What question did this study set out to answer?

To develop an integrated monitoring system for groove grinding that enables real-time detection of grinding wheel wear.

April 20, 2026Advances in Mechanical Engineering0 citationsOpen Access

In-process tool wear and performance parameters monitoring for real-time warning during groove grinding bearing production

ANAnh-Tuan NguyenOptimization Technology (United States)VNVan-Hai NguyenPhenikaa University

Key Points

To develop an integrated monitoring system for groove grinding that enables real-time detection of grinding wheel wear.
Designed an HMI integrated with pneumatic sensors for real-time data collection.
Utilized machine learning models like ANN and XGBoost optimized by Bayesian methods.
Employed multi-objective optimization to define optimal grinding conditions.
Integrated SHAP technique for parameter influence interpretation.
Conducted factory trials to validate system functionality under industrial conditions.
Achieved stable latency and consistent predictions with MAPE values within 5%.
The system successfully monitored critical grinding parameters, triggering alerts based on wear measurements.
Real-time insights led to improved production efficiency with minimal surface roughness.

Abstract

This study develops an integrated monitoring system with a human–machine interface (HMI) for groove grinding on the outer surface of the inner ring of a bearing type 6205 in an industrial environment. The proposed system is specifically designed to ensure stable real-time monitoring under progressive grinding wheel wear evolution, rather than maximizing prediction accuracy under fixed and stationary operating conditions. The system combines a grinding wheel wear measurement setup using pneumatic sensors, machine learning algorithms, and multi-objective optimization methods. The input data are collected in real time from the pneumatic measurement system (PMS) to monitor wheel wear ( G w ), and from inductive proximity sensors to count the number of processed parts ( N p ) in each grinding cycle. Subsequently, machine learning models (ANN, XGBoost, CatBoost, LightGBM) tuned with Bayesian optimization (BO) and combined with the NSGA-II algorithm are used to determine the optimal solutions for grinding conditions that consist of wheel feed rate ( f w ), workpiece speed ( v w ), depth of cut ( a e ), and N p , based on the objectives of maximizing material removal rate ( Q w ), and minimizing surface roughness ( Ra ) and ovality ( O p ). The machine learning hyperparameters are optimized using the Bayesian algorithm. The selected solutions are adapted to user preferences and are used as input cutting parameters for the monitoring system. The SHapley Additive exPlanations (SHAP) technique is used to interpret the influence of grinding parameters on the output. The entire system is integrated into the HMI to display predictions in real time and to issue warnings when Ra exceeds 0.3 µm, O p exceeds 3 µm, or G w exceeds 12 µm, at which point a dressing alert is triggered. In factory trials, the HMI maintained stable latency and prediction consistency, with MAPE values within 5% across the full wear range. These results reflect the actual behavior observed on the line and confirm that the system can operate reliably under industrial conditions.

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