Retooling Manufacturing Bridging Design, Materials, and Production (2004) / Chapter Skim
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3 Tools for Virtual Design and Manufacturing
3 Tools for Virtual Design and Manufacturing
Pages 23-69
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From page 23... ...
An AEE may include the following elements: Design tools such as computer-aided design (CAD) , computer-aided engineering (CAE)
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TABLE 3-1 Representative Tools Used in the Industry 24 System Life Cycle Engineering/Technical Cost Analysis Systems Engineering Activity Marketing Product Engineering Industrial Engineering Marketing Mission, or Product Product Engineering Manufacturing Manufacturing Field Function Customer Needs Planning Architecture Design Engineering Operations Operations Requirements Functional Analysis, Visualization, Analysis and Production Use, Support, Action Synthesis Analysis Analysis and Simulation Visualization and Assembly and Disposal Forecasting @Risk, Crystal Ball, Arena PLM, Arena PLM, Arena PLM, Arena PLM, Arena PLM, Innovation Excel, i2, Innovation Eclipse CRM, Innovation Innovation Innovation MySAP PLM, Management Management, JD Innovation Management, Management, Management, specDEV, TRIZ Edwards, Manugistics, Management, MySAP PLM, MySAP PLM, RDD- MySAP PLM, Oracle, PeopleSoft, MySAP PLM, RDD-IDTC, IDTC, specDEV, specDEV, TRIZ QFD/Capture, RDD-SD, RDD-SD, specDEV, TRIZ TRIZ SAP, Siebel specDEV, TRIZ Product Innovation Geac, I-Logix, Innovation Functional Functional Innovation Life-Cycle Management, Innovation Management, Prototyping, RDD- Prototyping Management, RDDse Planning and QFD/Capture, RDD-RM Management, RDD-SD SD SD ca sse Management Invensys, JD Edwards, Oracle, sin PeopleSoft, RDD Bu SA, SAP, Windchill Resource Project, RDD-DVF, Innovation DSM, Geac, DSM, Project, RDD- TaskFlow TaskFlow TaskFlow Planning RDD-SD, TaskFlow Management, Invensys, JD SD, TaskFlow Management, Management Management Management RDD-DVF, Edwards, Management HMS-CAPP RDD-SD Oracle, People Soft, RDD-SD, SAP, TaskFlow Management Modeling Caliber, DOORS, RDD- Caliber, ADAMS, Caliber, Abaqus, AML, Ansys, Caliber, DFMA, Caliber, DOORS Caliber, DOORS, SD, RDD-OM, DOORS, DADS, DOORS, AutoCAD, AVL, DOORS, Innovation ing Innovation Innovation Dynasty, EASA, Caliber, CATIA, Functional Management Management, Management, Engineous, DOORS, EASA, EDS, Prototyping neer Statemate RDD-OM, Innovation Engineous, Fluent, ngie Statemate Management, Functional Prototyping, d LMS, MatLab, IDEAS, MSC, Opnet, ide MSC, Opnet, Phoenix, ProE, RDD ter-a Phoenix, RDD- SD, StarCD, State OM, RDD-SD, mate, Unigraphics, Statemate, VL Working Model Compu
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System Life Cycle Engineering/Technical Cost Analysis Systems Engineering Activity Marketing Product Engineering Industrial Engineering Marketing Mission, or Product Product Engineering Manufacturing Manufacturing Field Function Customer Needs Planning Architecture Design Engineering Operations Operations Requirements Functional Analysis, Visualization, Analysis and Production Use, Support, Action Synthesis Analysis Analysis and Simulation Visualization and Assembly and Disposal Simulation Caliber, DOORS, Caliber, Caliber, Abaqus, AML, ANSoft, Caliber, Caliber, DOORS Caliber, DOORS, Innovation DOORS, DOORS, Ansys, Caliber, DOORS, Innovation Management, RDD- Innovation CATIA, Delmia DICTRA, DOORS, Functional Management DVF, Statemate Management, V5, Enovia V5, DYNA3D, EASA, EDS, Prototyping, RDD-DVF, RDD-SD, Engineous, Functional HMS-CAPP Statemate, Innovation Prototyping, ICEM ing Working Model Management, CFD, LMS, nee