Volume 20 - Materials Selection and Design Part 12 pdf

• Steel Castings Handbook, 6th ed., ASM International, 1995 Deformation Processes • T. Altan, S I. Oh, and H. Gegel, Metal Forming: Fundamentals and Applications, American Society for Metals, 1983 • T.Z. Blazynski, Ed., Plasticity and Modern Metal Forming Technology, Elsevier, 1989 • T.G. Byrer, Ed., Forging Handbook, Forging Industry Association, Cleveland, 1985 • Forming, Vol 2, Tool and Manufacturing Engineers Handbook, 4th ed., Society of Manufacturing Engineers, 1984 • Forming and Forging, Vol 14, ASM Handbook, ASM International, 1988 • S.K. Ghosh and M. Predeleanu, Ed., Materials Processing Defects, Elsevier, 1995 • W.F. Hosford and R.M. Caddell, Metal Forming: Mechanics and Metallurgy, 2nd ed., Prentice Hall, 1993 • K. Lange, Ed., Handbook of Metal Forming, McGraw-Hill, 1985 (now SME) • O.D. Lascoe, Handbook of Fabrication Processes, ASM International, 1988 • Z. Marciniak and J.L. Duncan, The Mechanics of Sheet Metal Forming, Edward Arnold, 1992 • E. Mielnik, Metalworking Science and Engineering, McGraw-Hill, 1991 • R. Pearce, Sheet Metal Forming, Adam Hilger, 1991 • J.A. Schey, Tribology in Metalworking: Friction, Lubrication and Wear, American Society for Metals, 1983 • D.A. Smith, Ed., Die Design Handbook, 3rd ed., Society of Manufacturing Engineers, 1990 • R.H. Wagoner, K.S. Chan, and S.P. Keeler, Ed., Forming Limit Diagrams, TMS, Warrendale, PA, 1989 • R.A. Walsh, Machining and Metalworking Handbook, McGraw-Hill, 1994 Powder Processing • H.V. Atkinson and B.A. Rickinson, Hot Isostatic Pressing, Adam Hilger, 1991 • G. Dowson, Powder Metallurgy: The Process and its Products, Adam Hilger, 1990 • R.M. German, Powder Metallurgy Science, Metal Powder Industries Federation, 1985 • C. Iliescu, Cold-Pressing Technology, Elsevier, 1990 • H.A. Kuhn and B.L. Ferguson, Powder Forging, Metal Powder Industries Federation, 1990 • M.H. Liebermann, Rapidly Solidified Alloys, Dekker, 1993 • Powder Metallurgy, Vol 7, ASM Handbook, American Society for Metals, 1984 • Powder Metallurgy Design Manual, 2nd ed., Metal Powder Industries Federation, 1995 Machining Processes • G. Boothroyd and W.W. Knight, Fundamentals of Machining and Machine Tools, 2nd ed., Dekker, 1989 • Machining, Vol 1, Tool and Manufacturing Engineers Handbook, 4th ed., Society of Manufacturing Engineers, 1983 • Machining, Vol 16, ASM Handbook, ASM International, 1989 • S. Malkin, Grinding Technology: Theory and Applications, Ellis Horwood, 1989 • P.L.B. Oxley, The Mechanics of Machining, Ellis Horwood, 1989 • M.C. Shaw, Metal Cutting Principles, 4th ed., Oxford University Press, 1984 • D.A. Stephenson and J.S. Agapiov, Metal Cutting Theory and Practice, Dekker, 1996 • R.A. Walsh, Machining and Metalworking Handbook, McGraw-Hill, 1994 Joining Processes • Adhesives and Sealants, Vol 3, Engineered Materials Handbook, ASM International, 1990 • Brazing Handbook, 4th ed., American Welding Society, 1991 • G. Humpston and D.M. Jacobson, Principles of Soldering and Brazing, ASM International, 1993 • D.L. Olson, R. Dixon, and A.L. Liby, Ed., Welding Theory and Practice, North Holland, 1990 • R.O. Parmley, Ed., Standard Handbook of Fastening and Joining, 3rd ed., McGraw-Hill, 1997 • A. Rahn, The Basics of Soldering, Wiley, 1993 • M. Schwartz, Brazing, ASM International, 1987 • Welding, Brazing, and Soldering, Vol 6, ASM Handbook, ASM International, 1993 • Welding Handbook, 8th ed., American Welding Society, 1996 Ceramics Processing • Ceramics and Glasses, Vol 4, Engineered Materials Handbook, ASM International, 1991 • Engineered Materials Handbook Desk Edition, ASM International, 1995 • S. Musikant, What Every Engineer Should Know about Ceramics, Dekker, 1991 • G.C. Phillips, A Concise Introduction to Ceramics, Van Nostrand-Rheinhold, 1991 • J.S. Reed, Principles of Ceramics Processing, 2nd ed., Wiley, 1995 • M.M. Schwartz, Ceramic Joining, ASM International, 1993 • M.M. Schwartz, Handbook of Structural Ceramics, McGraw-Hill, 1992 • R.A. Terpstra, P.P.A.C. Pex, and A.H. DeVries, Ed., Ceramic Processing, Chapman & Hall, 1995 Polymer Processing • R.J. Crawford, Ed., Rotational Moulding of Plastics, Wiley, 1992 • Engineering Plastics, Vol 2, Engineered Materials Handbook, ASM International, 1988 • R.G. Griskey, Polymer Process Engineering, Chapman & Hall, 1995 • Handbook of Plastics Joining: A Practical Guide, Plastics Design Library, 1996 • N.C. Lee, Ed., Plastic Blow Molding Handbook, Van Nostrand-Rheinhold, 1990 • N.G. McCrum, C.P. Buckley, and C.B. Bucknall, Principles of Polymer Enginering, Oxford University Press, 1989 • E.A. Muccio, Plastics Processing Technology, ASM International, 1994 • Plastic Parts Manufacturing, Vol 8, Tool and Manufacturing Engineers Handbook, Society of Manufacturing Engineers, 1995 • R.C. Progelhof and J.L. Throne, Polymer Engineering Principles: Properties, Tests for Design, Hanser, Munich, 1993 • G.W. Pye, Injection Mold Design, Longman/Wiley, 1989 • D.V. Rosato, D.P. DiMattia, and D.V. Rosato, Designing with Plastics and Composites: A Handbook, Van Nostrand-Rheinhold, 1991 Manufacture of Composites • Composites, Vol 1, Engineered Materials Handbook, ASM International, 1987 • L. Hollaway, Handbook of Polymer Composites for Engineers, Woodhead, Cambridge, 1994 • B.Z. Jang, Advanced Polymer Composites: Principles and Applications, ASM International, 1994 • M.M. Schwartz, Composite Materials Handbook, McGraw-Hill, 1992 • M.M. Schwartz, Handbook of Composite Ceramics, McGraw-Hill, 1992 • M.M. Schwartz, Joining of Composite Matrix Materials, ASM International, 1994 • W.A. Woishnis, Ed., Engineering Plastics and Composites, 2nd ed., ASM International, 1993 Modeling of Manufacturing Processes Anand J. Paul, Concurrent Technologies Corporation Introduction MANUFACTURING PROCESSES typically involve the reshaping of materials from one form to another under a set of processing conditions. To minimize the production cost and shorten the time to market for the product, all iterations in terms of an appropriate set of operating conditions should not be done on the shop floor. Predictive models need to be used generously to perform numerical experiments to give an insight into the effect of the operating conditions on the properties of the final product. Process models must be able to build the geometry of the product/process that is being modeled, accurately describe the physics of the process, and be able to analyze the results in a way that is comprehensible by manufacturing engineers. There are several types of models that are used by the industry. These include models used as a tool in the course of scientific research, models that are very generalized in nature and can be applied to a wide variety of processes, but may not be able to address the nuances of any one process, models that are very specific in nature and can address a narrow range of operating conditions, models that rely on gross phenomena and are about 90% accurate but take only 10% of the execution time or more accurate models. Irrespective of the complexity, most models are used to gain one or more of the following advantages: • Reduce iterations on the shop floor • Optimize an existing process • Understand an existing process better • Develop a new process • Improve quality by reducing the variability in the product and process Modeling of Manufacturing Processes Anand J. Paul, Concurrent Technologies Corporation Classification of Models The following points need to be considered in order to classify modeling problems: • The physical phenomena affecting the process under consideration • Mathematical equations describing the physical process • Data needed to solve the equations • Numerical algorithm to solve the equations given the boundary conditions and the constitutive behavior • Availability of the software to provide answers One of the most common classification methods is by type of process or physics. This means that one must identify the major phenomena occurring in the process, for example, convection, radiation, chemical reaction, diffusion, deformation, and so forth. Once the phenomena has been identified, the process needs to be defined in terms of mathematical equations, typically partial differential equations. These equations are dependent on time, space, field variables, and internal states. Ordinary differential equations can be used if the problem can be simplified so that the shape is not important and a lumped-parameter model can be used. Several people have used lumped-parameter models for various materials processes (Ref 1, 2). The requirements for particular data and the way in which it is gathered is an important step in the construction of a model. Researchers typically play down this step as an "industrial implementation detail." This means that the rest of the model needs to be robust and accurate before data are needed. Once accurate data are available, the result of the modeling effort will be good too. On the