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Effect of microstructure and chemical composition on dynamic factor of high strength steelsJ. Qu*, W. Dabboussi, J. Nemes and S. YueThe high strain rate properties of high strength steels with various microstructures and static strength levels were studied by means of split Hopkinson bar apparatus in shear punch mode. The as received cold rolled sheet steels were subjected to a variety of heat treatment conditions to produce several different microstructures, namely ferrite plus pearlite (FzP), ferrite plus bainite(FzB), ferrite plus martensite (FzM) and ferrite plus bainite and retained austenite (FzBzRA). According to the variation of dynamic factor (ratio of dynamic to static strength) with static strength, two groups of microstructures with two distinct behaviours were identified, i.e. classic dual phase (ferrite plus martensite) and multiphase (including ferritepearlite, ferritebainite, etc.). It was also observed that the general dependence of microstructure on the dynamic factor was strongly influenced by chemical composition in the case of ferrite plus martensite microstructures.Keywords: DP steel; TRIP steel; Hopkinson bar; Shear punch test; Dynamic factor; IntroductionHigh strength steels are being extensively applied to automobile body structures to improve crashworthiness without increasing weight. In particular, dual phase (DP) and transformation induced plasticity (TRIP) steels have received particular interest, as they exhibit an excellent combination of cold formability and strength, compared to conventional high strength low alloy (HSLA) steels.1,2 Dual phase steel is characterised by a matrix of ferrite with small islands of martensite. The hard martensite particles provide substantial strengthening while the ductile ferrite matrix gives good ductility. Therefore, the mixture acts somewhat like a particle reinforced composite and exhibits continuous yielding behaviour and a high work hardening rate. Transformation induced plasticity steel contains ferrite, bainite and retained austenite, and the relatively high ductility of this type of steel results from the transformation of metastable retained austenite to martensite under straining. High strength low alloy steel grades, which are low carbon grades with microalloying additions of Nb, Ti, and/or V, are also key materials fulfilling ultra light weight design requirement. The combination of microalloying and thermomechanical processing allows the exploitation of different strengthening mechanisms.35 . To obtain an indication of crashworthiness of a material, high strain rate deformation performance, quantified by parameters such as dynamic strength and absorbed energy, is usually examined by Hopkinson bar testing.6,7 or drop weight crush testing.8,9 Strain rate sensitivity can be defined by the strain rate sensitivity coefficient (e.g. the m-value of the extended Hollomon equation10 or the C-value of the Johnson-Cook equation11), or by the dynamic factor, which is the ratio of the material dynamic strength to static strength. Although there are some contradictory results in the literature, it is generally found that the dynamic factor decreases with increasing static strength level12,13. However, it seems that the dependence of microstructure on the general behaviour of dynamic factor with respect to static strength has not been clearly understood, and the effect of chemical composition has not been studied adequately. This investigation was undertaken to determine the effect of microstructure on the strain rate sensitivity of three steel compositions, which were originally designed to be high strength DP, TRIP and HSLA steels respectively. Different types of microstructures with a wide range of static strength levels were obtained by various heat treatments on the as received cold rolled steels. The dynamic properties of these microstructures were then measured by means of shear punch testing, using a split Hopkinson bar apparatus. The effect of chemistry was also studied by comparing the dynamic factor of certain microstructures generated from different steels.ExperimentalMaterialsThe materials used in this study were three cold rolled sheet steels with a thickness of 1-7 mm, the compositions1 Schematic diagrams of various heat treatment schedulesof which were originally designed to be high strength DP, TRIP and HSLA steels respectively, as listed in Table 1. Steel A has a relatively high level of manganese plus some molybdenum to facilitate the formation of the dual phase microstructure. Steel B has a very high level of aluminium to enable the formation of retained austenite. Steel C is mainly strengthened by grain refining and precipitation hardening by a small addition of niobium. Each of these steels was heat treated to generate different types of microstructures, namely ferritezmartensite (FzM), ferritezpearlite (FzP), ferritezbainite (FzB), and ferritezbainitezretained austenite (FzBzRA), as listed in Table 1.Heat treatmentsSmall square specimens with a dimension of 30630 mm were cut out from the sheets for heat treatment, microstructural examination and static and dynamic shear punch testing. Figure 1 shows schematically the three heat treatment schedules used to generate various microstructures. In schedule 1, after annealing at different temperatures (TA) for different times (tA), the specimens were cooled to room temperature by water quenching (WQ), forced air cooling (FAC), air cooling (AC), or a very low rate cooling (VLC, i.e. 0.5 or 1.5 s-1). Schedule 2 is a two stage heat treatment, an intercritical annealing followed by an