start out as virtual products. That is ideas and information about what the physical product should 

be. These virtual products are then realized in physical form through the manufacturing process. The 

manufacturing of products can be divided into three phases: making the first one, ramp-up, and making the rest. 

“Making the first one” entailed getting a physical product that embodied the ideas of what the virtual product 

was required to accomplish. Ramp-up and production (“making the rest”) relied on the premise that these 

products would be close enough to the first one so as to be functionally and physically equivalent. The accuracy 

of that premise varies widely even today, which is why 

expensive quality audit inspection processes are 

required of the actual product instances themselves. 

Progressive manufacturing processes now capture data 

about the product as it is being manufactured so as to 

create not only integrated product and process 

traceability, but a virtual product model as the physical 

product is being built. As inspection processes become 

more technologically sophisticated and automated, the 

ability to create robust virtual representations of 

individual physical components and products becomes 

not only possible but also necessary. 

These virtual representations form one of the components of the PLM Information Mirroring Model (Figure 1) 

and are a main element to allow Product Specification Management (PSM) to exist and perform a critical role in 

enabling quality as part of PLM. Product Specification Management consists of three components: the physical 

inspection hardware (gauges, CMM, scanners, etc.) to collect data as product is manufactured, middleware to 

take and organize this station-based manufacturing data and build a cohesive virtual products, and an 

Manufacturing Execution System (MES) to serve as a repository for this “as-built” virtual product. 

The Value of Virtual Products

There are a myriad number of uses that can be made of the virtual product created through PSM. In the 

manufacturing or build phase, the “as-built” virtual product is immediately available and can be transmitted to 

customers and other parties in the supply chain who need the information about the product to assure 

themselves that the product is actually being created to the required specifications. 

Page 1 of 2 Dr. Michael Grieves, NASA MSFC 

© 2009 Michael W. Grieves, LLC 

7
Page 2 of 2 Dr. Michael Grieves, NASA MSFC 

© 2009 Michael W. Grieves, LLC 

Unlike the physical product itself, the virtual product can be sent over large geographic areas instantaneously 

and can be sent to multiple locations simultaneously. As described elsewhere2

, the new slogan of “transmit us 

the virtual product and we will then tell you whether or not to ship the physical product” may define a new 

paradigm in purchasing and manufacturing. 

One automotive manufacturer has created a collaborative virtual space with its suppliers where the inspection of 

component parts at the supplier and later at the OEM is correlated down to the inspection point – though each 

may use different inspection methods and devices. The introduction of this collaborative model contributed to 

an 85% reduction in build issues in the subsequent model year as reported by the OEM. 

In the create phase, the as-built virtual product can be used to validate the design of new, similar products. The 

data collected on actual results compared against specifications is invaluable in assessing manufacturing 

validity of new designs. By providing a feedback loop, the engineering / manufacturing divide can be bridged, 

reducing the slow iterative process of trial-and-error typically performed by manufacturing companies.3

For instance, while a specification and its associated tolerances may be manufacturable for the beginning of a 

production run, it may be that tool and die wear over a much larger run does not allow for those specifications 

to be met. Having the sequence of virtual products allows designers to understand either the requirement for 

different specifications or understand when new tool and/or die replacement is required. 

At another automotive manufacturer, historical process capability information contained in the as-built virtual 

product of current and previous product models is being captured in the early design of new product models. 

This is in the form of dimensional tolerances that can realistically be expected to hold using similar 

manufacturing methods. In the absence of PSM technology, defining the proper tolerances in design for 

manufacturability (DfM) is a notoriously uncertain and difficult exercise, where the risk is that improperly 

assigned tolerances will lead to costly rework in design and tooling. 

In the support phase, the issue of product liability often hinges on proving whether or not the individual product 

was manufactured to the required specifications. Without the ability to present data about the manufacture of a 

specific product, companies are at the mercy of plaintiff attorneys who raise doubt about the manufacturing 

process by asking “Isn’t it possible that the bolts holding my client’s seat were not tightened properly? Having 

the as-built virtual product, especially after the physical product may have been destroyed in an accident, gives 

the manufacturer protection against such an accusation. 

Already, the US government has legislated detailed traceability at the level of individual product instances as a 

requirement on the F-35 JSF aircraft program, necessitating the implementation of PSM technology by the 

prime defense contractor and its suppliers. NASA has a one-hour informational demand in the event of an onorbit anomaly for the Constellation project. 

Conclusion

We have only manufactured physical products in the past, because we could not manage the amount of data that 

virtual products need. The exponential advances in computer technology are making virtual products feasible. 

Virtual products, i.e. the information about the product, have a myriad of uses, not only in the manufacturing 

phase, but also throughout the product lifecycle. Product Specification Management as part of Product Lifecycle 

Management defines the components necessary to capture and organize manufacturing data into virtual 

products. Manufacturers need to consider moving from single manufacturing to dual manufacturing: 

manufacturing physical and virtual products. 

 

1

 See Grieves, Product Lifecycle Management: Driving the Next Generation of Lean Thinking (McGraw-Hill, 

2006) 

2

 See Dr. Michael Grieves, MES: Achieving Real Quality through Virtual Products, 2008 Whitepaper 3

 See Dr. Michael Grieves, Multiplying MES Value With PLM Integration, 2007 Whitepaper 

8
Manufacturing Simulation: The Need for Standard Methodologies, Models, and Data Interfaces 

Charles McLean 

Guest Researcher 

Manufacturing Simulation and Modeling Group, NIST 

Simulation technology can provide a highly effective means for evaluating the design of a new manufacturing system or 

proposed modifications to existing systems. This technology can be especially useful in supporting agility, 

sustainability, supply chain integration, as well as the development of new advanced processes. Manufacturing 

simulations are often used as measurement tools that predict the behavior and performance of systems that have not yet 

been implemented, or to determine theoretical capabilities of existing systems. Simulations are essentially experiments. 

As defined in Jerry Banks Handbook of Simulation, a simulation is: “...the imitation of the operation of a real-world 

process or system over time. Simulation involves the generation of an artificial history of the system and the observation 

of that artificial history to draw inferences concerning the operational characteristics of the real system that is 

represented. Simulation is an indispensable problem-solving methodology for the solution of many real-world 

problems. Simulation is used to describe and analyze the behavior of a system, ask what-if questions about the real 

system, and aid in the design of real systems. Both existing and conceptual systems can be modeled with simulation.” 

Although the potential benefits of manufacturing simulations are significant, many problems still exist. For 

example, the development of individual simulations within industry is still often more of an art than a science. 

Simulation methodologies have not been standardized - the skills and experience of the simulation analyst may greatly 

affect the way a simulation that is developed, the type of model that is constructed, the time it takes to build the 

simulation, as well as the utility and correctness of the results. Another major problem, is the lack of standard models – 

with each new simulation study, models are often built from scratch, resulting in redundant development efforts and the 

possibility of introducing new modeling errors. Finally, the lack of standard data interfaces makes it costly and timeconsuming to transfer data back and forth between other manufacturing information systems and simulations. 

