Abstract
The improvement of industrial grinding processes is driven by the objective to reduce process time and costs while maintaining required workpiece quality characteristics. One of several limiting factors is grinding burn. Usually applied techniques for workpiece burn are conducted often only for selected parts and can be time consuming. This study presents a new approach for grinding burn detection realized for each ground part under near-production conditions. Based on the in-process measurement of acoustic emission, spindle electric current, and power signals, time-frequency transforms are conducted to derive almost 900 statistical features as an input for machine learning algorithms. Using genetic programming, an optimized combination between feature selector and classifier is determined to detect grinding burn. The application of the approach results in a high classification accuracy of about 99% for the binary problem and more than 98% for the multi-classdetection case, respectively.
Highlights
In industrial manufacturing, grinding often is a final production step that must fulfill high demands on precision and surface integrity
There is a broad range of machine learning algorithms; this study focuses on classification methods suitable for the detection of grinding burn
Each experiment is matched with a corresponding grinding burn class
Summary
In industrial manufacturing, grinding often is a final production step that must fulfill high demands on precision and surface integrity. The consideration of the manufacturing process environmental impact gains more importance, leading into aims to reduce the usage of resources. These three aspects contradict, as increasing the production speed results in a higher wear of the grinding tool, a higher energy input per time, and the amount of scrap parts increases. As almost all of the introduced energy is converted into heat, the high temperatures can cause undesirable effects onthe surface integrity. These microstructural changes comprise phase transformation, residual stresses, and
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