isothermal holding (TB, tB) in the bainite region, which was dedicated to steel B only, to produce ferrite+zbainite+zretained austenite microstructures. Schedule 3 was designed to produce ferrite plus martensite microstructures with lower strengths by a high temperature annealing (TH, tH) and a very low rate cooling (0?5uC s21) to room temperature, followed by intercritical annealing and water quenching. Table 2 lists the detailed parameters used in these heat treatment schedules for all the three steels. Specific etching solutions in addition to nital were used to characterise various heat treated microstructures, i.e. nital followed by 10% aqueous sodium metabisulfite (Na2S2O5) and LePeras etchant. Etching with nital and aqueous sodium metabisulphite reveals the ferrite as grey, bainite or martensite as black, and retained austenite as white. With LePeras etchant, martensite or retained austenite appears white, bainite appears black, and ferrite appears grey. Static/dynamic shear punch testing Static shear punch testing was performed on a hydraulic MTS machine with a special punch die fixture in which the specimen was sandwiched between the punch and the die. The diameters of the punch and the die are 9-4 and 9?55 mm respectively,2 Schematic illustration of Hopkinson bar apparatus for shear punch testingand the punching speed was 0.01 mm s-1. Figure 2 shows the schematic illustration of the Hopkinson bar apparatus for dynamic shear punch testing. The strike bar, driven by compressed air, hits the incident bar at a very high speed, and the incident bar deforms the specimen placed between it and the transmitted bar. The load and displacement are measured by means of strain gauges attached to the incident bar and transmitted bar. The signals from the strain gages are monitored and acquired by an oscilloscope. The incident bar, which is the punch, and the transmitted bar, which is the die, have the same diameters of 9?4 and 9?55 mm as that used in the static shear punch test, and the punching speed was 25 m s-1 (90 km h-1). Results and discussionMicrostructuresFigure 3a and b shows typical ferrite plus martensite microstructures of steel A produced by intercritical annealing and water quenching. Figure 3c and d shows the ferrite plus pearlite (Fig.3c) and ferrite plus bainite (Fig.3d) microstructures of steel A generated by annealing followed by a very low rate cooling and forced air cooling respectively. Figure 4a and b shows typical ferrite plus pearlite microstructures of different temperature annealed specimens of steel C. Figure 4c and d shows the ferrite plus bainite (Fig.4c) and ferrite plus martensite (Fig.4d) microstructures of steel C produced by high temperature annealing at 1000uCfollowed by air cooling and intercritical annealing followed by water quenching respectively. Figure 5 shows the various microstructures of steel B. The ferrite plus pearlite (Fig.5a) and ferrite plus martensite (Fig.5b) microstructures were produced by annealing followed by a very low rate cooling and water quenching respectively. The TRIP microstructures containing ferrite plus bainite and retained austenite (Fig.5c and d) were obtained by intercritical annealing and bainite holding process.Typical shear punch curvesFigure 6 shows typical forcedisplacement curves of static and dynamic shear punch tests. As may be expected, the dynamic force is higher than static force, and the dynamic fracture displacement is smaller than static fracture displacement. The shear punch force displacement curves have some similarities to the tensile stressstrain curves, e.g. elastic loading to the yield point, plastic deformation, and the ultimate load at failure. In a previous study, 14 a linear relationship between the maximum shear punch force (Fmax, as shown in Fig. 6) and the ultimate tensile strength (UTS) was found. Since the shear punch deformation speed is the same in all cases, it is reasonable to take the maximum shear punch force as a measure of the tensile strength to compare different specimens. Effect of microstructureFigure 7 shows the variation of the dynamic factor of maximum force (the ratio of dynamic to static maximum force) with static maximum force for all the microstructures of steel A. The overall trend is that the dynamic factor decreases with increasing static strengthTable 2 Detailed parameters used in various heat treatment schedulesa 750uC/2 min WQ (Fz22%M); b 775uC/2 min WQ (Fz40%M); c 850uC/2 min 0-5uC s21 (FzP); d 800uC/2 min FAC (FzB)3 Microstructures of various heat treated specimens of steel A . There appears to be two different populations of specimens, ferrite plus martensite and multiphase (including ferritepearlite and ferritebainite) microstructures, as delineated by the ellipses superimposed on this graph. In general, for the same static strength, ferrite plus martensite microstructures appear to have higher values of dynamic factor than any of the other microstructures investigated. The general variation of dynamic factor with respect to static strength can be explained by the thermal activation theory of plastic deformation.15 Flow stress can be written as the sum of two components, a thermal component, sth, due to short range obstacles and is dependent on strain rate and temperature, and an athermal component, sath, due to long range obstacles and is dependent on material structure, as ssthzsath. (1) It was generally observed1618 that, in the static strain rate range and at room temperature, the thermal stress contributes very little to the overall stress. The dynamic factor of stress R can then be expressed as R sdynamic sstatic sath(dynamic)zsth