Key Drivers for Manufacturing Simulation R&D 

Agility – Wikipedia defines agile manufacturing as a term applied to an organization that has created the processes, 

tools, and training to enable it to respond quickly to customer needs and market changes while still controlling costs and 

quality. Although historically discrete event simulations have been focused on addressing a number of issues relating to 

agility, e.g., system performance, throughput, and operating costs, simulation technology does not currently meet all 

needs in this area. Its biggest shortfall is in the time and cost associated with developing the simulations themselves. 

Simulations may take months to develop and are often not built because manufacturing managers are looking for 

immediate answers. Solutions are needed to accelerate the modeling and simulation development process, as well as to 

insure the technical correctness of the simulations themselves. 

Sustainability - Simulation technology has been a significant tool for improving manufacturing operations in the 

past; but its focus has been on lowering costs, improving productivity and quality, and reducing time to market for new 

products. Sustainable manufacturing includes the integration of processes, decision-making and the environmental 

concerns of an active industrial system to achieve economic growth, without destroying precious resources or the 

environment. Sustainability applies to the entire life cycle of a product. It involves selection of materials, extraction of 

those materials, manufacture of component parts, assembly methods, retailing, product use, recycling, recovery, and 

disposal. Changes will need to occur if simulation is to be applied successfully to sustainability. Manufacturers will 

need to focus on issues that they have not been concerned with before. Since there has not been a demand for 

simulation technology with sustainability features, simulation software vendors and analysts have not typically 

addressed these issues in the past. 

Supply Chain Integration – To achieve supply chain integration, multiple enterprises often need to work 

cooperatively to deliver end products. Some examples of the functional elements of a supply chain may include 

component part and raw material suppliers, transportation networks, distributors, warehouses, final assembly plants, and 

retailers. Typically, some elements of a supply chain will cross enterprise boundaries. Simulation analysts building 

supply chain models may need to interact with peer analysts in other enterprises that use different simulators for their 

enterprises. Complete internal information on each supply chain element may not be available to the analyst due to 

proprietary issues. Major research issues that need to be addressed include the development of distributed supply chain 

simulations using different simulators as well as the exchange of information between these simulations, e.g., standard 

message formats and access to shared databases. Data specifications are needed to identify the types of information that 

will need to be exchanged between different suppliers models, manufacturing applications, and databases. Examples of 

data that needs to be shared includes orders; schedules; tooling, raw material, work-in-process (WIP), finished part 

inventory and tracking data; production capabilities and capacities; resource status and usage; reject and rework data. 

9
Other research areas include the development of simulation integration infrastructures using Web services technology 

that will allow supply chain partners to connect simulations of their facilities over the Internet. To address production 

requirements, simulations will need to include technical solutions for modeling manufacturing supply chains at multiple 

levels. Web-based solutions could enable the integration of multiple simulations at the supply-chain, enterprise, plant, 

and shop-floor levels. Off-the-shelf solutions do not exist today. 

Advanced Manufacturing Processes – Some of the issues associated with the development and implementation of 

new, advanced manufacturing processes includes process validation, process capability analysis, tolerance analysis, 

ergonomic analysis, and tool design. Simulations can support these activities through: the modeling of systems, the 

execution of manufacturing plans, programs; the use of statistical process control techniques to determine whether 

processes can be kept in control range; modeling the effects of tolerance stack up on overall tolerance budget for a 

product or machine setup configuration to determine the probability that an instance of the product will meet 

specifications; evaluation of ergonomic aspects of worker tasks for efficiency of operation, theoretical production rate, 

risk of injury, rest requirements; and the development of tool management plans, definition of standard tool sets, 

prediction of tool wear, etc. Although special purpose simulation tools have been commercially developed to support 

each of these areas, standard data interfaces that would enable the exchange of data between these tools is very limited. 

Need for the Development of New Simulation Standards 

Need for Standard Methodologies - Simulation case studies are conducted to analyze and improve the efficiency 

and effectiveness of manufacturing organizations, systems, and processes. A study essentially represents a methodology 

for solving specific problems and getting answers to specific questions. Studies often model some aspect of current 

operations and validate the effect of some hypothetical change(s) to those operations. The performance of current and 

proposed systems are evaluated according to some set of metrics. Simulation textbooks typically recommend that a ten 

to twelve step process be followed in a simulation study. The recommended approach usually involves the following 

steps: (1) problem formulation, (2) setting of objectives and overall project plan, (3) model conceptualization, (4) data 

collection, (5) model translation into computerized format, (6) code verification, (7) model validation, (8) design of 

experiments to be run, (9) production runs and analysis, (10) documentation and reporting, and (11) implementation. 

Unfortunately, this approach often leaves considerable work and possibly too much creative responsibility to the 

simulation analyst. 

Each new simulation case study performed today probably repeats at least some work previously done by others. 

Case studies typically contain proprietary information that private companies do not want to share. For this reason, it is 

unlikely that most case studies will ever be seen outside of the company that commissioned them. How can the 

duplication of work be minimized? The development of standard templates for different types of case studies would be 

a step in the right direction. More work could be done to create case study templates that are generic but more problemdomain specific, e.g., scheduling, layout, and material handling. 

Individual case studies should be able to be used as modular building blocks and templates to solve more complex 

manufacturing problems. Ideally, case study templates should be “atomic,” i.e., unique, indivisible, and nonoverlapping. A rigorous analysis should be used to ensure that each case study forms a clean, basic building block. The 

analysis should aim to assign any specific objective or question type to only one type of case study. A major reason for 

this rule is to avoid the infinite proliferation of custom-defined case studies. Repositories would need to be established 

for the case study templates so that they could be readily accessed by simulation analysts and software developers. 

Resources in the academic, research, and standards communities could be applied to this problem, thus avoiding the 

proprietary information content issues. 

Need for Standard Models - Neutral model formats would help enlarge the market for simulation models and make 

their development a more viable business enterprise. Model libraries could be marketed as stand-alone products or 

distributed as shareware. Standard formats for models would make it possible for simulation developers to sell model 

libraries much the same way clip art libraries are sold for graphics software packages today. Simulation model libraries 

could be expected to increase the value of manufacturing simulators for industrial users much the same way graphics 

libraries increase the value of photo processing, paint, and graphics illustration software packages to their users. 

Need for Standard Data Interfaces - The development of neutral, vendor-independent data formats for storing 

simulation data could greatly improve the accessibility of simulation technology to industry by enabling the 

development of reusable models. Such neutral, simulation-model formats would enable the development of reusable 

models and reference data by individual companies, simulation vendors, equipment and resource manufacturers, 

consultants, and service providers. Reference data sets to support sustainability could also be developed to provide 

information on energy consumption, alternative processes and materials, pollution data, improved equipment 

capabilities, worker task analysis, job satisfaction evaluation criteria, material recycling and recovery opportunities, 

community impact, mitigation strategies, etc. Standard message formats are needed to facilitate the exchange of 

information between simulations built by different organizations within supply chains. 

