Predicting the martensite content of metastable austenitic steels after cryogenic turning using machine learning
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ORIGINAL ARTICLE
Predicting the martensite content of metastable austenitic steels after cryogenic turning using machine learning Moritz Glatt 1 & Hendrik Hotz 1 & Patrick Kölsch 1 & Avik Mukherjee 1 & Benjamin Kirsch 1 & Jan C. Aurich 1 Received: 21 August 2020 / Accepted: 24 September 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract During cryogenic turning of metastable austenitic stainless steels, a deformation-induced phase transformation from γ-austenite to α’-martensite can be realized in the workpiece subsurface, which results in a higher microhardness as well as in improved fatigue strength and wear resistance. The α’-martensite content and resulting workpiece properties strongly depend on the process parameters and the resulting thermomechanical load during cryogenic turning. In order to achieve specific workpiece properties, extensive knowledge about this correlation is required. Parametric models, based on physical correlations, are only partly able to predict the resulting properties due to limited knowledge on the complex interactions between stress, strain, temperature, and the resulting kinematics of deformation-induced phase transformation. Machine learning algorithms can be used to detect this kind of knowledge in data sets. Therefore, the goal of this paper is to evaluate and compare the applicability of three machine learning methods (support vector regression, random forest regression, and artificial neural network) to derive models that support the prediction of workpiece properties based on thermomechanical loads. For this purpose, workpiece property data and respective process forces and temperatures are used as training and testing data. After training the models with 55 data samples, the support vector regression model showed the highest prediction accuracy. Keywords Machining . Machine learning . Cryogenic cooling . Surface integrity . Metastable austenitic steel
1 Introduction The surface morphology of a component is significantly influenced by the characteristics of the manufacturing process and has a decisive impact on the application behavior of the component [1, 2]. In order to tailor the surface morphology of a component to the demanded requirements, extensive knowledge regarding the causal correlations between the input variables of the manufacturing process, the acting mechanical, chemical and thermal loads, and their impact on the material properties is essential [3]. Especially for small batch sizes, the determination of suitable process parameters for new demanded requirements tends to be expensive due to a high number of required experiments. In this context, the application of machine learning can help to decrease the required amount of expensive experimental investigations. The * Moritz Glatt [email protected] 1
Institute for Manufacturing Technology and Production Systems, TU Kaiserslautern, Kaiserslautern, Germany
presented case study on cryogenic turning focuses on the prediction of the martensite content generated during the process,
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