Statemate ModelCenter, MSC, ngie NASTRAN, Phoenix, d RDD-SD, Statemate, ide Stella/Ithink ter-a Visualization Innovation Innovation CATIA, Delmia Abaqus, ACIS, Amira, Functional Innovation Management, RDD- Management, V5, EDS, Enovia Ansys, EDS, EnSight, Prototyping, Management Compu OM, Statemate RDD-OM, V5, Innovation Fakespace, Functional Statemate RDD-SD, Management, Prototyping, Ilogix, Statemate Jack, RDD-SA, Jack, MatLab, Open Slate, Statemate DX, RDD-SD, Rhino, SABRE, Simulink, Slate, Statemate, VisMockup Product Data Innovation Innovation CATIA, Delmia CATIA, Dassault, Innovation Management Management Management V5, Enovia V5 Delmia V5, EDS, Management Enovia V5, Metaphase, ng PTC, Windchill acturi Electronic Caliber, DOORS Caliber, Caliber, Doors, Cadence, Caliber, Caliber, Caliber, DOORS, Caliber, DOORS, anuf Design DOORS, Integrated Dassault, DOORS, DOORS PADS Integrated Analysis m Automation MatLab Analysis, Integrated Analysis, d ide Simulator, Neteor Graphics, PTC, Verilog-XL System Vision ter-a Manufacturing Functional Prototyping, Innovation Integrated Data CimStation, CIM Bridge, Functional System Design RDD-ITDC, RDD-SD Management, Sources, RDD- Envision/Igrip, EDS, Prototyping Compu RDD-ITDC, ITDC, RDD-SD Integrated Data Tecnomatix RDD-SD Sources, RDD-ITDC, RDD-SD 25 continues
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TABLE 3-1 continued 26 Manufacturing Functional Prototyping, Functional DICTRA, Abinitio, CimStation, Abinitio, Arena, Abinitio, Dante, Functional System RDD-ITDC, RDD-SD Prototyping, Functional Dante, DEFORM, Dante, DEFORM, DEFORM, Prototyping Modeling RDD-ITDC, Prototyping, Envision/Igrip, Extend, Functional Functional RDD-SD Pandat, RDD- Functional Prototyping, Prototyping, Prototyping, ITDC, RDD-SD, MAGMA, ProCast, MAGMA, Pro/ MAGMA, Thermo-Calc RDD-ITDC, RDD-SD, Model, ProCast, ProCast, SysWeld Simul8, SysWeld, SysWeld TaylorED, Witness Manufacturing Functional Caliber, Abinitio, Caliber, Abinitio, Arena, Abinitio, Dante, System Prototyping DOORS, CimStation, Dante, Caliber, Dante, DEFORM, Simulation Functional DEFORM, DOORS, DEFORM, MAGMA, Prototyping Envision/Igrip, DOORS, Extend, ProCast, MAGMA, ProCast, MAGMA, SysWeld SysWeld Pro/Model, Pro Cast, Simul8, SysWeld, ng TaylorED, Witness Manufacturing Functional Prototyping Functional Functional CimStation, Arena, Extend, Abinitio, Functional acturi System Prototyping Prototyping Envision/Igrip, Functional Functional Prototyping anuf Visualization Functional Prototyping, Prototyping, m d Prototyping Pro/Model, MAGMA, Pro ide Simul8, Taylor Cast, SysWeld ED, Witness ter-a Reliability RDD-ITDC , RDD-SD Functional DEFORM, CASRE, Functional JMP, Minitab, RDD-ITDC , RDD Models Prototyping, DisCom2, Prototyping, RDD- SAS, WinSMITH SD Compu RDD-ITDC, Functional ITDC, RDD-SD RDD-SD Prototyping, RDD-ITDC, RDD-SD Logistics Eclipse ERP, Integrated Integrated RDD-ITDC , Integrated Analysis, Integrated JD Edwards, Integrated Analysis, Analysis, RDD-ITDC, Analysis, RDD- RDD-SD RDD-ITDC, RDD-SD Analysis Logistics, RDD-ITDC, RDD RDD-SD ITDC, RDD-SD Manugistics SD Purchasing Purchasing plus I2, Invensys, JD Edwards, Oracle, PeopleSoft, PTC, SAP Supervisory QUEST Invensys, Siemens Control Machine Virtual NC Labview, MATLAB, Control Unigraphics
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Figure 3-1 shows that there is little overlap between manufacturing modeling and simulation tools, or manufacturing process planning, and engineering design tools, reflecting the lack of interoperability between these steps with currently available software. Many vendors sell tools that are now beginning to offer intriguing solutions toward overlap of key functions.