sath(static). (2)The temperature rise during dynamic deformation have a small effect on the athermal stress, as shown by the measured temperature dependence of shear modulus.19 Assuming sath(dynamic)5sath(static), the above equationthen becomes R1zsth sath. (3)It can be seen from the above equation that at a given strain rate, the dynamic factor decreases with the increase in sath by increasing static strength. Basically, the physical reason why increasing static strength reduces the dynamic factor, in this simple analysis, is because the athermal stress does not directly affect the thermal stress. Dual phase steel is known to have high density of mobile dislocations generated by the austenite to martensite transformation. These mobile dislocations may act as thermal barriers and thereby increase the thermal stress, which is probably the reason why dual phase steel has a higher dynamic factor compared to other microstructures.Effect of chemistryFigure 8 shows the variation of the dynamic factor of maximum force with static maximum force for all the steels and microstructures. It can be seen that the dynamic factors depend mainly on microstructure as opposed to chemistry. For example, if steel C is heat treated to create a ferrite plus martensite microstructure, it falls within the envelope of that microstructural type. Thus, from these results, there does not seem to be a strong effect of chemistry, for the same microstructure, on dynamic factor.a 800uC/2 min AC (FzP); b 900uC/2 min 1?5uC s21 (FzP); c 1000uC/2 min AC (FzB); d 750uC/2 min WQ (FzM)4 Microstructures of various heat treated specimens of steel CIt can also be seen that the FzBzRA microstructures (the TRIP microstructures) have lower dynamic factors than the FzM microstructures with similar static strength levels, which tends to confirm the findings in the literature.8 This is probably because the much pronounced deformation heat generated during the dynamic test increases the stability of the retained austenite, which lowers the strengthening effect from the austenite to martensite transformation.One exception to the general variation is the ferritezmartensite microstructures generated from steel B (the TRIP chemistry), as shown in the broken ellipse, which exhibit very high dynamic factors at the static strength levels observed. This may be because solid solutions increase the thermal stress, since steel B has a higher alloying level than steel A and steel C. This is also a possible reason why the TRIP microstructures generated from steel B belong more in the population of FzM microstructures than the population of multiphase.Solute atoms could be treated as both long range and short range obstacles to dislocation movement,20 whereas grain boundaries, dislocation forests, precipitates,and other defects could be only long range obstacles. The short range obstacles can be overcome with the assistance of thermal activation, and hence are dependent on strain rate and temperature. As the strain rate is increased, there is less time available to overcome the obstacles and the thermal energy will be less effective, and thereby increases the thermal stress. It may be that different elements influence the athermal and thermal components of stress in different manners, and harden a material differently in static and dynamic cases.The results on the effects of microstructure and chemistry on the static and dynamic property relationship suggest that solid solution strengthening probably should be more utilised in the design of crashworthysteels. Design of steels based on solid solutions in combination with a dual phase microstructure may be the next phase in the development of automotive steels.Although the main focus of this paper is on strength, ductility is also a key factor for automotive steels. Investigations have been conducted on different aspects of formability in the literature. In particular, Misra et al.4,5 studied the effect of microstructure on the formability of hot rolled NbTi and VNb microalloyed steels. On the other hand, absorbed energy is essentially a combination of strength, ductility, and work hardening rate of the flow curve. Thus, a high value of elongation may lead to a high absorbed energy in the dynamic case. More work is required to understand the effect of microstructure on the dynamic properties on the aspect of ductility.Conclusions A wide range of microstructural variants was generated by subjecting cold rolled high strength steels to variousa 850uC/5 min 0?5uC s-1 (FzP); b 800uC/5 min WQ (FzM); c 850uC/5 minz400uC/2 min WQ (FzBzRA, 7%RA); d 850uC/5 minz400uC/10 min WQ (FzBzRA, 3%RA)5 Microstructures of various heat treated specimens of steel Bannealing and cooling conditions. The subsequent changes in mechanical properties were measured by means of static and dynamic shear punch tests. It was found that the variation of dynamic factor with static strength was influenced by microstructure, which can be classified in two broad groups: classic dual phase and multiphase. The dynamic factor generally decreased with increasing static strength, but the ferrite and martensite microstructures appeared to have higher values of dynamic factor than any of the other microstructures investigated. It was also found that the6 Typical forcedisplacement curves of static and dynamic shear punch testsgeneral variation of the dynamic factor was not strongly influenced by chemical composition, except the ferrite plus martensite microstructures generated by the TRIP composition, which exhibited much better dynamic factor values. From these observations, a new paradigm for design of crashworthy steels is suggested: that of combining the mobile dislocation of dual phase steel with solid solution mechanisms7 Variation of dynamic factor of maximum force with static maximum force for all microstructures of steel A8 Variation of dynamic factor of maximum force with static maximum force for all steels and microstructuresAcknowledgementThis research is funded by AUTO21, a member of the Network of Centres of Excellence of Canada programme.References1. D. Cornette, T. Hourman, O. Hudin, J. P. Laurent and A. Reynaert: SAE Trans. J. Mater. Manuf., 2001, 110, 3747.2. J. R. Shaw and B. K. Zuidema: SAE Trans. J. Mater. Manuf., 2001, 110, 976983. 