Integrated computational product and production engineering for multi-material lightweight structures

Abstract

Within product development processes, computational models are used with increasing frequency. However, the use of those methods is often restricted to the area of focus, where product design, manufacturing process, and process chain simulations are regarded independently. In the use case of multi-material lightweight structures, the desired products have to meet several requirements regarding structural performance, weight, costs, and environment. Hence, manufacturing-related effects on the product as well as on costs and environment have to be considered in very early phases of the product development process in order to provide a computational concept that supports concurrent engineering. In this contribution, we present an integrated computational concept that includes product engineering and production engineering. In a multi-scale framework, it combines detailed finite element analyses of products and their related production process with process chain and factory simulations. Including surrogate models based on machine learning, a fast evaluation of production impacts and requirements can be realized. The proposed integrated computational product and production engineering concept is demonstrated in a use case study on the manufacturing of a multi-material structure. Within this study, a sheet metal forming process in combination with an injection molding process of short fiber reinforced plastics is investigated. Different sets of process parameters are evaluated virtually in terms of resulting structural properties, cycle times, and environmental impacts.

Machine learning and simulation-based surrogate modeling for improved process chain operation

“…The presented work is a continuation of preliminary works on Integrated Computational Product and Production Engineering (icPPE) [24] and surrogate modeling [17]. In the following, the main aspects will be discussed in terms of improved process chain operation.…”

“…As process and product quality are decisive for future competitiveness of manufacturing, icPPE strives to leverage data of engineering methods (FEM) and process chain Fig. 2 Concept of Integrated Computational Product and Production Engineering (ICPPE) in accordance with [24] simulation, to improve manufacturing operation. This is accomplished by forwarding the detailed quality properties derived by FEM simulations along multiple scales of manufacturing.…”

“…This is accomplished by forwarding the detailed quality properties derived by FEM simulations along multiple scales of manufacturing. In this context, Hürkamp et al show the applicability and potentials of fast and accurate data-driven surrogates of FEM simulations for the design of process chains [24] and as virtual quality gates [17]. In addition, Filz et al [7] discuss the design and potentials of virtual quality gates for future manufacturing, considering FEM surrogate modeling as a promising approach.…”

“…good fit and fast training / prediction, and random forest (RF), i.e. best fit, have shown best performances for surrogate modeling of FE simulation data [17,24,32]. Hence, within this paper, DT and RF are selected for the data-driven modeling.…”

Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites

“…In today's product development, it is a matter of course to support the product and production engineering by virtual methods [9]. Thereby, computer simulations analyze the material behavior of products and processes using numerical methods based on Finite Elements or Finite Difference schemes.…”