10
Challenges in Net-Shape Manufacturing of Metallic Parts 

WT Carter, JS Marte, SR Hayashi, SV Thamboo 

 GE Global Research Center, Niskayuna NY 12309 

carter@ge.com 

Manufacturing of metallic parts can be accomplished by a large variety of processes including casting, 

forging, machining, powder processing, welding and countless others. Intrinsic to all of these processes is 

the desire to achieve a final in-service geometry – with requisite material properties – at the lowest possible 

cost. A typical cost flow analysis shows that cost quickly compounds late in the processing sequence. For 

example, in the processing and 

machining of a forged part shown in 

the figure to the right, the machining 

step (often viewed as an inexpensive 

process) actually adds significant cost 

because of the value of the metal 

removed and scrapped. If upstream 

processes to achieve net- or near-net 

shapes were fully developed, 

machining losses would be reduced to 

an insignificant level and the overall 

part cost would be far lower. 

However, achieving net shape early 

in processing is an elusive goal. 

Cost ($/lb)

The benefits of net shape processing to the US manufacturing infrastructure is clear in the reduction of 

wasted material and machining costs. Additional benefits include reductions in the energy and greenhouse 

gas emissions associated with production, transportation, and recycling of wasted metal. Such reductions 

would impact material sustainability and availability for high-tech manufacturing, and provide a 

competitive advantage for US manufacturers. Modern net-shape processes include the traditional (e.g., 

investment casting) as well as emerging (e.g., laser additive methods, isothermal forging, powder 

metallurgy) technologies. In addition to improved material utilization, many of these processes provide an 

opportunity to introduce technological and practical advances, such as location-specific properties, lean 

manufacturing cycles, and inventory reduction. However, the full benefits of the processes have not been 

exploited because of economical and technical challenges. Cost is a key driver, and cost is driven by 

process rate, yield, raw material cost, capital cost, repeatability and flexibility. Many of the technical 

challenges are similar to those faced by the established processes: microstructural defects, shape retention, 

equipment capabilities, etc. 

Net-Shape Deformation Processes 

It may seem obvious from the plot above that achieving near-net shape from a forging can reduce the 

machining cost. This is true, but forging press capacity, die material strength, and workpiece plasticity 

impose practical limitations that have not been overcome. Improvements in ingot and billet material that 

enable net shape forging can have a large impact. Superplastic forming, for example, has been 

commercialized for a few sheet metal applications, but shows promise for bulk deformation as well. 

Required developments include thermomechanical processing for producing superplastic billets, alloy 

design methodologies for meeting property requirements for both service and processing, (alloy 

developments to date have concentrated on in-service material property requirements, ignoring the 

processing limitations), and the development of advanced presses equipped with controls to forge to netshape. 

Material Additive Processes 

Material additive processes include laser-net-shape manufacturing, direct metal laser sintering, plasma 

transferred arc and electron-beam free form fabrication. They typically require expensive metal powder or 

11
wire as a raw material. Despite the high cost of raw material, these processes find niches in manufacturing 

where local additions of expensive metal is more economical than removing a large amount of less 

expensive metal from an over-sized workpiece. Where small numbers of parts are required, the additive 

processes obviate the need for expensive tooling, thus becoming economically favorable. Tailoring 

properties by tailoring chemical composition to the local requirements such as corrosion resistance, wear 

resistance, chemical resistance and hydrophobicity seems to be an obvious benefit of these processes, but 

this advantage has not been largely exploited. The largest obstacle these processes face is the presence of 

microstructural defects (e.g., voids, impurities, or inclusions) in the final product; such defects can lead to 

catastrophic failure. Developments in process monitoring and control with in situ defect detection and 

remediation could reduce or eliminate the cracks, inclusions, and pores between deposit layers. 

Joining

The advent of high brightness lasers makes it possible to weld thick gauge materials commonly seen in 

numerous industries including windmill towers, locomotives, and pipe. These high brightness lasers enable 

the welding of materials that are up to 1 inch thick in a single pass. This is significant. Utilizing today's 

technology (e.g., metal inert gas or submerged arc welding) requires a “weld prep” where metal is removed 

and scrapped, then replaced with weld metal filler wire. The ability to weld 1-inch plates in a single pass 

can lead to a 90% reduction in both energy consumed and CO2 emissions during the manufacturing 

process. For the heavy industrial manufacturing sector in the United States, this amounts to a reduction of 

2.98x109

 kWh/yr. Combined with technologies that reduce the forging envelope, research in advanced 

joining will provide additional opportunity to introduce net shape manufacturing into the supply chain. 

Advanced Machining 

Several new machining techniques that combine electrical, chemical and mechanical removal of material 

are emerging with the goal of increased throughput. These techniques apply lower mechanical loads, 

leading to lower capital equipment costs due to reduced machine stiffness requirements. They enable costeffective machining of high-performance materials that prove difficult or impossible to machine 

conventionally. Environmental stewardship adds a burden to these new technologies, requiring process 

developments. 

In line monitoring of the output of high-throughput machining centers is required to ensure that the product 

consistently meets geometric tolerances. However, conventional gauging and tooling is expensive and 

inflexible. High-speed, general-purpose, non-contact measuring systems could detect tool wear or 

alignment issues, allowing corrective actions to center products within customer tolerances. 

A review of the balance sheet of a typical machining center indicates that approximately 10% of income 

results from the sale of machining chips and scrap metal. As this amount represents the typical net income 

of such a center, the chips and scrap must be viewed as a product rather than waste. High speed detection 

and sorting of chips by alloy composition can add significant value. Reclamation of machining waste in 

electrochemical machining processes should be addressed. 

Recommendations 

While there has been impressive fundamental work in some of the above areas to develop new 

technologies, they have not been widely implemented. In some cases this is because of high initial 

investment. In other cases, new design practices to take advantage of new materials have not been 

established. The key areas in R&D needed to overcome the challenges of net shape manufacturing should 

include: 

• Development of new manufacturing technologies for net shape manufacturing 

• Enhancing current net shape manufacturing technologies 

• Modeling & simulation of net shape manufacturing processes 

• Developing design practices capable of taking advantage of the new technologies 

• Devising approaches for process control that incorporate in-line monitoring and adaptive control 

12
Third Wave Systems, Inc. 

The Future of Advanced Alloy Manufacturing: Material Modeling 

Advanced alloy development is an active area of research with pervasive impact on the United 

States’ manufacturing industry; indeed airframe, jet engine, power generation, medical device, 

defense, and automotive companies all stand to benefit from such research. We need to avoid 

time-consuming traditional methods of development and access state-of-the-art micromechanical 

modeling techniques that accelerate the development of these alloys and sustain our country’s 

global competitiveness. Unfortunately, a disconnect currently exists between alloy developers 

and the manufacturing base of industries that want to machine components utilizing new alloys. 

Time-to-market advantages are being lost while our manufacturing base struggles with 

machinability issues that accompany new, unfamiliar alloys. Additionally, new alloys are 

inhibited from broad-based dissemination due to prohibitive manufacturing costs. 

