Abstract
Self-optimizing process planning is an essential approach for finding optimum process parameters and reducing ramp-up times in machining processes. For this purpose, polishing is presented as an application example. In conventional polishing processes, the process parameters are selected according to the operator’s expertise in order to achieve a high-quality surface in the final production step. By implementing machine learning (ML) models in process planning, a correlation between process parameter and measured surface quality is generated. The application of this knowledge automates the selection of optimal process parameters in computer-aided manufacturing (CAM) and enables a continuous adaptation of the NC-code to changing process conditions. Applying the presented ML-model, the prediction accuracy of 83% will adapt the process parameters to achieve the target roughness of 0.2 μm. The sample efficiency is shown by the decrease in root mean square error from 0.1–0.28 to 0.02–0.07 μm with additional polishing iterations.
Highlights
In times of “Industry 4.0,” self-optimizing machining systems are in demand which enable a low-cost process with the output of individual high-quality products [1]
Process planning via the computer-aided manufacturing (CAM) interface provides an expedient way of achieving self-optimizing machining systems (SOMS) [2]
Process planning for finishing operation of complex workpiece geometries is usually done manually, which requires expert knowledge
Summary
In times of “Industry 4.0,” self-optimizing machining systems are in demand which enable a low-cost process with the output of individual high-quality products [1]. The development of highly adaptable manufacturing processes is technically advanced to a degree that allows it to be applied in the industrial sector [2, 3] In this context, process planning via the CAM interface provides an expedient way of achieving self-optimizing machining systems (SOMS) [2]. Machine learning models play an increasingly significant role in processing the understanding between the machining parameters in the NC-code and manufacturing outcome [5]. This eliminates the need for time-consuming experiments and purely experience-based process design. The approach will be validated and the process parameters will be adapted to changing process conditions
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