Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining

Abstract

Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining.

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression

“…Recent advancements, including the application of AI for predicting surface roughness in additively manufactured components, as demonstrated by Temesgen Batu et al [35], the utilization of causality-driven feature selection to enhance deep-learning-based models in milling machines by Hyeon-Uk Lee et al [36], and the investigation of novel parameters in grinding processes by Mohammadjafar Hadad et al [37], collectively suggest that innovative methodologies can markedly improve predictive accuracy. The enhanced prediction of surface roughness in titanium alloy during abrasive belt grinding, achieved through an advanced Radial Basis Function (RBF) neural network by Kun Shan et al [38], and the high precision attained by integrating hybrid features with an Improved Sparrow Search Algorithm-Deep Belief Network (ISSA-DBN) for milling die steel P20, as reported by Miaoxian Guo et al [39], further highlight the efficacy of these cutting-edge approaches.…”