Integration of forming simulation in the digital manufacturing process chain

Transcription

1 Integration of forming simulation in the digital manufacturing process chain Prof. Dr. Karl Roll Daimler AG; Materials and Process Engineering, Sindelfingen, Germany 1. Introduction The industrial forming simulation used today is the result of a development process during the last two decades. A main focus of this process was the continuous optimization to characterize the material properties. The presentation will give an overview of the simulation technology challenges arising from the need to improve bridging of the gaps between material data and computational technology. Furthermore, an integrated technology pathway from the perspective of simulation requirements, material characterization needs, simulation technology innovations, and impacts of those requirements on the accuracy of practical applications in the automotive world is discussed. Based on the Digital Factory project at Daimler, now we are integrating the forming simulation in our digital planning, manufacturing and review process of sheet metal part production. Particularly during the process of sheet-metal part production, planning times for press plants and tools can be drastically reduced by the introduction of digital planning and simulation methods. This applies to the application of standards, full data integration, defined planning processes (workflows) and the automation of planning tasks. Furthermore, planning quality is increased as well as the maturity of the planning results due to consistent digital review of each individual process step by means of simulation. In addition, effective areas and the benefit potential of the digital techniques with regard to the planning process can be identified. This would show how the implementation of the vision of a "virtual press shop" can help to resolve the challenges faced in the planning of systems and tools for manufacturing press parts. 2. Sheet metal forming simulation State of the Art Until a few years ago, the design of metal-forming tools was mostly based on knowledge gained through experience, and designing the optimum tool often required a protracted and expensive trial and error process. Today, even in the early design phases, simulations of sheet-metal parts forming processes are performed using finite element methods. The most important goals in this process are verification of the manufacturability of the sheet-metal parts and obtaining vital information on optimum tool design. Today, simulations in the automobile industry are as a rule restricted to the sheet-metal forming process, which is where the greatest changes in shape take place. For a typical sheet-metal body in the course of fabrication, the first forming step is the deep drawing process. Many commercial finite element programs are today capable of reliably forecasting possible crack and tear formation and the sheet thickness and form changes of conventional sheet-metal materials. In the past ten years, a number of studies have been made of sheet-metal forming process simulation [1,2,3], based on a very broad range of approaches, where, in addition to implicit and explicit processes, what are known as one-step procedures (often also known as inverse procedures) are also on the market. Different metal-forming simulation programs are used, depending on the chronological position of the operation in the production process. They differ primarily not only in terms of computing time and user friendliness, but also in the theories underlying them and in the quality of the results. As a rule, it is inverse, explicit, and implicit simulation procedures that are used. Today, sheet-metal forming simulation is used as follows [4]: Inverse programs are used to gain a rapid overview and a rough verification of the manufacturability, and for pre-optimization. Depending on the formed sheet-metal part, both implicit finite element programs with membrane or shell elements and explicit programs with shell elements are used to optimize the tool and the process. The use of sheet-metal forming simulation is no longer restricted to verification of manufacturability, although this continues to be the most important result, and simulation is increasingly used to optimize the first forming stage. Up to 30 computing runs are made for complex body parts, with the formed metal parts being optimized to counteract two failure phenomena: tearing and wrinkle formation. In exceptional cases, distribution of sheet thickness is improved. It should be noted that large savings have already been achieved by the introduction of process simulation in