3. C. P. Reip, S. Shanmugam and R. D. K.Misra: Mater. Sci. Eng. A, 2006, A424, 307317.4. R. D. K. Misra, K. K. Tenneti, G. C. Weatherly and G. Tither: Metall. Mater. Trans. A, 2003, 34A, 23412351.5. R. D. K. Misra, S. W. Thompson, T. A. Hylton and A. J. Boucek: Metall. Mater. Trans. A, 2001, 32A, 745760.6. A. Yoshitake, K. Sato, Y. Hosoya, T. Okita, K. Iwase and T. Yokoyama: NKK Tech. Rev., 1998, 79, 2430.7. K. Mirua, S. Takagi, O. Furukimi, T. Obara and S. Tanimura: New steel products and processing for automotive applications, 15; 1996, Detroit, Society of Automotive Engineers.8. K. Miura, S. Takagi, T. Hira, O. Furukimi and S. Tanimura: SAE Trans. J. Mater. Manuf., 1998, 107, 673679.9. N. Mizui, K. Fukui, N. Kojima, M. Yamamoto, Y. Kawaguchi, A. Okamoto and Y. Nakazawa: Steel in automotive application, 3944; 1997, Detroit, Society of Automotive Engineers. 10. W. Bleck and I. Schael: Steel Res., 2000, 71, 173178. 11. B. Yan and K. Xu: Proc. 44th Mech. Working Steel Process. Conf., Orlando, FL, USA, September 2002, Iron and Steel Society, 493508.12. Y. Ojima, Y. Shiroi, Y. Taniguchi and K. Kato: SAE Trans. J. Mater. Manuf., 1998, 107, 687695.13. K. Miura, S. Takagi, O. Furukimi, T. Obara and S. Tanimura: Proc. 29th Int. Symp. on Automotive technology and automation, Florence, Italy, June 1996, Automotive Automation Limited, 7784.14. J. Qu, W. Dabboussi, F. Hassani, J. Nemes and S. Yue: ISIJ Int., 2005, 45, 17411746.15. H. Conrad: J. Iron Steel Inst., 1961, 198, 364375.16. J. D. Campbell and W. G. Ferguson: Philos. Mag., 1970, 2, 6382.17. A. R. Rosenfield and G. T. Hahn: ASM Trans. Q., 1966, 59, 962980.18. S. Xu, R. Bouchard and W. R. Tyson: Metall. Mater. Trans. A, 2004, 35A, 14101414.19. K. Ogawa, H. Kobayashi, F. Sugiyama and K. Horikawa: JSME Int. J., Series A, Solid Mech. Mater. Eng., 2005, 48, 228233.20. V. Schulze and O. Vohringer: Metall. Mater. Trans. A, 2000, 31A, 825830.THE EXPANDING ROLE OF SIMULATION IN FUTURE MANUFACTURINGABSTRACTSimulation technology holds tremendous promise for reducing costs, improving quality, and shortening the time to market for manufactured goods. Unfortunately, this technology still remains largely underutilized by industry today. This paper suggests benefits to industry resulting from the widespread, pervasive implementation of manufacturing simulation technology. Potential simulation impact areas are closely intertwined with strategic manufacturing. Yet, a number of factors currently inhibit the deployment of simulation technology in industry today. The development of new simulation interface standards could help increase the deployment of simulation technology. Interface standards could improve the accessibility of this technology by helping to reduce the expenses associated with acquisition and deployment, minimize model development time and costs, and provide new types of simulation functionality that are not available today.1 BACKGROUNDStrategic manufacturing is a hot topic today. Steve Brown, author of Strategic Manufacturing for Competitive AdvantageTransforming Operations From Shop Floor To Strategy, suggests that it is the next step beyond lean manufacturing. Strategic manufacturing, as defined in Browns text, is viewing production-operations capabilities as a core competence, having a long term view of the business, being fully aware of all market opportunities, planning strategies to outperform competitors by targeting sectors in which it can compete while deliberately avoiding those in which it cannot, and engaging in horizontal and vertical partnerships. He goes on to characterize strategic manufacturing in terms of corporate strategy, manufacturing strategy, product innovations, process technology, quality, materials management, and human resources. Manufacturing strategy “decisions will include investment in technology, expanding into new plants and adding capacity, strategic buyer/supplier relationships, the extent of joint ventures with other firms, the extent of vertical integration, and so on” (Brown 1996).1.1 Benefits of SimulationThe authors of this paper contend that manufacturing simulation should be treated as a key component of strategic manufacturing. What is manufacturing simulation? In The Handbook of Simulation, Jerry Banks defines simulation as:“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.” (Banks 1998)Manufacturing simulation focuses on modeling the behavior of manufacturing organizations, processes, and systems. Organizations, processes and systems include supply chains, as well as people, machines, tools, and information systems. For example, manufacturing simulation can be used to: Model “as-is” and “to-be” manufacturing and support operations from the supply chain level down to the shop floor Evaluate the manufacturability of new product designs Support the development and validation of process data for new products Assist in the engineering of new production systems and processes Evaluate their impact on overall business performance Evaluate