Computational alloy design is an emerging approach to new alloy development that relies on 

mechanistic and predictive material models. By working with the end-user of an alloy, the final 

microstructure is optimized for the best combination of relevant properties. During the 

computational alloy design process, structure-property models dictate optimal microstructure to 

achieve the desired properties; in turn, process-structure models dictate optimal processing to 

achieve the targeted microstructure. In the last decade, such physics-based material modeling has 

proven to be an effective method for reducing new process costs and accelerating process 

implementation. 

We now need to fill the void of structure-property models relevant to machinability using a 

combination of computational alloy design expertise and machining simulation leadership. 

Current computational performance levels often impede rapid tooling and process development, 

but these tools can be expanded and leveraged using advanced machining simulations to 

incorporate both alloy performance and manufacturability into a concurrent engineering 

framework for high performance alloys. By focusing on relevant microstructural features and 

their impact on properties that drive machinability, the United States can leverage the same 

process-structure models utilized in alloy design to develop an annealing cycle that achieves 

targeted microstructures. For example, it may be possible to design a titanium alloy annealing 

cycle that accesses a morphology of coarse alpha particles otherwise undesirable for material 

toughness, while being compatible with a subsequent final heat treatment to restore the 

properties of the final product. 

It is time for the manufacturing community to adopt integrated multiscale physics-based 

predictive modeling for the development of machinable advanced alloys and corresponding 

component machining processes. By incorporating micromechanical constitutive models from 

alloy development models into physics-based machining models, manufacturers will gain 

detailed microstructural information about new machined components. In addition, outputs from 

physics-based machining models will also serve as a machinability feedback loop during alloy 

development, enabling developers to improve alloy machinability in the development stage while 

maintaining high performance design properties. 

13
Third Wave Systems, Inc. 

Technology is the wave of the future, and an industry driver for the United States’ emergence as 

the leader in developing advanced alloys both affordably and time-efficiently. The task is 

complex – finite element modeling must account for geometric, tooling, speed, feed, and other 

extrinsic machinability factors using validated experimental techniques – but not unfeasible. The 

reward will be simulation accuracy that provides insights to intrinsic material properties that 

influence machinability. The end result is a substantially more productive, more competitive U.S. 

machining sector, generating high profits and providing products to market much faster – 

particularly components made from advanced alloys. It’s time to start machining smarter. 

14
21st CenturyMethodsfor CompositeProcessing

Thomas Rose: Advanced Processing Technology, Norman OK, 73071, Ph405-360-4848 

Energy is a critical to the economy of the US. Composite materials address many of the energy issues both to 

produce energy in products such as windmills and to save energy in products such as car bodies and aircraft. 

There is also a need to update infrastructure to retain and regain jobs in the USA. 

By 2010, the global market for Carbon Fiber Reinforced Plastic (CFRP) composite materials is predicted to be 

worth $13.6 billion, representing a huge increase of 37% over 20061. CFRP also has a role as a replacement for 

metals in infrastructure. Corrosion of metallic structures has a significant impact on the U.S. economy. In a 

congressional study, the total economic impact of corrosion and corrosion control applications was estimated to 

be $276 billion annually, or 3.1 percent of the U.S. gross domestic product (GDP). 2 Estimates for the DoD 

alone are between 10-20 Billion. 

While the use of composites has grown, many of the manufacturing and repair processes have remained stagnant. 

There is a large and growing need to update the underlying technology to take advantage of new tools developed over 

the past forty years. Hundreds of millions of dollars are wasted each year using specifications and practices that had 

their genesis in the 1950

s and 1960s. 

The need to update these specifications and practices has a significant relevance to retaining jobs and advancing both 

defense and commercial industry within the United States. 

Background and Approach: Current specifications for composite materials were developed before it was possible to 

measure material properties during manufacture so the approach was to use the same material and process them the 

same way every time. The integrity of this practice relies on a “no change” policy. 

Stated another way, any change in the process is unacceptable because its effect on the performance of the material 

is unknown. The objective of this white paper is to increase the visibility of properties critical to performance 

during the process and thus enable far greater range of acceptability. This enables many more opportunities for cost 

reduction and performance improvement. 

Fundamental to all process improvement is the ability to link material properties to performance and then optimize 

around those properties. The improvements in computers, cure models, communication, instruments and sensors 

combine to make it possible to measure and link material properties to process actions with far greater accuracy that 

was available in the past. 

The benefits range from salvaging a bicycle part that might otherwise be scrapped, to the ability to build a 

complex bridge or sophisticated weapon that would be impossible using the legacy technology. 

Modern Laboratory 1970 2008 

Challenge: The barriers to change are high. Success requires new infrastructure. There is no requirement for 

change to infrastructure without a specified requirement. The „

catch 22is that specifications cannot change 

without data and without a change to the infrastructure one cannot gather the data. 

By leveraging the knowledge gained from past processing science programs4 and substitution methodology 

projects5 and by using new instruments, computers and data management systems, an infrastructure can now be 

developed with the final goal of new specifications for manufacturing. 

Goals: The near term goal is to adapt instruments, equipment and software to create processing alternatives. During 

this phase the goal is more efficient and accurate methods evaluate materials, address production problems and 

improve manufacturing methods within the limits of existing specifications. 

The basic components to support the MSM approach have been installed and multiple milestones have been 

achieved. 

15
21st CenturyMethodsfor CompositeProcessing

Thomas Rose: Advanced Processing Technology, Norman OK, 73071, Ph405-360-4848 

• Instruments to measure state of prepreg during cure with linkage to controls. 

• Cure models that can be validated using low cost in process methods 

• Microwire sensors to determine temperatures deep within a laminate 

• Linkage of models and microwires for cure modeling 

• Remote link of process equipment to lab instruments 

• Real time and post process determination of visco-elastic state 

These technologies are ready to be tested and evaluated in manufacturing and, if properly supported, will 

provide durable jobs based on a domestic infrastructure. Many of these improvements can be targeted to 

applications such as bridges, and buildings whose jobs cannot be relocated. 

This will require a multiyear effort within the framework of a collaborative effort with industry, academia, and 

government. This work is still developmental and is expected to include failures and successes as the balance between 

sophistication and shop friendliness evolves. In the end such an approach will inevitably lead to major cost savings 

and performance improvements. 

AvPro has worked in collaboration with large (GKN, Spirit Aerosystems, Rockwell Automation Roper), small 

(Thermal Solutions, Helicomb, First Wave) universities (Wichita State, Oklahoma University, UCLA) and others to 

demonstrate proof of concept and lay the foundation. Much work needs to be done that can only be achieved with 

additional resources and beyond the scope of AvPro and much of which must ultimately reside in the public domain 

and therefore has limited potential for attracting private capital. 

Much of AvPros work has been within the aerospace community: thus emphasis on the catch 22 regarding 

specifications. However a similar catch 22 exists in the commercial world that is less defined and therefore a greater 

challenge. If it has not been done before and does not have an immediate ROI tied to a tangible product, venture 

money is extremely difficult to obtain. Thus truly innovative ideas that derive their utility from an existing 

infrastructure will not be funded until the infrastructure is in place but the infrastructure requires products, the 

development of which venture money will not fund. 

