Multi-condition wear prediction and assessment of milling cutters based on linear discriminant analysis and ensemble methods

Zhou, Honggen; Gao, Sande; Xie, Yang; Zhang, Chaoyong; Liu, Jinfeng

doi:10.1016/j.measurement.2023.112900

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…3 as I -initial wear zone , the second zone, marked as N is normal operation zone and the third zone, marked E to excessive wear zone. The obtained course of changes in the value of the total cutting force is consistent with the results obtained by the authors of other works [6,24,25]. To determine the durability period of the shortening edge, the formula (2) was used:…”

Section: Measurements Resultssupporting

confidence: 86%

“…Machining of alloy steels, due to its alloy components that are difficult to cut, is generally problematic and requires the use of wear-resistant tools; attempts are also made to carry out machining in conditions different from standard ones, for example dry or using MQL [7,12,17,18,20,23]. At the same time, work is being carried out on modifying the cutting edge geometry [1,11] or process modeling [21,24].…”

Section: Introductionmentioning

confidence: 99%

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AISI 304 stainless steel milling process state diagnosing

Polasik,

Roszak,

Boiko

2024

MATEC Web Conf.

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The article presents the results of works on the analysis of using cutting forces and torque to determine AISI 304 stainless steel milling process state. The research and analyzes were carried out for the determine tool state and the process state itself. The tests were performed using Sandvik milling head, equipped with 345R-1305M-PM 4230 inserts . This study presents and discusses the obtained results determining the resultant value of the minimum and maximum cutting forces and the tool life. The results of observations of the tool's condition were presented.

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Section: Measurements Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

AISI 304 stainless steel milling process state diagnosing

Polasik,

Roszak,

Boiko

2024

MATEC Web Conf.

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show abstract

“…The decision tree considers information gain and is based on a purity measure called entropy, where each p i is the probability of the property i within the set, which ranges from p i = 1 when all members of the set have the property i, and p i = 0 when no member of the set has the property i: Zhou et al, 2023). Random forest is based on decision trees, although they create a forest of trees where the outcome is determined by the votes in the formations.…”

Section: The Modelmentioning

confidence: 99%

Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction

Perroni,

Veiga,

Forteski

et al. 2024

Sage Open

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Predicting operational performance enables organizations to develop operational effectiveness goals considering different combinations of resources. Measuring performance is consolidated with advances in relative efficiency analysis techniques, including data envelopment analysis (DEA) and stochastic frontier analysis (SFA), albeit these methods lack predictive capability. This paper proposes an approach for performance prediction by integrating relative efficiency measurement models with machine learning algorithms. Data analyses were conducted using data provided by the energy assessment project offered to small and medium-sized manufacturing companies in the United States ( n 7,548) using sales as the output, with the inputs being the number of employees, hours of operation, electricity, natural gas, cost of electricity, and cost of natural gas. Performance was estimated differently, employing parametric (SFA) and non-parametric (DEA) methods. The prediction benchmarking process occurred by adopting machine learning algorithms: regression (LM), support vector machine (SVM), K-nearest neighbor (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT). The findings showed that it is possible to identify the best prediction algorithm associated with a performance model. However, the performance prediction may differ if different strategies for measuring performance or machine learning model configurations are used. In addition, SFA-LOG and SVM had the best performance for regression, and DEA-VRS/IRS excelled with random forest; the RF algorithm was the best fit across all performance approaches. The error rate depends on the algorithm and the performance model, and the number of classes must be reduced to obtain a higher success rate.

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“…This improvement is attributed to the application of dropout and early stopping techniques. Zhou et al [19] proposed a milling tool wear prediction method for multi-condition classification and rapid selection of prediction models based on linear discriminant analysis (LDA) and the ensemble method. First, the whole wear process of the cutting tool is divided into five stages.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

Lin,

Hsieh

2024

Applied Sciences

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This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

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Multi-condition wear prediction and assessment of milling cutters based on linear discriminant analysis and ensemble methods

Cited by 6 publications

References 28 publications

AISI 304 stainless steel milling process state diagnosing

AISI 304 stainless steel milling process state diagnosing

Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

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