# Machine Learning-Based Soft Sensor for Real-Time Wire Bow Prediction in Diamond Multi-Wire Sawing

## TL;DR

This paper introduces a machine learning-based system to predict wire bow in diamond multi-wire sawing, offering a cost-effective alternative to physical sensors.

## Contribution

A novel soft sensor framework using XGBoost with hyperparameter optimization for real-time wire bow prediction in MWS.

## Key findings

- The XGBoost model achieved an R2 score of 0.992 and MAE of 0.116 mm in predicting wire bow.
- The model successfully predicted wire bow at different positions (head, middle, tail) of the wire web.
- SHAP analysis revealed key mechanical dependencies influencing wire bow.

## Abstract

Real-time monitoring of wire bow is critical for ensuring wafer quality and preventing wire breakage in diamond multi-wire sawing (MWS). However, the deployment physical sensors in industrial MWS environments is hindered by severe sludge contamination, limited installation space, and high maintenance costs. To address these challenges, this paper proposes a novel data-driven soft sensor framework utilizing machine learning methods to predict wire bow based on readily accessible process data. A feature engineering pipeline, combining variance thresholding and correlation analysis, is established to identify key process variables. Subsequently, six representative ML algorithms are systematically evaluated, with eXtreme Gradient Boosting (XGBoost) optimized via two-stage hyperparameter optimization emerging as the superior model. Experimental results from an industrial MWS machine demonstrate that the proposed model achieves a coefficient of determination (R2) of 0.992 and a mean absolute error (MAE) of 0.116 mm. Furthermore, the prediction is also extended to spatially distributed positions (head, middle, and tail) of the wire web. Finally, SHAP (SHapley Additive exPlanations) is utilized to elucidate the mechanical dependencies. This work provides a reliable and low-cost solution for wire bow monitoring during the MWS process.