Causality-Driven Efficient Feature Selection for Deep-Learning-Based Surface Roughness Prediction in Milling Machines

Abstract

This paper studies the application of artificial intelligence to milling machines, focusing specifically on identifying the inputs (features) required for predicting surface roughness. Previous studies have extensively reviewed and presented useful features for surface roughness prediction. However, applying research findings to actual operational factories can be challenging due to the additional costs of sensor installations and the diverse environments present in each factory setting. To address these issues, in this paper, we introduced effective features for predicting surface roughness in situations where additional sensors are not installed in the existing environment. These features include feed per tooth, Fz; material removal rate, Q; and the load information. These features are suitable for use in highly constrained environments where separate sensor installation is not required, making it possible to apply the research findings in various factory environments. Additionally, to efficiently select the optimal subset for surface roughness prediction among subsets formed by available features, we apply causality to the feature selection method, proposing an approach called causality-driven efficient feature selection. The experimental results demonstrate that the features introduced in this paper are quite suitable for predicting surface roughness and that the proposed feature selection approach is more effective and efficient compared to existing selection methods.

CoDC: Accurate Learning with Noisy Labels via Disagreement and Consistency

“…Inspired by the biological nervous system, deep neural networks (DNNs) have been successfully utilized in various tasks [1][2][3][4][5][6], particularly in computer vision [7][8][9][10][11][12]. This is made possible by large-scale datasets with accurate labels, although collecting them can be challenging and costly, especially in certain professional fields that require personnel with relevant professional knowledge to label samples.…”

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.…”