Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy

Baig, Rahmath Ulla; Javed, Syed; Khaisar, Mohammed; Shakoor, Mwafak; Raja, P.

doi:10.1177/16878140211026720

Cited by 34 publications

(8 citation statements)

References 46 publications

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“…The idea behind ANNs is to emulate the brain's functioning to solve technical problems that may not be solvable using other methods, as noted by Svorcan et al [36]. Thus, ANN serves as a decision support tool [37]. The use of ANNs has reduced the development time and enhanced the flexibility of the studied system [38].…”

Section: Ann-based Modelingmentioning

confidence: 99%

Mathematical Modeling Using ANN Based on k-fold Cross Validation Approach and MOAHA Multi-Objective Optimization Algorithm During Turning of Polyoxymethylene POM-C

2024

jjmie

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The paper has a dual purpose: firstly, to examine the influence of various cutting conditions (cutting speed 𝑉 𝑐 , feed 𝑓, depth of cut 𝑎𝑝, tool nose radius 𝑟 ɛ , and cutting edge angle 𝑋 𝑟 ) on the quality of machined parts (𝑅𝑎), tangential force (𝐹 𝑍 ) and cutting power (𝑃 𝑐 ) during the turning process of polyoxymethylene POM-C. Two carbide inserts, SPMR 120304 and SPMR 120308, were used for the three-dimensional cutting operations. Secondly, the goal is to identify optimal cutting conditions that maximize material removal rate (𝑀𝑅𝑅) while minimizing three output parameters (𝑅𝑎, 𝐹 𝑍 , and 𝑃 𝑐 ). The study employed analysis of variance (ANOVA) to assess the significance of the input parameters on the desired outcomes and utilized an artificial neural network (ANN) to create mathematical models. The K-fold Cross-Validation approach was deemed suitable due to its efficiency in requiring fewer experiments. To optimize the cutting conditions, a new metaheuristic optimization algorithm called Multi-Objective Artificial Hummingbird Algorithm (MOAHA) was selected. ANOVA analysis reveals that factors 𝑓 and 𝑟 ɛ contribute 58.05% and 32.25%, respectively, to the response 𝑅𝑎. Classical parameters (𝑉 𝑐 ,𝑓, and 𝑎𝑝) also impact mechanical cutting actions (𝐹 𝑍 and 𝑃 𝑐 ).The MOAHA algorithm, coupled with four ANN models, optimized the five cutting conditions, resulting in optimal values 𝑉 𝑐 = 250 𝑚/𝑚𝑖𝑛, 𝑓 = 0.08 𝑚𝑚/𝑟𝑒𝑣, 𝑎𝑝 = 1.3 𝑚𝑚, 𝑟 ɛ = 0.8 𝑚𝑚, and 𝑋 𝑟 = 75°. Under these conditions, responses are: 𝑅𝑎 = 0.6 µ𝑚, 𝐹 𝑍 = 21.51 𝑁,𝑃 𝑐 = 60.24 𝑊, and 𝑀𝑅𝑅 = 26.38 𝑐𝑚 3 /𝑚𝑖𝑛. The ANN-MOAHA coupling provides an excellent, simple, and fast computer tool for multi-objective optimization.

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Section: Ann-based Modelingmentioning

confidence: 99%

Mathematical Modeling Using ANN Based on k-fold Cross Validation Approach and MOAHA Multi-Objective Optimization Algorithm During Turning of Polyoxymethylene POM-C

2024

jjmie

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“…The feed rate was shown to be the most important component based on the findings. According to Baig et al [33], to preserve the product's surface finish during milling, a modern machining system needs to be able to recognize tool wear. It has been demonstrated that the vibration fingerprints generated by a single-point milling cutter during machining are reliable indicators of the tool's condition.…”

Section: Ann-ga On Prediction Of Tool Wearmentioning

confidence: 99%

An Overview of the Study of ANN-GA, ANN-PSO, ANFIS-GA, ANFIS-PSO and ANFIS-FCM Predictions Analysis on Tool Wear During Machining Process

Okokpujie¹,

Sinebe²

2023

JESA

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The implementation of soft computing procedures in tool wear prediction and optimization is a significant process in machining operations for sustainable manufacturing of components with quality finishing. Tool wear is one of the response parameters that leads to a high rate of production cost due to constant tool substitution during machining, mostly when machining hard metals that are difficult to machine. With these challenges, several techniques have been put in place to optimize and predict tool wear rates, including turning, milling, grinding, shaping, and drilling. This study focuses on the evaluation of existing literature that employs soft computing procedures such as ANN-GA, ANFIS, ANFIS-PSO, and ANFIS-FCM in the prediction of cutting tool wear rate during machining processes. From the different study reviews, the results show that the application of these soft computing procedures significantly improves tool life during the manufacturing process by employing the optimal machining parameters in an eco-friendly nano-lubrication environment. This study also points out the challenges currently faced with these soft computing techniques and gives a sustainable way forward as a recommendation to improve the manufacturing process.

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“…It is therefore necessary to replace the tool regularly, in order to optimize production and reduce costs [ 3 ]. It is estimated that tool costs can account for up to 12% of the production costs of a part [ 4 ]. Thus, to ensure that tools operate under nominal conditions and to minimize tool costs, the monitoring of cutting tools has become an important research topic [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks

Colantonio,

Equeter,

Dehombreux

et al. 2024

Sensors

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The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies.

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Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy

Cited by 34 publications

References 46 publications

Mathematical Modeling Using ANN Based on k-fold Cross Validation Approach and MOAHA Multi-Objective Optimization Algorithm During Turning of Polyoxymethylene POM-C

Mathematical Modeling Using ANN Based on k-fold Cross Validation Approach and MOAHA Multi-Objective Optimization Algorithm During Turning of Polyoxymethylene POM-C

An Overview of the Study of ANN-GA, ANN-PSO, ANFIS-GA, ANFIS-PSO and ANFIS-FCM Predictions Analysis on Tool Wear During Machining Process

Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks

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