resource allocation and scheduling alternatives Analyze layouts and flow of materials within production areas, lines, and workstations Perform capacity planning analyses Determine production and material handling resource requirements Train production and support staff on systems and processes Develop metrics to allow the comparison of predicted performance against “best in class” benchmarks to support continuous improvement of manufacturing operations.Other examples of manufacturing simulation applications include: the modeling and verification of discrete and continuous manufacturing processes (e.g., machining, injection molding, sheet metal forming, semiconductor fabrication), offline programming of robots and other machinery, site selection, layout planning, process and system visualization, ergonomic analysis of manual tasks and work area layout, evaluation of scheduling algorithms and dispatching rules, and business process engineering.Simulation models are built to support decisions regarding investment in new technology, expansion of production capabilities, modeling of supplier relationships, materials management, human resources, and so forth thus simulation supports many of the strategic manufacturing target areas identified earlier in this paper. The authors contend that major long-term benefits could result from the widespread and pervasive implementation of manufacturing simulation technology. What do we mean by widespread and pervasive? The worldwide implementation of office automation software, such as word processors and spreadsheets, certainly fits this characterization. On the other hand, the engineers that have the greatest need for simulation systems use them far less frequently. The implementation of simulation systems within manufacturing could probably best be characterized as limited and sporadic. Our criteria for widespread use would suggest that most manufacturers would have or develop simulation models of their manufacturing operations. Pervasive would mean that simulation tools are used routinely and employed regularly for a broad set of tasks by product designers, manufacturing managers, manufacturing engineers, industrial engineers, process engineers, production planners, quality engineers, cost estimators, programmers, etc. But why is simulation so critical to manufacturing strategy? The answer is simple. Manufacturing systems, processes, and data are growing ever more complex. Product design, manufacturing engineering, and production management decisions often involve the consideration of many interdependent variables - probably too many for the human mind to cope with at one time. These decisions often have a long-term impact on the success or failure of the manufacturing organization. It is extremely risky to make these major decisions based on “gut instinct” alone. Simulation provides a capability to rapidly conduct experiments to predict and evaluate the results of alternative manufacturing decisions. It has often been said that you do not really understand your industrial processes and systems until you try to simulate them. Industry technology leaders in many sectors, e.g., aerospace and automotive manufacturers, are making greater and greater commitment to the use of manufacturing simulation in the various stages of their manufacturing processes (Schrage, 2000).1.2 Recommendations of Industry StudiesThe development of simulation technology and supporting interface standards has been identified repeatedly by industry as a top research priority that promises high payback. One study stated that “Modeling and simulation (M&S) are emerging as key technologies to support manufacturing in the 21st century, and no other technology offers more than a fraction of the potential that M&S does for improving products, perfecting processes, reducing design-to manufacturing cycle time, and reducing product realization costs (IMTR 1998).”The National Research Council (NRC) has repeatedly identified simulation and modeling as a high priority research area. In a 1995 study, the NRC stated: “Ultimately the modeling and simulation capabilities resulting from the research outlined here should be able to support configuring and constructing a real factory for high-level performance (on multiple dimensions), as well as planning how best to operate it once it has been constructed. A concrete demonstration of these capabilities would be the creation of a platform capable of comparing the results of real factory operations with the results of simulated factory operations using information technology applications such as those discussed in this report. For modeling and simulation to serve manufacturing needs, two broad areas of research stand out for special attention: the development of information technology to handle simulation models in a useful and timely manner, and the capture of manufacturing knowledge that must be reflected in models.” (NRC 1995) The NRC also identified simulation and modeling as one of two breakthrough-technologies that will accelerate progress in addressing the grand challenges facing manufacturing in 2020. The study goes on to recommend advancement of “the state of the art by establishing standards for the verification, validation, and accreditation of modeling tools and models (including geometric models, behavioral models, process models, and cost and performance models).Fulfillment of the recommendation would provide fundamental building blocks for the dynamic models and real-time simulations of 2020.” The study recommends research and development in “standards for software compatibility or robust software that does