Prediction of the Reaming Torque Using Artificial Neural Network and Random Forest Algorithm: Comparative Performance Analysis

Document Type

Article

Publication Title

Engineering Proceedings

Abstract

In any manufacturing setup, reaming operation is always prominent and present because of ever increasing demands for improved quality of the manufactured products. At the same time, new engineering materials make the process challenging. Further, reaming is the highly sought-after operation to achieve specified tolerance for specified applications to satisfy the rising demand for high-quality and precision-engineered products. Hence, accurate prediction of reaming torque is of utmost necessity, as it gives rise to uneven cutting forces, thereby affecting the surface finish of the reamed hole. High torque produces high-cutting forces, resulting in uneven surface finish and oversized holes. In this regard, the ability of traditional statistical tools to identify intricate correlations and patterns in reaming operation data is limited. To overcome these issues, machine learning methods such as the Artificial Neural Network (ANN) provide reliable options. The present study compares the use of ANN and Random Forest to analyze the data from reaming operations to predict the torque and compares it with those of the Random Forest method and the polynomial regression model. The model is trained and tested using a well-structured dataset that includes multiple input parameters (e.g., material, tool radius, and rotation angle) and the related reaming outputs (e.g., torque) in the suggested supervised learning method. An interconnected single layer of artificial neurons is used to create the ANN model. A comparison is made between the ANN and the Random Forest algorithm, a well-liked ensemble learning technique based on decision trees, to assess the performance of the ANN. The same dataset is used to train both ANN and Random Forest algorithms. The result showed that ANN gave better performance when compared to the other models, with testing accuracy of 94.4% and 61% for ANN and Random Forest, respectively.

DOI

10.3390/engproc2023059097

Publication Date

1-1-2023

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