In summary: there is a significant opportunity to lead in many areas of composite processing if the tools to support it 

are developed. Personnel directly responsible for materials and processes from both the public and private sector 

support the concept. Funding of a team with the proper vision and capability with resources to move from proof of 

concept done “below the radar to a program large enough to instantiate change has not been available. 

Many of the key drivers for this technology are the establishment of (a.) new methods based on (b.) new instruments 

that require (c.) data to determine repeatability, reproducibility of results and (d.) methods and standards to validate 

and substantiate the accuracy and precision of the results. 

16
Product Tolerance Representation:

Critical Requirements for Product/Process

Interoperability

Curtis W. Brown, Engineer Principal Mechanical, NNSA’s Kansas City Plant1 and

Daniel A. Campbell, Software Director, MetroSage, LLC

The “perfectly nominal” part is an ideal never fully achieved in manufacturing; however industry

can fabricate parts that fit and function when acceptable limits from tolerances are introduced.

Therefore a critical responsibility of a designer is to define product acceptability by augmenting

the nominal geometric shape with the appropriate set of tolerances. Within the past 60 years,

we have seen the refinement and standardization of tolerance representation which, when

implemented properly, control the location, orientation, form, and/or size of part features in a

complete and unambiguous manner.

Statement of the Problem: Current electronic product definition systems (i.e., CAD

Systems) represent completely and unambiguously only a segment of the product’s

design. Product tolerance presentations are generally of the form of mere textual

annotations, devoid of any meaningful association to the product geometry. This gravely

limits the designer’s ability to efficiently create and communicate complete and

unambiguous tolerance information, and it cripples downstream applications that

depend on such information.

What is Needed: A full semantic representation of 3-D geometric dimensioning and

tolerancing (GD&T), within or tightly coupled to the product definition system.

Meeting the stated need in an adequate manner will require software capable of:

• Augmenting a solid shape with tolerance definitions

• Implementing the notion of tolerance features (collections of one or more topological

faces)

• Representing tolerances semantically (not just as annotations)

• Dimensional / coordinate tolerances

• Geometric tolerances

• Specifications (e.g., thread specifications.)

• General property attributes (e.g., notes, markings, cosmetics)

• Designating functionally important tolerance features as functional datum features

• Building datum reference frames (DRFs) from datum features

• Associating DRFs to appropriate tolerances

• Assigning tolerances to appropriate tolerance features

• Recognizing tolerance features automatically and interactively

• Inferring correct tolerances automatically

• Per ANSI Y14.5

• Per company standards

• Checking, validating, and scoring a piece-part’s functional tolerance definition

• Are all geometric faces assigned to tolerance features?

• Are all tolerance features properly constrained for location, orientation, size and

form?

• Are there any unused DRFs?

• Publishing application programmers’ interface (API) suite

• Extending tolerance analysis

• Supporting downstream applications (e.g., inspection)

• Exchanging tolerance definition to other product definition systems

1

 The Kansas City Plant is operated and managed by Honeywell Federal Manufacturing & Technologies, LLC, for the NNSA

17
The Key Driver: Efficient and Economic Manufacturing 

Tolerancing is an important aspect of design, plus the cost of correcting design errors during the

design process is relatively low. Intelligent automated tolerancing capabilities and, in particular,

the ability to independently check that a part’s tolerance definition is correct, complete and

unambiguous, will ensure that tolerance errors are caught early in the product development

cycle.

Having validated tolerance information as an integral part of product definition means that, with

suitable interoperability, the same validated information can be used in downstream

applications, such as measurement process planning, measurement results analysis, assembly

analysis, CMM part program generation, etc.

A Case in Point: Intelligent, Automated, Economically Optimized Inspection

Process Planning

An important aspect of measurement planning is to ensure that the measurement devices and

procedures to be employed are adequate for the precision required in the ensuing

measurements. Another is to ensure that the measurements are carried out in an economically

efficient manner, making optimal use of the measurement resources available. In inspection

operations the precision of the measurements bears heavily on accept/reject decisions and can

play a critical role in the risks of Type I and Type II errors, each of which has its own attendant

economic consequences. Recent years have seen noteworthy advances in the theory of risk

and cost analysis. National and international standards have addressed these concepts as well.

Moreover, new software products now offer well validated estimates of measurement

uncertainties via science-based modeling and simulation. Thus the essential theory and many

component technologies exist for the implementation of an intelligent automated inspection

process planning system, a software tool for use by the manufacturing community to enable the

automated production of design-based measurement strategies of known reliability and high

economic efficiency. Manufacturers using such a tool would find that they (1) could

dramatically speed the production of measurement strategies for new or existing parts; (2)

would know the reliability of these strategies; and (3) would know (based on their own

assessment of cost functions for measurement, the costs of accepting a defective component or

rejecting a good one) the economic consequences of each alternative strategy. Such

capabilities offer the prospect of significant advances in profitability and product reliability.

With all that said, the problem

stated at the outset of this

document remains. Under

current conditions, the

potential user of such a

system would not have ready

access from the design

system to validated tolerance

information tightly linked to

the part geometry. This

presents an obstacle to what

could otherwise provide a

significant advance in

manufacturing. 

18
Information models for machining interoperability, 

optimization, and simulation

Martin Hardwick

Professor and Acting Head of Computer Science, RPI

President STEP Tools, Inc.

Today, Computerized Numerically Controlled (CNC) machine s are programmed using

Computer Aided Manufacturing (CAM) systems that receive their input from Computer 

Aided Design (CAD) systems. The CAD systems are used to define the nominal

geometry and required final dimensions and tolerances of a part. The CAM systems are

used to define processes that will make the part by adding material to, or more commonly 

removing material from, a workpiece. 

The input to a CAM system is a drawing or its equivalent and the output is a set of Gcodes (Gerber plotter codes) that tell a machine tool how to move its components in a 

sequence. If the machine is setup correctly then executing these codes will reveal the part. 

The antiquated G-code language is now being replaced with a modern associative 

language that makes CNC programming more visual and easier to control. It builds on 

the STEP language that is implemented by nearly every CAD system. FANUC, the 

leading vendor of CNC controls, recently demonstrated a hybrid control that machines a 

part from a STEP-NC description. The figure below shows the data that was machined.

STEP Tools and an industry team of aerospace and heavy equipment manufacturers are

testing STEP-NC and extending its capabilities to enable cooperative process planning 

and simulation by teams of suppliers. The extensions include:

• Definitions to speed up or slow down a program in response to changes in the 

production schedule. The aerospace industry has estimated that the average time 

for a machining job can be reduced by 15% or more if the process can be fine 

tuned in this way. 

• Definitions to enable networked simulation so that a contractor can ask a team of 

suppliers to plan and simulate the manufacture of a part on multiple machines, at 

multiple locations and in multiple stages. 