not need standards, methods to make data accessible to everyone (protocols, security, format, interoperability), interactive, 3-D, simulation-based visualizations of complex structures integrating behavioral, organizational, and people issues with other analyses, methods to merge historical data with simulation systems, simulation of alternative business processes.” (NRC 1998). In 1999, the National Research Council completed another study that also identified manufacturing simulation as a priority research area. The report, titled “Defense Manufacturing in 2010 and Beyond: Meeting the Changing Needs of National Defense” recommended that research and development be augmented in four priority areas, one of which is “modeling and simulation-based design tools” (p.3). In a discussion on simulation and modeling (p. 52), the report goes on to state that “Techniques such as variation simulation analysis (VSA) and factory floor layout simulation can improve product performance. Assembly modeling can be used to complement simulations to determine if changing the order of steps in the assembly of a complex product can lead to labor savings and reduce variation. Combining three-dimensional product modeling with simulation techniques can help determine the cost of alternative manufacturing processes (NRC 1999). ”1.3 NIST Manufacturing Simulation and Visualization ProgramStandard interfaces could help reduce the costs associated with simulation data exchange and model construction -and thus could make simulation technology more affordable and accessible to a wide range of potential industrial users. In 1999, the National Institute of Standards and Technology (NIST) established a program in Manufacturing Simulation and Visualization (MS&V). The program is focused on the development of data interfaces and test methods for integrating manufacturing simulation and visualization applications to improve the accessibility and interoperability of this technology for U.S. industry. The technical approach of the program is to: (1) identify critical manufacturing process and system simulation domains and associated types of simulation software applications, (2) analyze current and future trends for simulation and testing technology, (3) establish specification and testing methods, models, and metrics for validating simulation systems interfaces, (4) identify tools and models to be used in the specification development, prototyping, and testing processes, (5) construct a test bed containing simulation applications, prototype integration, testing tools, and test cases, (6) specify and develop architectures, data models, and interface specifications for integrating simulation applications, component modules, and reference libraries, (7) conduct experimental tests, industry demonstrations, and reviews to substantiate the validation and testing process itself, and (8) promote specifications as candidate standards within the national and international standards community.1.4 The IMS MISSION ProjectAs a part of the MS&V Program, NIST is participating in and serving as the U.S. Regional Coordinator for an international collaboration in modeling and simulation. The collaboration is the Intelligent Manufacturing Systems (IMS, 2001) MISSION Project. The goal of MISSION is “to integrate and utilise new, knowledge-aware technologies of distributed persistent data management, as well as conventional methods and tools, in various enterprise domains, to meet the needs of globally distributed enterprise modelling and simulation. This will make available methodologies and tools to support the definition of appropriate manufacturing strategies and the design of appropriate organizations and business processes. This goal will be achieved by establishing a modelling platform incorporating engineering knowledge and project information which supports space-wise and control-wise design, evaluation and implementation over the complete enterprise life cycle. This will be the foundation stone for an architecture to support engineering co-operation across the value chain of the entire extended enterprise.”(MISSION, 2001) Partners in MISSION include manufacturers, software vendors, academic and other research institutions, as well as government agencies in the United States, Japan, and Europe. Within the project, NIST is also responsible for the leadership of the Work Package 2 effort. Work Package 2 focuses on system architectures, data modeling, and interface-specification development. The other three technical work packages address: industrial requirements, simulation reference models and template libraries, and test case scenarios. Interface specification activities described in this paper were initiated under the MISSION Project.One of the major focus areas for Work Package 2 is the development of architectures and interfaces for distributed manufacturing simulation. What is included within the scope of distributed manufacturing simulation? Multiple simulation software processes that are independently executing and interacting with each other. Simulation systems may have been developed by different software vendors. Modules may run on different computer systems in geographically dispersed locations. Distributed computing environments where other non-simulation manufacturing software applications are running and interacting with one or more simulation systems. Engineering systems may interact with simulation systems through service requests. Distributed manufacturing simulation systems that are comprised of multiple functional modules that together form a system, such as model builders, simulation