other hand, industrial practitioners place a greater emphasis on data gathering because they know the difficulties and time involved in gathering data on production-scale equipment. Numerical algorithms to solve the differential equations consist of meshed-solution methods and lumped-parameter models. The major meshed-solution models consist of finite differences, finite elements, and boundary methods. Each of them is more appropriate for different types of equations and boundary conditions. Within these methods, one can use a structured or an unstructured mesh. Structured meshes are created by using rectilinear, bricklike elements. It is easy to use this type of mesh; however, fine geometry details may be missed. Unstructured meshes can be of any shape tetrahedra, bricks, hexahedral, prisms, and so forth. Many of the disadvantages of using a structured mesh are eliminated through this type of a mesh. Lumped-parameter models may help in understanding the effect of certain parameters on the process as along as the problem formulation does not change. These models do not model spatial variation directly, and the parameters may or may not be physically meaningful in themselves. Choice of the appropriate software is an important aspect of the usefulness of the model. Almost without exception, research process models and all commercial software can be written directly in a third-generation language (Fortran, Lisp, Pascal, C, C++). User interfaces can be derived from various libraries. Because of the large number of calculations necessary to get the desired degree of detail, models may require parallel computing hardware for cost-effective solutions. Software developed for this has to be able to run and make use of the parallel-processing capabilities of the hardware. Models for manufacturing processes can be classified in two primary ways, as shown in Fig. 1. One classification scheme considers whether the model is on-line or off-line; the other considers whether the model is empirical, mechanistic, or deterministic. Fig. 1 Classification of models for manufacturing processes Fully on-line models are part of the bigger process control system in a plant. Sensors and feedback loops are characteristics of these models. They get their input directly from the system. These models implement changes in the plant on a continuous basis (Ref 3). Fully on-line models are extremely fast and reliable; therefore, these models need to be rather simple without the need to do any significant numerical calculations. For these models to be reliable, the physics of the process that they are addressing must be understood thoroughly. A good example of this type of model is the spray water system on a slab-casting machine, which is designed to deliver the same total amount of water to each portion of the strand surface. The flow rate changes to account for variations in the casting-speed history experienced by each portion as it passes through the spray zones. Semi-on-line models are similar to fully on-line models. The distinguishing factor is that rather than the model taking appropriate action, the process engineer analyzes the situation and performs any corrective action, if needed. These models are typically slightly more complex than the fully on-line models because they are essentially run by an operator. These models need to have an excellent user interface and should require minimum user intervention. Off-line models are typically used in the premanufacturing stage, that is, during research, design, or process parameter determination. These models help to gain an insight into the process itself and thereby help optimize it. There are many general-purpose models as well as models designed to be used for very specific applications. These models are typically very complex and therefore need to be validated thoroughly before being used for any real predictions. Literature models are those that exist primarily in the literature and are seldom used in conjunction with experiments. Typically these models are developed and run by the same individual. The advantage of literature models is that other developers can benefit from them instead of starting from scratch. Empirical models are developed through statistical data gathering on a number of similar events. The model does not help one understand the process itself and may not be valid beyond the range of the available data. Mechanistic models are based on the solution of the mathematical equations