Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

Alajmi, Mahdi S.; Almeshal, Abdullah M.

doi:10.3390/ma14143773

Cited by 21 publications

(8 citation statements)

References 27 publications

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“…e ANIFS-QPSO has shown great agreement with experimentally measured results. ey [15] have introduced an approach to compute the insert wear in the turning process with help of neural intelligence. ey have used support vector machines (SVM) for regression with Bayesian optimization to evaluate the wear based on varying the level of process factors.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625

Rajesh

Prabhuswamy

Naik

2022

Advances in Materials Science and Engineering

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Roughness is a prime parameter in any process/operation as it aids in confirming the quality status of the product. The insert and workpiece would develop a lot of friction and as a result, it generates heat in the cutting zone, which affects the machined surface. The speed, feed, and depth of cut were chosen as processing factors. L27 Orthogonal array is used based on the Taguchi technique. The regression analysis is used to develop an equation to predict the roughness. The impact of the processing factors on the machined surface is studied with help of ANOVA (Analysis of Variance). Furthermore, the estimation of surface roughness is carried out using a machine learning-based model-feed forward (nonlinear autoregressive network) NARX network, and the evaluated surface roughness is compared with the values predicted by the regression model and experimental results. The average percentage error observed with the predicted values by NARX is observed as 3.01%, which is lower than the average percentage error observed by the regression model 5.131%. Thus, this work provides the best machine learning approach to the prognosis of the roughness in dry turning of Inconel 625, which would save a lot of time and unnecessary wastage of the work material.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625

Rajesh

Prabhuswamy

Naik

2022

Advances in Materials Science and Engineering

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“…Tool wear is typically the dependent variable, and the feed rate, cutting time, spindle speed, and cutting depth are the independent variables. Through the use of a multi-sensor tool wear prediction method based on stationary subspace analysis (SSA), support vector machines (SVMs) have been successfully applied for tool wear prediction, tool status identification, and fault diagnosis [ 15 , 16 , 17 ]. Without any prior knowledge, multi-sensor signals are converted to stationary and non-stationary sources.…”

Section: Related Workmentioning

confidence: 99%

Novel Framework for Quality Control in Vibration Monitoring of CNC Machining

Apostolou,

Ntemi,

Paraschos

et al. 2024

Sensors

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Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.

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“…So far, commonly used ML models and algorithms in HEA design include neural networks (NNs) [31][32][33][34][35][36][37][38][39][40][41][42][43], support vector machine (SVM) [44][45][46][47][48][49][50][51][52][53][54], Gaussian process (GP) [36,[55][56][57][58][59][60][61], k-nearest neighbors (KNN) [62][63][64][65][66], and random forests (RFs) models and algorithms [67,68] etc.…”

Section: Common ML Models and Algorithms In Hea Designmentioning

confidence: 99%

Machine Learning Design for High-Entropy Alloys: Models and Algorithms

Liu,

Yang

2024

Metals

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High-entropy alloys (HEAs) have attracted worldwide interest due to their excellent properties and vast compositional space for design. However, obtaining HEAs with low density and high properties through experimental trial-and-error methods results in low efficiency and high costs. Although high-throughput calculation (HTC) improves the design efficiency of HEAs, the accuracy of prediction is limited owing to the indirect correlation between the theoretical calculation values and performances. Recently, machine learning (ML) from real data has attracted increasing attention to assist in material design, which is closely related to performance. This review introduces common and advanced ML models and algorithms which are used in current HEA design. The advantages and limitations of these ML models and algorithms are analyzed and their potential weaknesses and corresponding optimization strategies are discussed as well. This review suggests that the acquisition, utilization, and generation of effective data are the key issues for the development of ML models and algorithms for future HEA design.

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Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

Cited by 21 publications

References 27 publications

Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625

Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625

Novel Framework for Quality Control in Vibration Monitoring of CNC Machining

Machine Learning Design for High-Entropy Alloys: Models and Algorithms

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