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Source: Special permission to reproduce figure from "Advanced Engineering Environments for Small Manufacturing Enterprises," © 2003 by Carnegie Mellon University, is granted by the Software Engineering Institute. design optimization software tools that bundle discrete tools in order to facilitate multiprocess optimization are being introduced.
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It recommends organizational and algorithmic approaches for addressing obstacles. "Life-Cycle Assessment Tools" measures the total environmental impact of manufacturing systems from the extraction of raw materials to the disposal of products and evaluates product and process design options for reducing environmental impact.
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that require interconnections and the sharing of data and information. Engineering Cost Analysis The next logical advance is what is referred to as an engineering or technical cost analysis.
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In its second-generation form, engineering cost analysis software will approximate the costs associated with each phase of the product developmentrealization cycle. In its ultimate form, the engineering cost analysis will include and improve upon all of systems engineering's current discrete event optimization functions; but, more importantly, it will extend forward in time to include accurate estimates for various design, material, and process selection options.
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For that reason, it is imperative that all design options, along with their associated manufacturing processes and materials, be evaluated prior to committing to a final design strategy. Again, several presentations to this committee emphasized the importance of improving the assessment of needs and the exploration of the design trade space and a means to minimize total life-cycle costs.
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Lilienthal, Defense Modeling and Simulation Office, "Observations on the Uses of Modeling and Simulation," presented to the Committee on Bridging Design and Manufacturing, National Research Council, Washington, D.C., February 24-25, 2003. Continue the early collaborative exploration of the largest possible trade space across the life cycle, including manufacturing, logistics, time-phased requirements, and technology insertion.
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All engineers in the design process for a product are also tasked with understanding the economic trade-offs associated with their decisions. At issue are not just the manufacturing costs but also the costs associated with the product's life cycle.
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Also, nearly all of the basic manufacturing cost models are supplemented by yield models, learning curve models, and test/rework economic models. Many commercial vendors exist for manufacturing cost modeling tools.
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Historically, many of these tools are based on a public domain tool called COCOMO15 and later evolutions of it. Life-Cycle Cost Modeling While manufacturing costing is relatively mature, life-cycle cost modeling is much less developed.
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Initially, this cultural change may require a totally separate organizational unit with a reward structure tailored to recognize enterprise successes rather than discrete events. To be fully successful, this new culture ultimately has to infect all levels and units of an organization.
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. functional prototyping has enabled Siemens Transportation Systems to accelerate the overall virtual prototyping process and correct potentially costly errors on the fly before such errors are discovered in manufacturing."a Case Study 3 Conti Temic Product Line Body Electronics utilized software tools to standardize model-based development for electronic control units (ECUs)
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Working with supplier requirements, Conti Temic is able to visually express systems functionality and ensure, up front in the design process, that the product will meet the specifications. In many cases, automotive original equipment manufacturers (OEMs)
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Once a design configuration is selected, analyzed, simulated, prototyped, and validated, the design information is passed to the manufacturing engineers to design the manufacturing systems and processes to fabricate the design in the desired quantities. This manufacturing engineering process entails many of the same steps as in the engineering design process, including the application of sophisticated modeling and simulation.