• Definitions to allow changes to the tooling so that an operator can make

adjustments to a program received from a supplier without having to ask a CAM 

programmer to make a complete new program from the original drawing. 

• Definitions to enable energy consumption estimates so that an enterprise can 

minimize the energy required to make a part by selecting the most appropriate 

machines and tooling.

19
• Definitions to adjust the machining programs using the results of measurements 

so that the form of a part can be adjusted to meet the current dimensions of a large 

assembly.

The mathematics required for these capabilities is mostly defined in the literature. The 

STEP-Manufacturing team is assembling an infrastructure that allows the se definitions to 

be harvested in an open, shared framework defined by standards. 

A new modeling method, called a Usage Guide, is being developed to add onto the STEP

standards for the new semantics. The first Usage Guide showed how gears can be 

represented as AP-214 data. STEP-Manufacturing is developing a Usage Guide to

describe the kinematics of machine tools in AP-214. Concepts first developed for

ontologies are being used to enable Dynamic Usage Guides that can be customized to 

the requirements of specific machines and operations. Examples include the operations 

specific to a particular CAM system, and the program cycles specific to a particular 

machine tool.

The new STEP-NC programs are a shared resource that can be stored in appropriate

media. The new programs can be edited and linked using software tools such as the 

STEP-NC Explorer illustrated below. Simulators are used to check the consistency of the 

programs. Engineers like to solve technical challenges but do not like to waste time 

because of antiquated methods such as G-codes. By making CNC programming more 

accessible, STEP-NC allows more innovative products to be developed more quickly. 

The definitions described here add new functionality to the standard so that the new 

products can be made faster and more cost effectively. 

STEP-NC Part machined by Fanuc at Boeing Renton Plant on 7.14.2009. 

20
White paper submitted to the NIST Workshop, November 3, 4, 2009 

Personalized Production Paradigm

Y. Koren and W. Wang 

The University of Michigan 

How can we sustain a strong auto industry in the US? 

How can we create new Small Business industries? 

How can we create new manufacturing jobs? 

Our proposed personalized production of automobile interiors will boost the US economy, 

and create new jobs and new industries. Instead of compromising on an interior design offered 

by the auto manufacturer, buyers will be able to design their new car interiors to meet their 

needs: Starting from an open interior space and filling it with available modules. 

Automobile interior modules may include, computer stations, storage boxes, microwaves, 

refrigerators, beds, dog baskets, folding tables, clothing racks, and portable-potties for kids, 

etc. We are proposing an open-architecture structure for all these mechanical components, 

parallel to the i-Phone and PC electro-type open architecture software. 

When this approach is adopted by the auto industry and mechanical-electrical openarchitecture standards are established, dozens of small new companies will start to produce 

special modules (such as dog baskets and storage cabinets), which will evolve to several new 

industries. In addition to trading used cars, people will trade used modules as their needs 

change and they want to update and remodel their existing cars. Because this personalized 

production business model is beneficiary to both the manufacturers (that are being paid before 

the product is built) and to the customers (who are getting exactly the product that they need), 

and because it will generate new industries that produce innovative modules, it could be a 

giant booster to the US economy. 

The main engineering research challenges are 

1. Creating a new-generation of CAD based systems by which buyers, who are not 

necessarily engineers, could easily design their car interiors; it will apply control 

feedback principles, which will aid buyers to converge to arrive at their desired 

products. 

2. Creating a new-generation of assembly systems that will be able to handle thousands 

of options, and still produce cars at mass-production cost. 

The main practical challenges are defining the regulations and standards for mechanical 

interfaces that will guarantee safety, as well as defining the standards for electrical and 

information interfaces. NIST should take a leading role and work with General Motors, Ford 

and Toyota on establishing these standards. 

21
22
Ushering in the Next Generation of Factory Robotics & Automation 

Leandro G. Barajas, Ph.D. 

Manufacturing Systems Research Laboratory, General Motors R&D Center, Warren, MI 48090 

Andrea L. Thomaz, Ph.D. 

School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30332 

Henrik I. Christensen, Ph.D. 

College of Computing, Georgia Institute of Technology, Atlanta, GA 30332 

The manufacturing capability and sustainability of the U.S. industry has been losing ground to its Asian and 

European competitors for the last few decades. For example, Japanese and German companies currently dominate 

the market of industrial Robotics and Automation (R&A) solutions with the support of low price Chinese 

manufacturers. Given the high labor cost in North American markets, the only viable option for U.S. industries to 

compete with a global market is via state-of-the-art R&A. Furthermore, most capital-intensive and wealth producing 

industries in the U.S. neither have the technical expertise nor the manufacturing capability to survive without cost 

effective R&A, which places these industries in a precarious state of vulnerability to disruptive technologies that 

may redefine the value stream map of their respective businesses. 

The unfortunate reality is that the domestic production of consumer products using conventional processes 

could soon cease to exist based on a 30-year track record of global outsourcing pressure toward regions with low 

labor and investment costs. The transformational development and establishment of next generation manufacturing 

assembly processes using the latest in dexterous and intelligent robotics and lean production technologies will 

provide the necessary competitive edge for a variety of affordable products for the future. As a result, jobs will be 

retained as some will shift from line work to technical support and operation of the robotic systems. 

The structured environment existent in current production facilities that enables robots to perform their tasks 

actually limits flexibility and drives a significant cost penalty for using robots. There has been some progress in 

enabling robots to operate in manufacturing operations with less structure, but robot capability in this area is very 

limited. This “robot capability gap” persists and limits the range of applications and business conditions under which 

robotics provide a feasible commercial alternative to other means of implementing manufacturing processes. This 

gap is especially evident in the automobile industry when examining the final assembly process. 

From an end user perspective, we believe that a new generation of assembly automation can be anticipated to 

significantly reduce the reliance on fixturing, mechanized structuring, and conventional sense-plan-act 

programming. This capability would enable assembly automation with a set of little or no more infrastructure 

requirements than a completely manual process would. These new assembly processes will exploit the existence of a 

flexible robot perception system as an integral component of a three-part strategy that includes: 1) highly flexible 

robots/end effectors, 2) flexible perception, and 3) safe integration/harmony with people, which are also performing 

tasks in the assembly process. The cognitive component of the perception system would facilitate the “assignment” 

of the automation to a set of assembly tasks and/or assistance to others performing a task not yet appropriate for 

automation. This capability will also enable the rapid “reassignment” of the automation to other tasks as required by 

production mix and business needs. Many U.S. manufacturing domains stand to benefit from the flexibility and 

productivity that this form of dynamic automation brings to the assembly process. Multi-purpose robots that can 

23
safely collaborate with human workers will elevate the capabilities of existing assembly workers in the pursuit of 

providing quality products to end-users. 

A key factor in creation and adoption of the next generation manufacturing technologies is the development of 

flexible perception and human-like control technologies. In addition, by taking a leadership role in the development 

and adoption of such emerging technologies, we could ensure that the jobs created in this new area stay in the U.S. 

