This article addresses process, stamping, and manufacturing engineers, as well as tool designers (prototype and series production tools), and press shop planners in the range of metal forming. The paper deals with methods of modelling and simulating the metal forming process and their application in product design, production, and forming process planning. In models usually applied major effects on the forming process are neglected. For instance, the elastic behaviour of presses and die tools is not considered in process and tool planning. Thus, reworking of tools is a consequence of this model oversimplification. The paper illustrates how interactions between forming press, die tool and metal forming processes can be modelled by enhancing conventional FE models. Several examples demonstrate the information value of the Advanced Forming Process Model (AFPM).Keywords: simulation of forming process, digital simulation of behaviour, virtual press, advanced forming process model, AFPM

This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

Deep drawing of paperboard with rigid tools and immediate compression has only a small presence in the market for secondary packaging solutions due to a lack of understanding of the physical relations that occur during the forming process. As with other processes that deal with interactions between two solids in contact, the control of the factors that affect friction is important due to friction’s impact on runnability and process reliability. A new friction measurement device was developed to evaluate the factors influencing the friction behavior of paperboard such as under the specific conditions of the deep drawing process, which differ from the standard friction testing methods. The tribocharging of the contacting surfaces, generated during sliding friction, was determined to be a major influence on the dynamic coefficient of friction between paperboard and metal. The same effect could be examined during the deep drawing process. With increased contact temperature due to the heating of the tools, the coefficient of friction decreased significantly, but it remained constant after reaching a certain charging state after several repetitions. Consequently, to avoid ruptures of the wall during the forming process, tools that are in contact with the paperboard should be heated.

The scientific paper proposes a method to analyze and optimize the drawing press and the deep drawing process with a virtual model. It focuses on hydraulic multi-point die cushions since they offer a wide range of possible adjustments and a high potential to increase production quality and efficiency. The operator can apply individual die cushion cylinder forces to the blankholder which enables the control of the forming process and manual die spotting can theoretically be reduced to a minimum. Utilizing the virtual model can improve the drawing press’ behavior and significantly reduce the set-up time for drawing presses during try-out of new tools or when tool sets are transferred to a different machine. The paper presents a system simulation model of a deep drawing press including its mechanics, hydraulics, and control. It serves as a basis for developing an advanced control system which improves system performance with potentially higher slide speeds, and therefore, a more efficient production. Another aspect in the paper is the development of a coupled simulation consisting of the machine model and a process model. This includes the elasto-static as well as the dynamic behavior of the drawing press and allows for simulations with the highest possible level of detail. The model was used to determine individual set forces for the die cushion cylinders which allowed for the production of sound quality parts without manual die spotting.

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One of the main errors in the machining accuracy of machine tools is the displacement through thermal induced deformation. Modern design and construction methods aim to optimize the heat flow in the machine to achieve minimum displacement. To enable a further improvement it is essential to know the displacement state of the complete machine structure. However, most measurement methods that are used to capture the influence of a thermal load only measure the displacement of the TCP or individual axes. This paper presents a methodology to capture the complex spatial displacement condition of a state of the art machine tool in one measuring cycle using a multichannel laser interferometer. It describes the development of the measurement model as well as the measurement setup in the workspace of the machine. With measurements according to the presented procedure, it is possible to uncover weak points in the structure of a machine tool and to derive warm-up and cooling strategies.

Although today's deep drawing tools are thoroughly designed and calculated by means of computeraided design (CAD), finite element (FE) simulation and computer-aided manufacturing (CAM), the sequence of operations to put a tool into production still encompasses manual and irreproducible labor. In particular, the die spotting is empirical and is almost entirely dependent on the toolmaker's experience. This fine-tuning of the drawing tool consumes a large amount of time. In minimizing manual die spotting, a large potential to decrease time and costs exists. This article presents error compensation methods to create deep drawing tools, which require less manual die spotting in order to produce sound quality stampings. In FE simulations of deep drawing operations, it is general practice to assume rigid tool and press properties. The fact that die and punch design are based on these simplifications might be one of the main causes that empirical die spotting is still imperative. Therefore, the authors developed a methodology to compensate the tool face for effects of elastic press and tool deformations, which occur under applied process load. The authors demonstrate the static compensation with two examples. The first shows the static compensation for ram tilting caused by an unbalanced load, which can originate from asymmetric part design and/or eccentric mounted tools. The second example describes the compensation for elastic die deformation caused by local and global deflections. In either case, the compensated die face, under applied process load, deformed into the desired die face. This research work shows the potential and limits of a static compensation for effects of elastic tool and press deformations on the final shape of the stamping.

Machine learning, big data and deep learning are today’s catchphrases for how to improve reliability and productivity of your manufacturing equipment. Production companies implement a large number of sensors to record every activity within their production lines and learn as much as possible about their running processes in order to predict shifting product properties and to prevent stoppage due to failure. The successful application of machine learning algorithms to predict machine and process behavior depends on a reliable and balanced database. Since the foremost goal of every manufacturing business is to make sound parts and to avoid defects, there is a large amount of data available for smoothly running processes but only very little for failure production. One approach to solve this imbalance would be to link the production line data with simulation data. Simulation models allow for computing failure parts with no additional costs and therefore enable the exploration of the entire parameter space. We conducted press-hardening experiments with a variation of process parameters for a structural car body part on the press hardening line at Fraunhofer IWU. As an evaluation criterion, we measured the hardness of the final part at critical spots. In order to expand the experimental data, we applied FE simulations to the entire press hardening process chain. The paper explains limitations of the model and elaborates on its parameterization. As a final task, we applied a basic machine-learning algorithm to both experimental and numerical data as well as to their combination in order to evaluate the data space expansion through simulations. The results obtained through machine learning indicate significant differences in the prediction of part quality for solely experimental data and its combination with simulation data. This is especially true for press hardening because of non-linear system behavior and a large amount of uncertain and hard-to-identify parameters. We found that the most challenging parts of uniting measured and simulated data is not only to create simulations with appropriate accuracy, which allow for a meaningful extrapolation of the parameter space, but also to compare simulation and production data based on the same criterion and to have stable simulation models for the entire parameter range.