Application of Cause-Effect-Networks for the process planning in laser rod end melting

Abstract

In micro manufacturing, a precise configuration of manufacturing processes constitutes an essential factor for success. The continuing miniaturization of work pieces results in ever decreasing tolerances, whereas machines and processes become more and more specialized. As a result, a precise determination of each process result is important to guarantee the final product quality. Unfortunately, so called size effects often prevent the direct transfer of knowledge from the area of macro manufacturing. To cope with these effects, finite element simulations provide a suitable tool to simulate forming processes and their results in advance and to perform parameter studies in order to analyze the process and effect interdependencies. Unfortunately, these simulations usually require a rather long computing time, so that only simulations for a small subset of the available parameter range can be performed in a reasonable planning interval. In this context, this article presents an application of the method “Micro – Process Planning and Analysis” (μ-ProPlAn) for the configuration of laser rod end melting, which is used to create preforms for further forming processes. This method uses cause-effect networks, to combine expert knowledge with methods from artificial intelligence to estimate the result of laser melting processes quickly. For this purpose, the cause-effect networks are trained using a finite element simulation of the laser process using different process parameters and varying rod diameters. Results show a high accuracy for the prediction of the finite element simulation results. This article focusses on the validation of these cause-effect networks in comparison to the real laser rod end melting process and demonstrates how these models can be used to predict the resulting volume, eccentricity and the largest diameter of the solidified preform for different process configurations.

RETRACTED: AI-driven techniques for controlling the metal melting production: a review, processes, enabling technologies, solutions, and research challenges

“…FEM (Finite Element Method) is a tool for deciphering micro-level components. When we lower the size of a portion and apply load to it, we get side effects [62].…”

Digital Twins: A Maturity Model for Their Classification and Evaluation

“…In micro-manufacturing, cause-effect networks have been applied, e.g., for the (offline) configuration of rotary swagging, the estimation of tool degradation in micro punching [84], or as surrogate, to reduce the computational times of physical simulations by finite element method (FEM) for a laser rod-end melting process [85], as depicted in Fig. 10.…”

Process Design

“…In combination with the software prototype, it can be applied to different topics, e.g. for cost assessments of different configurations [Rip14], for the evaluation of different machining strategies [Rip14a], for the process characterization and configuration [Rip17b], or even as an abstraction for physics-based finite-element simulations to enable fast and precise process planning [Rip18a]. In addition, the methods underlying the cause-effect networks can easily be extended to utilize time-related information, thus enabling a re-training of causeeffect networks to cope with a lack of initial training data [Rip18] or in the context of predictive maintenance [Rip17a].…”