2 designing shaped sheet-metal parts. These savings are conferred mainly by the more rapid development of tools and by a dramatic shortening of the trial and error process for series production tools. The whole process of laying out the forming method and the designing and producing sheet-metal tools can be abbreviated, while development time is similarly shortened. In recent years, tool development and production time has been reduced by about 50% by simulation, and a further 30% reduction over the next few years appears realistic. The simulation of forming has today reached a stage where its results can be fed directly into the press tool digital planning and validation process. Thus today, starting from the design model, through a number of process steps as far as the actual design of the press tool, already computer-assisted, the production of a component in the line of presses can be simulated before a first prototype is built. Figure 1 show as an example the springback simulation of a sidewall. Figure 1: Springback simulation of a sidewall Springback between 0 and 5.5 mm When we compare the physical reality of a sheet-metal forming process with the simulation models used today, we recognize that significant factors in the forming process are incorrectly described in the simulation model, or even not at all [4]. Table 1 gives an overview of the reality and of simulation models in use today. Approximation of the simulation model is sufficient to yield good forming simulation results. However, for a more accurate calculation of springback, the model must be amplified at various points. To date, the effects of the forming process, tool surface quality, and temperature have not been specified in detail. In the numerical computation of springback-related form changes using sheet-metal forming simulation, numerical factors as well as geometrical, materials, and technological characteristics affect the calculation results. For example, the material model used and the hardening rule for describing metal behavior, the finite elements used to describe the deformable sheet, or the friction model used all have great influence. Numerical values, such as the time discretizing chosen, also have an effect. Factor Reality Simulation Production stroke rate Not constant Not in the model Machine Elastic Not in the model Tool Elastic Rigid Characteristics of the direction of draw Not constant Not mapped Coefficient of friction Not constant Constant Temperature Not constant Not in the model Topology of blank holder surface Not constant Not in the model Material Complex Simple models Material characteristics Not constant (Not) constant Table 1: Comparison of machine, tool, and material characteristics in reality and in the simulation model 3. Simulation chain of the production process of sheet metal parts Nowadays numerical feasibility studies of the first forming operation are already performed in early design phases of the sheet metal forming parts, the finite element analysis is mainly used. However, to get statements about all difficulties during the manufacturing process and especially the final part geometry the complete forming process has to be analyzed. The simulation of a complete process chain of a real car part including deep drawing, trimming, flanging, hemming processes and springback is presented in figure 2 [5]. In order to simulate the whole process chain of the production process of a sheet metal part we still have to solve some

3 problems: Each operating step (drawing, cutting, hemming etc.) needs its "own FE - Mesh Data must be mapped between the FE - Meshes statically and kinematically compatibly Simulating the assembly process parts the different FE - Meshes of the parts must be brought by best fit into the correct position. How can we handle the springback of the assembled part? In which operation step do we have to compensate the springback of the assembled part? How can we simulate the influence of the adhesive bonding in the hemming operation? Model Setup CAD Meshing Tools Forming Gravity Holding Stamping Line Dies Trimming Flanging Figure 2: Simulation chain of the production process of sheet metal parts Hemming Springback There is some progress, mainly in the hemming process [6], but most of the problems are not solved. Today it is not possible to simulate the whole process chain of the production of a sheet metal part in an industrial enviroment. 4. Simulation of the final Properties of a Sheet Metal Part In the area of production planning nowadays all processes for manufacturing sheet metal parts are calculated upfront using efficient simulation software before actually producing the part. Thus, statements about the producibility of certain components for future parts using certain materials can be done in an early design stage. In addition, tool geometries can be optimized in time. Besides feasibility studies, basically processes that lead to changes in the material structure and for this reason to changes in the material properties are of special interest. So far the simulation calculations of each single process were usually done independently. A possible influence of a previous step in the process chain on the following step was not considered with this approach. Depending on the material examined the deformation history is, however, of crucial importance for the accuracy of the simulation results. Therefore, great efforts are taken to close up the digital processing chain for manufacturing sheet metal parts holistically. The digital process chain for manufacturing sheet metal parts can be roughly divided into the simulation packages casting, rolling, annealing, forming, joining, durability analysis respectively crash. At present each individual component (module) is been worked on with the objective of closing the process chain in reference to calculating the component properties. Figure 3 shows a schematic overview of the future simulation environment. To simulate all steps in this enviroment we have to solve some problems: Each simulation method (static, crash, etc. ) needs its "own" FE - Program (exception Forming - Crash with explicit codes) Each simulation method (static, crash, etc. ) needs its own FE - Mesh