These jobs can only be created and retained if a technological edge can be found that overcomes the attraction to 

low-cost labor regions. Through the pervasive use of intelligent R&A that can be as flexible and as easily trained as 

people, related industry jobs could also be moved from offshore to the U.S. as a direct result of this new technical 

capability. 

Our goal is to see revolutionary advancements in dexterous robotics leveraged in a new energy efficient 

automation environment that combines the best possible mix of human and machine capabilities. These next 

generation robots include “safe robot” technologies that allow the seamless integration of people and dexterous 

robots in one lean process. The key factor for the success of this approach is that the new systems leverage the 

infrastructure and flexible material processes that traditional manual systems use rather than expensive and 

traditionally inefficient automation methods. This substitution enables a substantial reduction in R&A support 

investment that can normally be up to 10 times the cost of the robot themselves. 

From a scientific point of view, this endeavor encompasses a wide range of disciplines. Even when current 

commoditized hardware capabilities are almost at the level required to enable us to cross the capability gap, the 

actual integrated control and communications software systems are still lagging due to the heavy burden of current 

legacy systems. The historical paradigm for controlling R&A systems relies upon the system designer being able to 

specify a priori every requirement and possible condition of the system. This approach leaves no room for changing 

conditions, adaptability, plasticity, and in general, learning. 

One of the main hindrances that is currently preventing the evolution of the next generation R&A is the lack of 

standards of performance and test methods. Every R&A manufacturer attempts to keep their customer base captive 

by having closed and mostly incompatible systems. Most of the major specifications of these systems are given in 

terms of mechanical or electrical characteristics rather than in terms of overall system performance. NIST could play 

a vital role in advent of the new wave of R&A technologies by facilitating the dialog among interested parties and 

establishing both system standards and evaluation metrics in order to be able to track the level of capability 

improvement of such systems. Such specifications should not only encompass hardware and software metrology 

targets, but also high-level system qualitative and quantitative capability measurements for standardized processes. 

In a way, this will enable an R&A revolution equivalent to the one observed on the computer industry in the mid 

1980’s. Effects of this achievement will be reflected deep into the fabric of industry and ultimately into the entire 

society; but in this case instead of putting a computer in every home or pocket, it will enable the pervasive use of 

functional R&A in all areas of our daily lives, from the factory plant floor to even your kitchen floor. 

We predicate that there is a unique opportunity to make progress in this arena by harnessing collaborations 

between industry and academia. Our existing collaboration between Georgia Tech and General Motors is one such 

good example. In our collaborative efforts to bring cutting-edge R&A technology from the labs to the factory floor, 

we are forced to reconcile some of the real issues involved with integrating flexible R&A with existing 

manufacturing processes and to focus on technologies that deliver real value added to the end customer. 

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Key Barriers to Rampant Random Bin Picking Retrofit Deployment 

Dennis Murphey dmurphey@braintech.com

Braintech Government & Defense Inc. McLean, VA 

 

 With an estimated 1,000,000 robots deployed by the end of 2010 the increasing requirement 

for Advanced Sensor Retrofits to deployed Robotic Workstations is expected to continue to increase. 

Retrofit solutions have always made good sense for complex substantial installations for discrete and 

continuous process manufacturing. The barriers however have remained the same throughout the 

years: controller interface, sensor compatibility, solution engineering and systems integration. Issues 

such as these become real barriers for complex applications such as Random Bin Picking 

 There is little doubt that Random Bin Picking (RBP) is a significant advanced manufacturing 

technology innovation. The key industry drivers for RBP include cost of manual operations, difficulty 

in material handling, and hazardous conditions. However the key drivers for Retrofitting are different; 

cycle time, error rates, down time and recovery processes. A common set of enablers to Robotic 

Retrofit experience would at the same time enhance additional innovations. We believe these enablers 

would also span multiple manufacturing sectors and would be of particular interest to the baseline 

infrastructural technology areas including measurements, performance metrics, test methods, and 

standards. 

Reasons to deploy advanced manufacturing technology innovations include: sustainability, flexibility, 

agility, reconfigurability, additive manufacturing, lifecycle information exchange and management, 

science-based modeling and simulation, intelligence and optimization of manufacturing systems, high 

throughput, high-accuracy measurement technologies, automation and robotics with increased pace of 

innovation. A good robotic retrofit candidate will naturally address many of these points. A good 

random bin picking solution will focus on solving some of the more complex issues for manufacturing 

such as: flexibility, agility, and reconfiguration. However, critical factors that are harder to achieve 

and that remain barriers to deployment are, front end engineering in order to complete deployment, 

sustainable high throughput, rapidly deployable enhancements and innovations. 

The front end application engineering includes: part programming, path planning, end effector design 

and build, sensor and controller integration and then the systems engineering to make the operation 

function as intended. We believe addressing all these front end technical barriers will dramatically 

improve the successful update of aging robotic deployments. It has been our experience just this year 

with a body assembly line in Ohio, that after the pain and agony of the “front end” the end result was 

beyond the customers expectation, in fact our retrofit of vision guidance to a 10 year old robot brought 

the solution beyond the original systems capability. However the weeks taken to get there were very 

costly. 

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 “What are key drivers for advanced manufacturing technology innovation?” 

1) We see a need for a new Vision Guidance Controller Architecture. With open standards that 

supports things like additive manufacturing, where vision guidance for example can also provide 

product quality and process validation. Higher speed device communication is critical to meeting 

throughput requirements. A more open solution would allow wider variety of sensors that could 

improve the accuracy as well. 

2) We see a need for modeling and simulation research where standards for object representation and 

solid model exchange could enhance the use of dynamic simulation. Simulation when uses effectively 

can help prevent engineering errors and solution gaps, however the pain and cost to produce effective 

simulations remain too high for routine everyday use. A simple pick and place dynamic simulation 

with complete robot model data still takes several days to complete in the most crude representation. 

 “What are the most important areas where R&D is needed (particularly in measurement and 

standards) to overcome barriers and accelerate manufacturing technology innovation?” 

1) We think an Adaptive Guidance Open Architecture standard could be a focus area that with 

Defense support and Manufacturing’s requirements could produce a serious dual use 

opportunity. Such an open standard would also allow a large body of research to produce innovation at 

a much increased pace. 

2) We also think Modeling and Simulation should be supported by a standards effort for information 

exchange as well as performance measurements. With strong simulation capable of emulating complex 

and complete intelligent automation systems designs could be validated before code is completed or 

machines are built. Performance enhancements could be identified and validated very early in the 

deployment cycle. Saving time and money for all involved. 

 Where is the next innovation? 

- Real-time instant sensor and device calibration process eliminate lengthy manual calibration 

processes. Embed calibration data such as fixed focal length or camera model specific information. 

- Real-time instant object pattern/feature learning, detection, orientation and inspection, How all this 

gets done is the challenge, once we are able to rapidly retrofit and deploy complex robotic solutions 

like random bin picking this is where we will turn our attention. 