that represent the physics of the process that is being addressed. These models are very extensible and study the effect of a variety of external factors on the process. Neural network models are based on artificial neural networks and provide a range of powerful techniques for solving problems in pattern recognition, data analysis, and control. Neural networks represent a complex, trainable, nonlinear transfer function between inputs and outputs. This allows an effective solution to be found to complex, nonlinear problems such as heat distribution. References cited in this section 1. M.F. Ashby, Physical Modeling of Materials Problems, Mater. Sci. Technol., Vol 8 (No. 2), 1992, p 102-111 2. H.R. Shercliff and M.F. Ashby, Modeling Thermal Processing of Al Alloys, Mater. Sci. Technol., Vol 7 (No. 1), 1991, p 85-88 3. B.G. Thomas, Comments on the Industrial Application of Process Models, Materials Processing in the Computer Age II, V.R. Voller, S.P. Marsh, and N. El- Kaddah, Ed., The Minerals, Metals & Materials Society, 1995, p 3-19 Modeling of Manufacturing Processes Anand J. Paul, Concurrent Technologies Corporation Important Aspects of Modeling There are several important issues that need to be addressed to understand what is important in modeling in general and what is important to the current problem in particular. Some of these are briefly discussed below. Analytical versus Meshed Models. Developing an analytical or a closed-form solution model may be advantageous in many instances. However, it may be necessary to construct a discrete meshed model for finite element or finite difference calculations if the modeled volume: • Has a complex shape (commonly found in many engineering applications) • Contains different phases and grains, which are typically modeled by research groups (Ref 4) • Contains discontinuous behavior such as a phase change, which can be handled easily with meshes using a volume-of-fluid (VOF) technique (Ref 5) • Has nonlinear process physics such as when the heat transfer coefficient is a nonlinear function of the temperature In many instances, meshed models are supplemented by some nonmeshed symbolic or analytical modeling. This is done in order to decide on appropriate boundary conditions for the meshed part of the problem because it is the boundary conditions that effectively model the physical problem and control the form of the final solution (Ref 6). Analytic models are always useful for distinguishing between mechanisms that have to be modeled as a coupled set and mechanisms that can be modeled separately. These models no longer need closed-form solutions. Even simple computers can track evolving solutions and iterate to find solutions to implicit formulations (Ref 1). Boundary Conditions and Multiphysics Models. Application of appropriate boundary conditions is a major part of the activity of process modeling. Boundary conditions are statements of symmetry, continuity and constancy of temperature, heat flux, strain stress, and so forth. Boundary conditions need to be set at a very early stage in analytical models. In meshed models, these are typically represented separate from the main equations and are decoupled to some extent from the model itself. Therefore, sensitivity analysis can be done much easier using meshed methods. The type of boundary conditions used also determines what solving algorithm should be used for the partial differential equations. This determines the speed, accuracy, and robustness of the solution. Material Properties. All process models require material properties to be simulated. Acquiring these properties can be difficult and expensive (Ref 7). A sensitivity analysis of the model with respect to these data provides information as to the importance of minor changes in them. In many instances, it may be possible to use models with doubtful material property information in order to predict trends, as opposed to determining actual values. A problem arises if the material properties are extrapolated beyond the range of their applicability where one does not know the behavior of the material at all. Related information is provided in the articles "Computer-Aided Materials Selection" and "Sources of Materials Properties Data and Information" in this Volume. Modeling Process Cycles. The process of modeling is done in different cycles. Figure 2 attempts to distinguish those cycles (Ref 8). This figure shows three loops suggesting three levels of activities in any