4 Each simulation method has "own" material and failure models Data must be mapped between the programs statically and kinematically compatibly. Today it is only possible to interpolate Scalars such as thickness, plastic strain, etc. Which influence does the "damage" have by the mapping process on the characteristics of the component? With the extrapolation of tensors still many questions are open (e.g. can an equilibrium be achieved with mapping?) ΔT Rolling Walzen Annealing Glühen Mapping Umformen Forming Rückfederung Springback Kompensation Compensation Joining Fügen Mapping Crash-simulation - Beurteilung texture des Gefüges grain size Anisotropie strength in Dickenrichtung transformation Oberflächenqualität ductility etc. Beurteilung yield locus der Herstellbarkeit hardening Anisotropie phase transformation in der Ebene damage Umformbarkeitetc. accuracy Beurteilung depending on des Gefüges joiningtechnique Beurteilung thermal wakening der Wärmeeinflusszone mechanical hardening Welding distortion Energyconsumption Energieaufnahme Intrusion Entwicklung Stiffness Bauteilgeometrie Fatigue Div. Anforderungen aus Crash/Festigkeit/ Steifigkeit Möglichst One universales, description durchgängiges of the material Materialmodell for all simulations oder größtmö gliche if possible Kompatibilität! Figure 3: Schematic overview of the future simulation environment Nowadays, FE-simulations for calculating the crash behaviour of component parts are mainly built up using CAD data that only contain geometrical information on the component or component assembly neglecting the deformation history. In the past years so called mapping algorithms were developed, which allowed to transmit certain parameters (element thickness, strain) from the results of the forming simulations to the starting mesh of the crash simulation. This poses a big challenge because the FE-meshes used for forming simulations, which are done for each part separately, are much finer than the FE-meshes used for crash simulations, which are done for a whole vehicle. Thus, a wise mapping algorithm has to be able to transform result parameters obtained from many fine forming elements to a rougher element of the mesh used for crash simulation. Such a method is already being used for certain crash calculations but it does not represent the common approach. With increasing requirements on crashworthiness, and light-weight car body structures being a central issue in future automotive development, the use of high strength steel qualities has become wide-spread in modern cars. Since these materials often show significantly lower ductility than conventional steels, it is of great importance to precisely predict failure under crash loading conditions. Hence constitutive models in crashworthiness applications need to be initialized with correctly determined internal variables mapped from a corresponding sheet metal forming simulation. Here two principle ways could be used theoretically: On the one hand different understanding of damage and failure in crashworthiness and sheet metal forming applications may be unified by a generalized incremental stress state dependent damage model. This approach can be considered as an attempt to replace the currently used Forming Limit Diagram (FLD) for the failure description in forming simulations. Furthermore, an advantage would be the inherent ability to account for load-path dependent failure behavior. On the other hand the already applied Gurson model in crash simulations may be fed by an estimation of the internal damage value from the forming simulation. The idea here would be to perform the forming simulation with a state-of-the-art anisotropic material model like e.g. the Barlat model, with a simultaneously executed estimation of Gurson s damage evolution law. First results are shown in Figure 4 [7]. The described possibilities for determination and transfer of local pre-damage data from forming to crash simulations, show a promising potential to make crack prediction in crash simulations more accurate in the future. Both options proposed, for the combination of a material model for forming simulations (like Barlat89), with a crash damage model, can be improved by implementing the described extensions to the damage models.