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The diagram shows that there is little overlap between manufacturing modeling and simulation tools, or manufacturing process planning, and engineering design tools, reflecting the lack of interoperability between these steps with currently available software. However, there are efforts being made to change this situation.
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In this environment, the incentive for engineers to reuse portions of earlier designs is limited. "No-build" conditions: One of the key roles for a stronger bridge between engineering design and manufacturing was repeatedly identified during the course of the study as the need, particularly early in the engineering design process, to be able to easily identify designs that cannot be fabricated or assembled (no-build conditions)
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Researchers at the Applied Research Laboratory (ARL) at Pennsylvania State University recently developed an integrated design process utilizing advanced computational methods and successfully applied it to develop a UUV system.
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Recommendation 2. Engineering Design: The Department of Defense should develop interoperable and composable tools that span multiple technical domains to evaluate and prioritize design alternatives early in the design process.
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to fundamental (ab initio electronic structure calculations) , computational materials science enables a more integral link between materials, design, and manufacturing as illustrated in Figure 3-7.
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Despite their limitations, electronic structure calculations are valuable tools for elucidating the underlying physics of materials behavior. They have been particularly successful in calculating phase diagrams, crystal structures, solute distribution, and the structure and properties of internal defects.
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. Several EAM research codes are publicly available.27 Similar empirical codes are used for polymeric materials.28 Both the strengths (fundamental science basis)
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Because of this, mesoscale models remain a research opportunity but are little used in the manufacturing design process. Continuum Models.
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Otherwise, the models depend on the use of the available phase diagrams. Kinetic models, such as DICTRA,34 apply chemical kinetic models to such thermodynamic information in order to determine phase transformation rates for process control.
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Current research is seeking to utilize laser and other advanced sensing techniques to measure the carbon potential of the atmosphere even more accurately. With typical 4- to 8-hour or longer carburizing cycle times, even minor reductions in cycle time become significant cost reductions while concurrently the improved process control yields quality improvements, such as controlled grain growth.
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Although approximations of this ideal exist in some proprietary industry databases, there is no such data repository for all the nonproprietary data generated in industry, universities, and government laboratories. The creation of such a database could trim redundant experimental efforts to decrease cost and increase productivity.35 This is a particularly compelling argument for defense acquisitions.36 Because the DoD contracts its design and manufacturing work, it often pays for data acquisition to support the design process.
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For example, electronic structure calculations, supported by larger atomistic simulations, can provide phase diagrams and some property information to steer designers toward promising alloys. Mesoscale models can suggest target microstructures and processing routes.
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and manufacturing are completely unconnected and are conducted by analysts in different organizations. Castability constraints are input early in the design process by manufacturing engineers using engineering "rules of thumb" that are imperfect and are imperfectly applied.
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The physics-based computational materials models had to be able to accurately extrapolate and interpolate existing empirical understandings of mechanical properties while also being simple to use and computationally efficient. To accomplish this goal a wide range of materials modeling tools were used and the results linked and embedded in easy-to-use subroutines.
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Generating both an interim and final geometric model is also a significant bottleneck in the overall product design process. In addition, design changes introduced by one analyst to improve the product must be updated and transmitted to the other codes.
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While there are some examples of fully automated design optimization, this process is usually done by trial and error. Facilitating this optimization represents a real opportunity to improve the design process.
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The following is a list of some of the most common tasks and necessary tools: Process planning, including identifying the necessary steps and equipment, and their sequence for part fabrication, for assembly, and for test and inspection Process simulation, analyzing capability, cost, time, and yield, e.g., injection molding, casting, machining, and assembly Logistics planning, planning factory layout, e.g., equipment location, storage, and material flows Factory flow simulation, determining location of bottlenecks and total yield Ergonomics analysis for worker safety and effectiveness Robotics and material-handling simulations, timing, tooling and workstation layouts, and cost Production management of materials requirements planning, manufacturing resource planning, and scheduling Economic analysis and justification Quality measures, including statistical process control (SPC) , six sigma, process capability measurement and analysis Box 3-5 shows an example of the benefits of using manufacturing software tools.