modeling effort. The outer loop is managed by someone close to the process who understands the business context of the problem and can concentrate on specifying the objective and providing the raw data. The innermost loop (shaded dark) requires mostly computational skills while the middle loop (shaded light) consists of activities balancing the other two. It may very well happen that all three of some combination of the activities can be done by the same person. However, very seldom is that the case. This highlights the need for forming modeling teams where all aspects of the problem can be addressed rigorously. It also emphasizes the importance of training and appropriate software tool development so that the input and output of the tools can be easily understood by all involved in the process. Fig. 2 Modeling cycles. Source: Ref 8 References cited in this section 1. M.F. Ashby, Physical Modeling of Materials Problems, Mater. Sci. Technol., Vol 8 (No. 2), 1992, p 102-111 4. M. Rappaz and Ch A. Gandin, Probabilistic Modeling of the Microstructure Formation in Solidification Processes, Acta. Metall. Mater., Vol 41 (No. 2), 1993, p 345-360 5. J. Wang, S. Xun, R.W. Smith, and P.N. Hansen, Using SOLVA- VOF and Heat Convection and Conduction Technique to Improve the Casting Design of Cast Iron, Modeling of Casting, Welding and Advanced Solidification Processes VI, T. Piwonka, Ed., TMS, 1993, p 397-412 6. L. Edwards and M. Endean, Ed., Manufacturing with Materials, Materials in Action, Butterworth Scientific, 1990 7. S.C. Jain, Recognizing the Need for Materials Data: The Missing Link in Process Modeling, J. Met., Oct 1991, p 6-7 8. K J. Bathe, Some Issues for Reliable Finite Element Analysis, Reliability Methods for Engineering Analysis: Proc. First International Conference, K J. Bathe and D.R.J. Owen, Ed., Pineridge Press, Swansea, U.K., 1986, p 139-157 Modeling of Manufacturing Processes Anand J. Paul, Concurrent Technologies Corporation Modeling of Deformation Processes Finite element analysis (FEA) of deformation processes can provide an insight into the behavior of the product under various processing conditions and can help optimize the conditions to get the desired properties. It can also help understand the performance of the product before the part is put in actual use. Common problems solved by FEA include insufficient die filling, poor shape control, poor flow of material, cracks and voids that lead to fracture and poor final part properties. The occurrence of typical processes in a forging operation are shown in Fig. 3. Figure 4 (Ref 9) shows a schematic representation of the interactions between the major process variables in metal forming. From Fig. 4 it can be seen that for a metal-forming analysis, one needs to satisfy the equilibrium conditions, compatibility equations/strain-displacement relations, constitutive equations, and, in some instances, the heat balance equation. In addition, one needs to apply appropriate boundary conditions. These may comprise displacement/velocity imposed on a part of the surface while stress is imposed on the remainder of the surface, heat transfer, or any other interface boundary condition. Fig. 3 Typical physical phenomena occurring during a forging operation Fig. 4 Interaction among major process variables during forming. Source: Ref 9 Relevant Equations. The equilibrium equations describing the various forces acting on the body are given as: (Eq 1) where is the normal stress component, is the shear stress component, and F is the body force/unit volume component. Similarly, the strain-displacement relationships are given as: (Eq 2) where is the normal strain, is the shear strain, and u, v, and w are the displacements in the x, y, and z directions, respectively. Constitutive Theory. A constitutive equation relates the stress and strain behavior of a material. Schematic stress/strain curves for idealized materials are shown in Fig. 5. In addition to this information, a yield criterion and flow rules are also needed to adequately describe the material behavior. The stress/strain curve for a typical metal along with its major features is shown in Fig. 6. Fig. 5 Schematic stress/strain curves for various materials Fig. 6 Stress/strain curve for a typical metal [...]