engines, display systems, analysis tools, etc.Why do we need to build distributed manufacturing simulation systems? Some reasons include: modeling of supply chains across multiple organizations where some information from each organization may be hidden from others, modeling multiple levels of manufacturing systems, providing capabilities that do not exist in a single simulator, hiding proprietary information about the internal workings of a simulation, creating low-cost run-time simulation models, taking advantage of computing power afforded by distributing execution, providing simultaneous access to models for users in different locations, and providing different numbers and types of licenses for different simulation activities (model building, visualization, execution, analysis).2 FACTORS INHIBITING THE USE OF MANUFACTURING SIMULATION2.1.1 Costs of Simulation TechnologyOne might argue that cost is the primary factor affecting widespread and pervasive use of manufacturing simulation technology. Although there are a number of issues, they could perhaps all be reduced down to a cost factor. This leads one to ask - Is simulation technology affordable? The answer - It depends upon the user. Some factors affecting an individual companys view of simulation affordability may include: the companys resources: availability of discretionary funds simulation skills and experience base of current staff or consultants (Rohrer and Banks 1998) existing information systems infrastructure (availability of required computer systems, related software applications, and databases) scope and complexity of the target simulation application area availability of turnkey or readily-adaptable simulation models and solutions availability and format of input data cost and risks of implementing manufacturing systems without the use of simulationLicensing costs for some of the high-end simulation software packages are often viewed as a major factor. Commercial manufacturing simulation software packages range in price from about $500 to over $50,000 a seat. For more information on product availability and licensing costs, see (IIE Solutions 1999). In fact, these costs are only “the tip of the iceberg.” A more complete picture of the cost factors in the deployment of simulation technology includes: Computing hardware and peripheral devices Initial software licenses, options (plug-ins, translators, analysis tools), and maintenance upgrades Salaries of manufacturing domain experts, simulation specialists, consultants, and support staff Training classes, learning curves, and maintaining proficiency Requirements analysis and data acquisition Translation of existing company data Systems integration with other related manufacturing software applications and/or databases Development, maintenance, and configuration management of simulation models.It is important to note that these costs must be weighed against the risks of not using simulation technology. There are countless case studies where companies have either realized significant cost savings or avoided major disasters through the effective use of simulation. Undoubtedly, if complex manufacturing systems are involved, simulation is probably the only reliable mechanism for predicting andevaluating the performance of the system under varying loads and operating conditions.2.1.2 Data Interface ProblemsAmong the cost factors identified above, the “interoperability problem” is particularly significant. Interoperability includes a number of system integration, data translation, and model development issues. What is the nature of this problem?1.Interoperability between other manufacturing software applications and simulation is currently extremely limited. By other applications, we mean product design, manufacturing engineering, and production management. The simulation software used to model and predict the behavior of manufacturing systems do not use the same data formats as the systems used to design products, engineer production systems, and manage production operations. Neutral interface specifications that would permit quick and easy integration of commercial off-the-shelf software do not exist. The only qualification that needs to be added is that most simulation systems have a capability for importing some form of two or three dimensional graphics data. Considerable manual intervention often required to make effective use of the graphics data within the simulation.2.The cost of transferring data between simulation and other manufacturing oftware applications is often very high. Users must either re-enter data when they use different software applications or pay high costs to system integrators for custom solutions. In some cases, it may not be possible to integrate “closed systems” with simulation. By closed systems, we mean those with undocumented, proprietary data file formats.3.The simulation model development process is labor intensive. Vendors and industrial users alike have recognized that the development and maintenance of models of their production systems and resources is very costly. For example, the development of a detailed simulation model of a single machine tool may take an engineer 4 to 6 weeks. Models must now be custom developed for each simulation software package. Each industrial user must build his or her models of manufacturing systems, processes, and resources. This is true even if the models represent commercial off-the-shelf manufacturing equipment. If the industrial user has several different vendors simulation packages, unique models must typically be reconstructed for each package. The models developed for one simulation system are of little or no use to another. The simulation development process is very much an adhoc process. Texts provide high level guidelines, but model development is perhaps more of an art than a science.