5 As some unintended, but very welcome side-effect, the damage models also show promising results in predicting ductile failure in forming simulations. The use of these damage models could therefore also lead to an improved failure prediction in forming simulations. Linear accumulation GISSMO n=2 Real failed part Failure spot predicted correctly at the same position Less damage predicted in areas that are not close to failure Figure 4: Prediction of Failure using Barlat-Model with a Generalized Incremental Stress State Dependent Damage Model 5. Vision of the digital factory To facilitate fulfilment of the described requirements in future, Daimler is implementing a planning process supported by digital methods across all productive manufacturing stations. In this context, the Daimler vision for the digital factory, which is set to be implemented in a strategic production engineering project, no production facility will be designed, constructed, commissioned or operated without a (full) review being first carried out using digital planning methods. The review (see Figure 5) is to cover the entire factory layout and buildings as well as individual manufacturing stations, facilities and production lines through to the tools, operational steps and technical operations such as forming, welding and adhesive bonding, in accordance with an extensive multilevel information pyramid. The scope of manual workloads is also taken into consideration, particularly the interaction of workers with equipment and machines with regard to ergonomic aspects such as physical effort. Viewed in isolation with regard to individual levels, this vision may not seem so different, as various digital planning and simulation methods have been used to support individual process stages in the past. However, it represents a new challenge if the entire system including all the individual aspects is to be linked in the form of continuous workflows and relevant digital tools with access to a central data management system. 5.1 Main Approaches for the Digital Factory From our point of view, the digital planning methods for a digital factory will need to be based on the following four main approaches (see Figure 4) and it will be necessary to further develop these approaches consistently in order to realize the potential time and cost savings, referred to above, in the process as a whole [8]. In future, data integration will be necessary to replace the wide variety of individual databases currently used with a few data management systems. Each data record must only be recorded and saved once and the supplier of the data remains responsible for updating it. This applies to all data, including 3D product and factory data, tool and equipment data records, process and production plans and simulation results. This will call for certain obligatory changes in the approaches and working methods of developers and planners. This means that in the

6 future, it will be necessary to save incomplete data and interim versions on the system and not just complete, reviewed results. In addition, all the information must be available worldwide. Furthermore, processes must be defined and integrated in the form of workflows so that the current sequential working methods can be replaced by a form of meshed cooperation including revision management. The automation of repetitive routine planning tasks will relieve the workload on planners and ensure further benefits. Not a single production facility will be planned be built/ go into operation be working without having been completely supported by digital planning methods Focus : technical processes facility components quality costs capacities Figure 5: Vision of the digital factory Building Facility Line Cell Operation Specific Process Worker The duration of production engineering work on press plants and tools for sheet metal part production can be significantly reduced by using digital planning methods focussing on standardization, data integration, workflows and automation. In addition, the consistent digital verification of all process steps can significantly improve planning quality. These improvements result in a steady increase in the maturity of planning results and tool technology from the initial design through to commissioning. This shows how the digital factory will have a key role to play in overcoming the challenges posed and the introduction of digital planning methods will mean drastic changes in working practices. These developments will be driven by the need to design buildings, plants and tools for the production of pressed parts for a wide variety of new, attractive models as efficiently as possible. 6. References [1] Proceedings of the 5th International Conference, Numisheet 2002, ed. Yang, D. Y., Oh, S. I., Huh, H., Kim, Y. H., [2] Proceedings of the 6th International Conference, Numisheet 2005, ed. Smith, L. M., Pourboghrat, F., Yoon, J-W., Stoughton, T. B., [3] Roll, K.: Advanced Simulation Techniques Exceeding Reality? Proceedings, Materials & Technology Conference, September 16 20, Detroit, [4] Roll, K.: Simulation of Sheet Metal Forming Necessary Developments in the Future. Proceedings 7th Numisheet - Conference 2008, pp [5] Rohleder, M.; Roll, K.: Springback Prediction on a Ring taken form a Deep Drawn Cup; Proceedings of the 9th International Conference on Sheet Metal, pp , [6] Le Maout, N.; Thuillier, S.; Pauvert, O.: Numerical Simulation of the Hemming Process - Iintegration in the Virtual Design of Automotive Parts. Proceedings 7th Numisheet - Conference 2008, pp [7] Neukamm, F.; Feucht, M.; Haufe, A.; K. Roll, K.: A Generalized Incremental Stress State Dependent Damage Model for Forming and Crashworthiness Simulations. Proceedings 7th Numisheet - Conference 2008, pp [8] Schiller, E.; Roll, K.; Wöhlke, G.; Wiegand, K.: Digital manufacturing in press part production. International Sheet Metal Review, Nov./Dec. 2001; pp