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An average of 83 percent reduction in manual programming time was achieved in a test of five typical aluminum aircraft parts. The total product production process time was reduced by more than one week on average.
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Moreover, the lack of understanding of cultural and managerial barriers may be more important than lack of computer tools. Technical Coordination of Specifications and Procedures Some of these coordination activities include the following: Identification of critical resources such as suppliers, factories, long lead items, and employees' skills needed to manufacture a given design Identification of design and materials alternatives or process alternatives needed to manufacture a given design, together with ways of finding the best combination Determination of the structure of product families and architectures to coordinate with layout, equipment, and organization of the factory to permit flexibility and efficient redeployment of assets to meet changing requirements Alignment of materials properties specifications and production outputs, tolerances on parts and resulting variation, and tolerances on assemblies and resulting variation Collection and utilization of lessons learned during product launch Collection and utilization of lessons learned during use of the product There is a race between advancing knowledge and rising expectations regarding product quality and performance.
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Also, some car companies can bring out new versions of existing cars in as few as 2 years. The reasons appear to be a combination of more astute use of computational tools plus managerial techniques such as coordination of tasks, reuse of existing designs and factories, smart supply chain management, and incentives for design and manufacturing engineers to work more closely together.44 As another example, the new Boeing 7E7 commercial transport is planned to be in final assembly for only 3 days, reflecting the culmination of the lean enterprise transformation of lean engineering, lean supply chain, and 43Charles Fine and Daniel Whitney, "Is the Make-Buy Decision a Core Competence?
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LIFE-CYCLE ASSESSMENT TOOLS This section deals with the evaluation of the environmental impact of a product over its life cycle from concept to disposal and not with life-cycle design or life-cycle analysis, which is a broader topic that encompasses life-cycle costing, design for reliability, design for maintainability, and life-cycle analysis. Some aspects of life-cycle design are addressed in the earlier sections on systems engineering tools and engineering design tools.
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Aggregating these impacts into metrics for decision making is perhaps one of the greatest challenges facing LCA.49 From this point, decision makers, such as product design and development teams, identify strategies to improve environmental performance. Development and implementation of these strategies involve a set of activities known in the industrial ecology community as design for the environment (DfE)
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This definition suggests four steps in LCA: goal and scope definition, resource and emissions inventory, impact assessment, and improvement analysis. The ideal is an objective assessment of the environmental implications of a well-defined production process and the identification of opportunities to improve environmental performance.
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Other researchers have adopted a similar approach, such as Allen55 in his study of chlorine minimization strategies in the chemical industry. Design for the Environment A firm evaluating new technology must balance cost and strategic concerns with environmental performance.
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One subset of OR tools are engineeringeconomic process models. As the name suggests, these models integrate engineering detail about the production process with cost information.
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Box 3-6 illustrates the use of such engineeringeconomic process models for steel production.56 In some cases, the design process is too complex and detailed to perform an overall system optimization. In these cases, process models can be used to optimize system components and less formal methods can be used to arrive at a final design that combines the environment, performance, and cost considerations.
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Another coke steel-making process is Calderon whereby coal feeding and product recovery are employed in a closed process. To evaluate the economic and environmental performance of these technologies, an engineering economic model of steel production is used.
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This finding suggests that electricity supply decisions are a critical element in assessing the economic and environmental performance of new steel production technologies. Some of the Incremental Private and Social Costs of Steel Design Options Scrap Scrap-DR Jewell Corex Electric Electric Calderon New capital expenditures 216.76a 575.42 437.41 503.17 246,32 Labor and capital 32.40 90.78 89.66 72.73 0.48 Energy 4.11 30.69 20.07 15.10 3.02 Materials 0.00 35.89 85.57 63.34 0.82 Total operating cost 28.29 85.59 24.19 24.49 4.32 (less byproduct sales)
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Integrating knowledge of virtual manufacturing into university curricula to train new engineers can help them use tools to bridge design and manufacturing. To ensure an adequate supply of such trained engineers, the DoD can help to develop programs to increase the quality and the number of graduating engineers available to work in these fields.
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