微观结构和化学成份对高强度钢力学性能的影响J. Qu, W. Dabboussi, J. Nemes and S. Yue通过Hopkinson 装置的剪切冲孔模式对高应变速率性能的高强度钢的各种微观组织和静强度水平进行实验，得到了不同热处理情况下冷轧薄板钢的几种微观结构，铁素体和珠光体（F+ P）,铁素体和贝氏体（F+B），铁素体和马氏体（F+M）,铁素体.贝氏体和残余奥氏体（F+B+RA）。根据静力强度的变化（静力强度比值的变化）,被分成双相（铁素体加马氏体）和多相（包括铁素体，珠光体，铁素体贝氏体等）两种。有观点认为，以马氏体和铁素体为主的化学成分对微观结构的力学性能有较大的影响。关键词：双相(DP)钢; 相变诱导塑性(TRIP)钢; 霍普金森（Hopkinson）装置; 剪切冲孔试验; 力学性能; 引言高强度钢被广泛用于汽车车身结构，在提高了汽车的耐撞性的同时没有增加汽车的重量。目前双相（DP）钢和相变诱导塑性（TRIP）钢越来越被人们所认识，因为相比传统的高强度低合金钢（HSLA），它们兼具高强度和高延伸性能。双相钢是铁素体为主体与少量的马氏体相结合而成，有良好延展性的铁素体和坚硬的马氏体两者的结合，使混合物的特征有点像颗粒增强复合材料，表现出很好的柔性和高加工硬化速度。相变诱导塑性钢包含铁素体，贝氏体和残余奥氏体，有相对较高的延展性，这种类型的钢铁是通过使残余奥氏体在塑性变形作用下诱发马氏体形成。高强度低合金钢是低碳钢添加微量铌，钛，和/或钒，利用不同的压力装置对材料微合金化和热加工，也使材料达到超轻量化设计要求。通过动态强度和吸收能量的量化参数来了解耐撞金属的变形性能，通常是采用霍普金森（Hopkinson）装置测试或落锤破碎测试，应变敏感度通过应变灵敏系数确定，即物质动强度对静强度的变化率。尽管和人们生活中普遍认为的动态因素降低静强度增大相矛盾，但是，微观结构的一些力学性能与静强度之间的关系也没有明确的说明，而且化学成分的影响也没有深入的研究。这次实验要先确定影响应变敏感度的三种钢微观结构的主要成分，采用的三种钢是双向（DP），相变诱导塑性（TRIP）钢和高强度低合金（HSLA）钢。对他们进行不同的热处理，来获得不同静强度类型的微观结构。然后使用分离式霍普金森（Hopkinson）装置，这些材料进行剪切冲孔试验，得到不同的微观结构，然后比较不同微型结构的钢材的化学性质对动态因素的影响。实验材料本实验使用的材料为三个厚度在1.7毫米的钢冷轧薄板，双向钢（DP）,相变诱导塑性钢（TRIP）和高强度低合金钢（HSLA）的化学成份分别列于表1。钢A是含有一定量锰外加少量钼，以促进形成的双相组织。钢B含有较多的铝，以形成残余奥氏体。钢C是加入微量的铌，从而加强晶粒细化和沉淀硬化。所有这些钢热处理后产生不同类型的微观结构，铁素体+马氏体（F+M）,铁素体+珠光体（F+P）,铁素体+贝氏体（F+B）,铁素体+贝氏体+残余奥氏体（F+B+RA），如表1 。表1 三种高强度钢的化学成分, 质量百分比材料 C Mn Si Others 显微结构A 0.07 1.84 0.09 Mo: 0.15 F+M, F+P, F+BB 0.16 1.57 0.017 Al：2.0 F+B+RA, F+P, F+MC 0.06 0.61 0.10 Nb: 0.019 F+P, F+B, F+M1 不同热处理方案的原理图热处理小正方形试样尺寸为30 * 30毫米，热处理后，进行显微检测及静态和动态剪切冲压测试。图1是为获得不同微观结构决定的三种热处理时间表。附表1 ，退火后在不同温度（TA）的不同时间（tA）的标本，分别通过水冷却（WQ），强迫空气冷却（FAC），空气冷却（AC），或非常低速率冷却（VLC，即0.5或1.5 C/s）冷却到室温。附表2是一个分级热处理，一是退火后在贝氏体区域进行了恒温处理（TB, tB），为钢铁B，产生铁素体+贝氏体+残余奥氏体微观结构。附表3是铁素体马氏体高温退火（TH, tH）然后以非常低的速度冷却（0.5C/s ）到室温，再次退火和水淬。表2列出了三种钢热处理的详细的参数。表2 各种热处理的详细参数附表1 TA,C tA, min 冷却方式 显微结构Steel A750/775/800 0.5/1/2/5/10/20 WQ F+M725/750/775/800/825 2 FAC F+B825/850 1/2 ACF+P850 2 0.5uC /s F+PSteel B 800/850 5 WQ F+M850/1000 5 0.5uC/sF+PSteel C 750 2 WQ F+M950 2 FAC F+B1000 2 AC F+B700/800/900 2 AC F+P900 2 1.5uC /sF+P附表 2 TA,C tA,min TB,C tB,min 显微结构Steel B850 5 400 2/5/10 F+B+RA附表 3 TH,C tH, min TA,C tA,min 显微结构Steel A 850/900/950/1000 2 750 2 F+MSteel B 900/950/1000 5 800 5 F+MSteel C900/950/1000 2 750 2 F+M1 不同热处理方案的原理图要了解各种材料热处理后的微观结构，采用腐蚀溶液测定的方法，所选用的腐蚀溶液有两种，一是含10亚硫酸钠（Na2S2O5）的硝酸酒精溶液，还有就是LePera腐蚀剂。用硝酸酒精溶液腐蚀的铁素体为灰色，贝氏体或马氏体为黑色，和残余奥氏体为白色。用LePera腐蚀剂，马氏体或残余奥氏体出现白色，贝氏体出现黑色，铁素体出现灰色。 静态/动态剪切冲孔试验静态剪切冲孔试验由一台MTS冲压机和一个特别的冲压模具组成。冲孔模具上孔的直径为9.4和9.55毫米，冲孔时速度为0.01毫米/秒。图2是Hopkinson 装置的动态剪切冲压模式的示意图。此装置采用压缩空气推动，以非常高的速度执行操作获得数据，实验时将样品放于冲压机和模具之间，进行操作，然后示波器将与模具和冲压杆相关联的应变片提供的信号转化为图形，再对图形进行整理后得到数据就是所要的负载大小和位移变化的数据。重复同样的过程，对有相同直径为9.4和9.55毫米的材料进行静态剪切冲压试验，冲压速度为25米/秒。2 剪切冲孔实验中霍普金森（Hopkinson）装置的结构图结果和讨论微观结构图3a和b是钢A通过退火和水淬后得到的铁素体加马氏体显微组织。图3c和d是钢A退火后缓慢冷却和空气冷却得到铁素体加珠光体（图3c）和铁素体加贝氏体（图3d）的微观结构。图4a和b是钢C不同温度下退火得到的铁素体加珠光体。图4c和d是钢C 在1000 C下高温退火后分别空气冷却和退火水淬，得到的贝氏体加铁素体结构（图4c）和铁素体加马氏体结构（图4d）。图5是钢B的不同微观结构，铁素体加珠光体（图5a ）和铁素体加马氏体（图5b）微观结构分别是退火后以非常低的利率冷却和水冷却产生的。TRIP钢的显微结构包含贝氏体铁素体和残余奥氏体（图5c和d），他们是通过贝氏体退火获得。6 静态/动态剪切冲孔试验下力位移曲线图 7 钢A的微观结构与力学性能的关系图典型的剪切冲孔曲线图6是静态/动态剪切冲孔试验下力位移曲线图。可以看出，当力的变化大于临界值时，只要很少的力就能产生很大的位移，这与剪切冲孔试验中的力位移曲线和拉伸应力应变曲线有些相似，例如：当加载超过屈服点时，产生塑性变形，达到最大负载后断裂。在先前的研究中，发现最大剪切应力得最大值（Fmax图6）和抗拉强度的极限（UTS）呈线性关系，由于在所有情况下剪切冲压变形速度是相同的，可以采取最大剪切力为标准比较不同标本的抗拉强度。 微观结构的影响图7是钢A的微观结构与力学性能的关系图，表现出的总趋势是力学性能的下降将带来静强度的增加，似乎有两个不同的类型的标本，在图形中以椭圆形式标出，马氏体加铁素体组织和多相（包括铁素体珠光体和铁素体贝氏体）组织，一般来说，在同一静强度下，铁素体加马氏体组织似乎比其他任何结构的研究有更高价值的力学性能。 一般静强度下力学性能的变化可以解释为塑性变形的热变形理论。应力变化可以定为两部分，热元件th ，由于短距离的障碍引起主要和应变率.温度有关，非热元件ath ，由于长距离障碍引起而主要和材料的结构有关， 即 = th + ath （ 1 ）因为室温下，在静应变速率范围内，热应力很小的变化就会引起整体压力的变化。所以压力R的力学性能可以表示为R = dynamic / static = （ath （动态） + th ） / ath （静态） （ 2 ）温度上升影响非热应力的变化，这说明了剪切模量对温度的依赖性。假设ath （动态）= ath （静态），则上述方程变成R = 1 + th / ath （ 3 ）可以看出，上述方程在给定的应变速率，通过增加静强度，引起力学性能的降低ath的增加，基本的原因，为什么静强度的增加会导致力学性能的降低，经过简单的分析，不难看出非热应力不会直接影响热应力。 双相钢是由奥氏体向马氏体转变而来的，有高流动性能的材料。这种结构可起热屏障作用，从而增加热应力，这也是双相钢相对于其他微观结构钢具有更高的力学性能的原因。8 不同材料和微观结构与力学性能的相互关系化学性质的影响图8是不同材料和微观结构与力学性能的相互关系。可以看出，材料的力学性能主要取决于微结构而不是化学成份。同样，如果钢C热处理后得到铁素体和马氏体组织，它将属于微观结构研究的范围。因此从这些结果，似乎并不能看出，化学对材料力学性能的影响并没有微结构的影响强。在许多资料里都能找到，F+B+RA（相变诱导塑性钢TRIP的微型结构）与F+M类型的微观结构相比有较低的力学性能与静强度的水平，这是因为变形产生的热量使力学实验时残余奥氏体的稳定性增加，从而降低了奥氏体向马氏体转变的难度。 有一个例外的变化是铁素体+马氏体的钢B（相变诱导塑性钢TRIP的化学性质）所示，在静强度下观察发现其中表现出非常高的力学性能，这可能是因为固溶体增加热应力，因为钢铁B相对钢A和钢C含有更高的合金化，这也可能是为什么相变诱导塑性钢TRIP显微组织比钢B的不同之处。溶质原子分为两种，长距离运动和短距离错位运动，而长距离运动时的障碍由晶界，位错，沉淀物或其他缺陷引起，在短距离的障碍可以依赖应变速率和温度克服。随着应变速率的提高，只有很少的时间用来克服这些障碍和热能，同时还提高了热应力。这也许就是不同的因素影响不同的结果，使材料产生不同的静态和动态现象。 微观结构和化学成份对钢力学性能的影响说明，固溶强化技术也许应该更多地被运用在设计防撞钢上。耐撞钢材的研究，以固溶技术为基础结合双相组织，可能会被用于以后发展汽车用钢。虽然本文的重点是关于钢的强度，但是韧性也是汽车用钢的一个关键因素。许多组织已经对不同的成形方法进行了研究，特别是米斯拉（Misra）等几个组织的研究，对微合金钢成形中加铌-钛和钒-铌。另一方面，在吸收能量的基本上结合强度，韧性和加工硬化率进行研究。因此，要想知道高延伸性对能吸收更多的能量，还有更多的工作需要理解研究，如延展性方面对力学性能的影响。 结论冷轧高强度钢在不同的退火和冷却条件，产生各种不同的微观组织结构，随后对其力学性能的测量，静态和动态剪切冲孔试验，结果发现，静强度的变化受微观结构的影响。能动因素的降低引起静强度的增加，而且铁素体和马氏体似乎比其他微观结构具有更优秀的力学性能。同时还发现，铁素体和马氏体结构为主的相变诱导塑性钢（TRIP）混合物拥有高的力学性能值，一般的化学成分对力学性能的变化影响并不大。从这些研究上，提出一个新的关于防撞钢设计的建议：结合双相钢和固溶体技术对其进行发展研究。a 750/2 min WQ (F+22%M); b 775/2 min WQ (F+40%M); c 850/2 min 0.5/ s (F+P); d 800/2 min FAC (F+B) 3 金属A在不同热处理情况下的显微结构a 800/2 min AC (F+P); b 900/2 min 1.5 /s (F+P); c 1000/2 min AC (F+B); d 750/2 min WQ (F+M)4 金属C在不同热处理情况下的显微结构 a 850/5 min 0.5/s (F+P); b 800/5 min WQ (F+M); c 850/5 min+400/2 min WQ (F+B+RA, 7%RA); d 850/min+400/10 min WQ (F+B+RA, 3%RA)5 金属B在不同热处理情况下的显微结构仿真在今后的制造业中的作用不断扩大摘要仿真技术具有很大的前景，有效的降低成本，提高质量，缩短产品上市时间。不幸的是，这一技术到目前为止仍然没有得到充分使用。本文提出的普遍实施制造业仿真技术，将给行业带来广泛的利益。仿真领域的潜在影响与战略制造密切相关。然而，目前一些外在因素正阻碍着当今业
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