... resulting heat transfer and fluid flow affect the size and shape of the weld pool, the cooling rate, and the kinetics and extent of various solid-state transformation reactions in the fusion zone and heat-affected zone The weld geometry influences dendrite and grain-growth selection processes Both the partitioning of nitrogen, oxygen, and hydrogen between the weld pool and its surroundings, and the vaporization... Voller, S.P Marsh, and N El-Kaddah, Ed., The Minerals, Metals & Materials Society, 1995, p 3-1 9 4 M Rappaz and Ch.-A Gandin, Probabilistic Modeling of the Microstructure Formation in Solidification Processes, Acta Metall Mater., Vol 41 (No 2), 1993, p 34 5-3 60 5 J Wang, S Xun, R.W Smith, and P.N Hansen, Using SOLVA-VOF and Heat Convection and Conduction Technique to Improve the Casting Design of Cast Iron,... the investment-casting process, taking radiation loss into account through the use of a novel approach for view-factor calculations (Ref 17) Knowledge-Based Systems for Rigging Design The starting step after the initial design of the casting is the design of gates and risers This consists of proper orientation of the part and the determination of the parting plane and the size, number, and location... 8 5-1 12 Manufacturing Cost Estimating David P Hoult and C Lawrence Meador, Massachusetts Institute of Technology Introduction COST ESTIMATION is an essential part in the design, development, and use of products In the development and design of a manufactured product, phases include concept assessment, demonstrations of key features, and detailed design and production The next phase is the operation and. .. Guleyupoglu, Knowledge-Based Design of Rigging Systems for Light Alloy Castings, AFS Trans., Vol 99, 1992, p 9 1-9 6 21 R Gadh and F.B Prinz, Shape Feature Abstraction in Knowledge-Based Analysis of Manufactured Products, Proc of the Seventh IEEE Conf on AI Applications, Institute of Electrical and Electronics Engineers, 1991, p 198 -2 04 22 G Upadhya, A.J Paul, and J.L Hill, Optimal Design of Gating and Risering... Guleyupoglu, Knowledge-Based Design of Rigging Systems for Light Alloy Castings, AFS Trans., Vol 99, 1992, p 9 1-9 6 21 R Gadh and F.B Prinz, Shape Feature Abstraction in Knowledge-Based Analysis of Manufactured Products, Proc of the Seventh IEEE Conf on AI Applications, Institute of Electrical and Electronics Engineers, 1991, p 198 -2 04 22 G Upadhya, A.J Paul, and J.L Hill, Optimal Design of Gating and Risering... Conference, K.-J Bathe and D.R.J Owen, Ed., Pineridge Press, Swansea, U.K., 1986, p 13 9-1 57 9 S Kobayashi, S Oh, and T Altan, Metal Forming and the Finite Element Method, Oxford University Press, 1989, p 27 10 M.L Tims, J.D Ryan, W.L Otto, and M.E Natishan, Crack Susceptibility of Nickel-Copper Alloy K-500 Bars During Forging and Quenching, Proc First International Conference on Quenching and Control... M.K Simmons, Designing with Features: Creating and Using Features Database for Evaluation of Manufacturability of Castings, ASME Comput Rev., 1988, p 28 5-2 92 19 J.L Hill and J.T Berry, Geometric Feature Extraction for Knowledge-Based Design of Rigging Systems for Light Alloys, Modeling of Casting, Welding and Advanced Solidification Processes V, TMS, 1990, p 32 1- 328 20 J.L Hill, J.T Berry, and S Guleyupoglu,... D.J Srolovitz, Ed., TMS-AIME, 1985, p 17 1-1 88 25 D.M Stefanescu, G Upadhya, and D Bandyopadhyay, Heat Transfer-Solidification Kinetics Modeling of Solidification of Castings, Metall Trans A, Vol 21A, 1990, p 99 7-1 005 26 J.A Spittle and S.G.R Brown, Acta Metall., Vol 37 (No 7), 1989, p 180 3-1 810 27 E Niyama, T Uchida, M Morikawa, and S Saito, A Method of Shrinkage Prediction and Its Application to Steel... D.J Srolovitz, Ed., TMS-AIME, 1985, p 17 1-1 88 25 D.M Stefanescu, G Upadhya, and D Bandyopadhyay, Heat Transfer-Solidification Kinetics Modeling of Solidification of Castings, Metall Trans A, Vol 21A, 1990, p 99 7-1 005 26 J.A Spittle and S.G.R Brown, Acta Metall., Vol 37 (No 7), 1989, p 180 3-1 810 27 E Niyama, T Uchida, M Morikawa, and S Saito, A Method of Shrinkage Prediction and Its Application to Steel . the initial design of the casting is the design of gates and risers. This consists of proper orientation of the part and the determination of the parting plane and the size, number, and location. is provided in the articles "Computer-Aided Materials Selection& quot; and "Sources of Materials Properties Data and Information" in this Volume. Modeling Process Cycles. The process. D.L. Olson, R. Dixon, and A.L. Liby, Ed., Welding Theory and Practice, North Holland, 1990 • R.O. Parmley, Ed., Standard Handbook of Fastening and Joining, 3rd ed., McGraw-Hill, 1997 • A.