- 3D Models of objects, workstations, devices, parts, environment and with dynamic information to 

drive simulations. We need solutions that can be engineered more accurately, and faster with 

validation of results before fully executed or deployed. 

- Bundled mechanical software solutions. In random bin picking we have found that the end effector is 

as complicated to design as the vision guidance application. Plus the need for the vision sensor to have 

clear FOV is becoming more and more an issue. We see two innovations in the horizon that can help 

rapidly deploy RBP and other advanced automation. 1) define a set or range of end effectors that are 

grouped by capability, flexibility, dexterity, power and pre-engineer them with universal wrist 

attachments base don a standard. 2) split the sensor positioning from the point of action, this means 

develop a robot arm just to position the lens, then maintain the muscle action to a separate arm that is 

able to maneuver into tight positions without a camera hanging off the wrist or having to move to an 

awkward location to get an image then relocate to pick the part. 

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Sustainable Manufacturing 

Vijay Srinivasan, NIST 

After surveying thirty large corporations, a recent article in the Harvard Business Review 

declared that “there is no alternative to sustainable development”1. A parallel, more extensive 

study by MIT found that “there is a strong consensus that sustainability is having – and will 

continue to have – a material impact on how companies think and act”2

. These dramatic 

developments owe to the fact that the manufacturing sector, represented by these companies, has 

a significant impact on the economy, society, and the environment around the world. Close to 

home, the U.S. manufacturing sector contributes 11% of the Gross Domestic Product (GDP) and 

provides 10% of the nation’s workforce with high-paying jobs. It is also the largest consumer of 

energy (45%), the second largest consumer of mined materials (21%), a major producer of solid 

waste (10 trillion kg per year), and a significant user of hazardous materials – all of which are 

implicated in a growing number of environmental problems. These facts are not lost on the U.S. 

government. The U.S. Department of Commerce (DOC) recently named sustainable 

manufacturing as one of its key performance goals and called upon NIST to provide national 

assistance to realize this goal. 

 Recognizing the environmental impact of manufacturing and the products they produce, 

many countries and regions have introduced regulations such as RoHS (Restriction of Hazardous 

Substances), REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) and 

WEEE (Waste from Electrical and Electronic Equipment) that restrict the sale of products 

containing hazardous or prohibited substances. Additionally, many companies have introduced 

consumer-oriented labeling to indicate various aspects of sustainability in their products, 

including Energy Star and labels for recycled content and recyclability of products. Some of 

these labeling are mandated by governmental regulations. Even if many of these regulations are 

local, their implications on the manufacturing sector are global – for example, the U.S. 

manufacturers are scrambling to comply with the European regulations because they do not want 

to be locked out of that lucrative market. 

 As the U.S. manufacturing sector sells globally, it also sources globally. It manages a 

global supply chain in all four major phases of a typical product’s life cycle: raw material 

selection, product realization, customer use, and material recovery. As the U.S. manufacturers 

and their global suppliers struggle with sustainability issues in the product life cycle, they are 

discovering that they need to measure, control, and manage sustainability in a complex mix of 

temporal (life cycle) and spatial (global supply chain) dimensions. Additionally, they have to 

respond to the impact of their actions on economical, social, and environmental issues in this 

complex space-time domain. Business executives often bemoan that “you are only as green as 

your supply chain”3

, and compare the global sustainability challenges of today to the ‘total 

quality management’ (TQM) challenges they faced nearly a quarter century ago4. They are also 

concerned about the dwindling supply of raw materials and resources (e.g., energy, water), and 

the sometime unfriendly sources of material supply. 

 At a recent summit organized by the DOC Sustainable Manufacturing Initiative, 

 

1

“Why sustainability is now the key driver of innovation”, Harvard Business Review, Sept. 2009, pp. 56-64. 

2

 The business of sustainability, MIT Sloan Management Review Special Report, 2009. 3

 http://www.hbrgreen.org/ 

4

 “The green conversation”, Harvard Business Review, Sept. 2008, pp. 58-62. 

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representatives of a broad spectrum of U.S. industries expressed their frustration over a vast 

number of inadequately defined measures of sustainability, the difficulties with collecting and 

exchanging sustainability information, and difficulties with working across enterprise supply 

chains to ensure meaningful improvements in sustainability and conformance to regulations. 

 These concerns were echoed with greater technical depth and clarity in a Sustainable 

Manufacturing workshop hosted by NIST soon afterwards. The NIST workshop attracted 

participants from large and small companies in the U.S. manufacturing sector (GM, Ford, GE, 

Xerox, Lockheed Martin, Rockwell Automation, P&G, Siemens, Harbec Plastics, Masco, URS), 

software vendors (Dassualt Systems, Siemens PLM, PTC), government (DOC, NIST, NASA, 

NSF), non-governmental organizations (WRI, NCMS, CAMDUS, ANSI, NACFAM, ASTM), 

and academia (Stanford, Purdue, Georgia Tech, RIT, U of Kentucky, Portland State U., Texas 

Tech). 

 Most of the industrial concerns and lessons learned were summarized in the industrial 

panel convened by the NIST Sustainable Manufacturing Workshop. Some of the messages were: 

• Sustainability should start with leaders at the top. Also, bottom-up solutions are very 

useful and powerful (because people want to be part of the solution to an important 

problem). 

• Educating suppliers on sustainability is important and is a challenge. 

• Regulations drive a lot of engineering action – often, non-compliance is the fear that 

drives these actions. 

• Branding is very important for business. Many companies are positioning themselves at 

the forefront of sustainability movement to protect and/or enhance their brands. 

• Is sustainability an opportunity or cost? There was a general agreement that there is no 

choice but to treat it as an opportunity. 

In the NIST Sustainable Manufacturing Workshop we found evidence that the more experienced 

manufacturing firms see opportunities in sustainability beyond mere compliance with regulations 

– in fact, they view this as a driver of innovation. They find that by adopting lean manufacturing 

practices they can reduce waste (a sustainability goal) while saving associated costs. They also 

see new market opportunities if they can introduce innovative materials, processes, and products 

to meet the global economic, societal, and environmental sustainability needs. 

 In the meantime, several non-governmental and standards development organizations are 

actively engaged in proposing and issuing guidelines, standards, and regulations. It was clear at 

the NIST workshop that they need some urgent coordination. Several academics have studied 

these problems and are trying to bring some order and understanding to various sustainability 

practices. It is encouraging to see that the academic community that studies these problems 

includes economists, who are proposing methods to monetize many of the sustainability metrics. 

 Based on the NIST Sustainable Manufacturing Workshop, the major challenges faced by 

the U.S. manufacturing industry in their pursuit of sustainability goals can be summarized as: 

(1) they are unable to accurately measure economic, societal, and environmental impacts and 

costs of their products during the entire life cycle and across their supply chain; (2) full life cycle 

analysis (LCA) of products requires new methods to analyze, integrate, and aggregate 

information across hierarchical levels, organizational entities, and supply chain participants; and 

(3) they lack neutral and trusted programs to demonstrate, deploy, and accredit new sustainable 

manufacturing practices, guidelines and methods.

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