Estimation of parameters for the free-form machining with deep neural network
Gudelek, M. Ugur
Özbayoğlu, Ahmet Murat
Ünver, Hakkı Özgür
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Predictive Analytics is a crucial part of a Big Data application. Lately, developers have turned their attention to deep learning models due to their huge success in various implementations. Meanwhile, there is lack of deep learning implementations in manufacturing applications due to insufficient data. This phenomenon has been slowly shifting due to the application of IoT and Industry 4.0 concept within the manufacturing industry. Streaming and batch data producing sources are becoming more and more common in the machining industry. In this paper, we propose a deep learning predictive analytics model based on the data generated by a particular machining process. The results indicate that using such a model can make very accurate predictions and can be used as part of a real-time decision-making process in the manufacturing industry. In this study, the prediction models of three crucial metrics of machining such as quality, performance and energy consumption have been developed by utilizing artificial neural networks and deep learning methods. Specific measures of quality, performance and energy consumption refer to material removal rate (MRR), surface roughness (Ra) and specific energy consumption (SEC) respectively. The control parameters of machining are selected as stepover (a(e)), depth of cut (a(p)), feed per tooth (f(z)) and cutting speed (V-c). In addition, variance analysis (ANOVA) has been used to examine the effects of the input parameters on the output parameters.