Prediction and optimization of tool wear rate during electric discharge machining of Al/Cu/Ni alloy using adaptive neuro-fuzzy inference system

Phate, Mangesh; Bendale, Aditya; Toney, Shraddha; Phate, Vikas

doi:10.1016/j.heliyon.2020.e05308

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…9. It is interpreted that the parameter IP is the major significant parameter followed by the PON, which contributes 32.6142% and 21.7588% towards the impact on GRG or overall performance [18].…”

Section: Resultsmentioning

confidence: 99%

“…In the present work, the aim is to examine the influence of various EDM process parameters and determine the optimum process conditions during the EDM of Al/B4C MMC [17]. The Al/Cu/Ni alloy was prepared, and experiments were carried out to know the ease of the EDM process [18]. The researcher examined the WEDM of Al/SiC MMC to determine the optimized machining parameters [19].…”

Section: Introductionmentioning

confidence: 99%

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Multi-response Optimization and Analysis of Al/B4Cp EDM using Grey Relational Analysis

Toney¹,

Phate²,

Phate³

2022

JMechE

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Development of material, especially metal matrix composite (aluminumbased MMC), attracts worldwide researchers due to its enhancement in wear and abrasive resistance, lightweight and high strength, low coefficient of thermal expansion. This work investigates the electrical discharge machining (EDM) of Aluminum/Boron Carbide (Al/B4C) MMC. The optimization will help the researchers or metal processing industries use the investigated optimized process parameters for efficient and effective machining parameters. Pulse on time (PON), pulse off time (POFF), a variation of boron carbide(CP), and input current (IP) were varied to measure material removal rate (MRR), wear rate of the tool (TWR), and the surface roughness (Ra). The Taguchi method is used for the analysis of the machining parameters. Grey relational grade (GRA) is employed for the optimization of machining responses and the input parameters. The influence of various EDM parameters is investigated by analysis of variance (ANOVA). The multi-response optimization was carried out by GRA (CP: 4% of B4C, PON” 25 micro-sec, POFF: 9 micro-sec, and input current of 8 amp). The optimum responses were MRR: 17.58 mm3 /sec, TWR: 0.064 mm3 /sec, and Ra: 1.20 microns. The Aluminum-based alloys or composites are widely used in aerospace applications, automobile sectors, construction, aircraft industries, etc.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-response Optimization and Analysis of Al/B4Cp EDM using Grey Relational Analysis

Toney¹,

Phate²,

Phate³

2022

JMechE

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“…The research aims to achieve model interpretability, enabling a better understanding and reliability of the model's predictions. 26,27 Deep Learning (DL) methods have gained prominence in Prognostics and Health Management (PHM), 28 including Tool Wear Monitoring (TWM). The research efforts reflect a proactive exploration of DL's potential, with various studies highlighting advantages, discussing opportunities, and addressing challenges.…”

Section: Introductionmentioning

confidence: 99%

Computer numerical control machine tool wear monitoring through a data-driven approach

Gougam,

Afia,

Aitchikh

et al. 2024

Advances in Mechanical Engineering

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The susceptibility of tools in Computer Numerical Control (CNC) machines makes them the most vulnerable elements in milling processes. The final product quality and the operations safety are directly influenced by the wear condition. To address this issue, the present paper introduces a hybrid approach incorporating feature extraction and optimized machine learning algorithms for tool wear prediction. The approach involves extracting a set of features from time-series signals obtained during the milling processes. These features allow the capture of valuable characteristics relating to the dynamic signal behavior. Subsequently, a feature selection process is proposed, employing Relief and intersection feature ranks. This step automatically identifies and selects the most pertinent features. Finally, an optimized support vector machine for regression (OSVR) is employed to predict the evolution of wear in machining tool cuts. The proposed method’s effectiveness is validated from three milling tool wear experiments. This validation includes comparative results with the Linear Regression (LR), Convolutional Neural Network (CNN), CNN-ResNet50, and Support Vector Regression (SVR) methods.

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“…In order to accomplish real-time tool condition monitoring, the power signals should be processed by artificial intelligence technology. Neural networks [6,18], fuzzy logic [19], Bayesian regression [20], support vector machine [21], etc., are applied to estimate machining status through monitoring data. For tool wear prediction in fixed cutting parameter set, Kuntoğlu et al [22] constructed the prediction model of tool flank wear in turning using fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Application of cutting power in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

Qiang¹,

Shi²,

Liu³

et al. 2022

Preprint

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Tool wear is inevitable in actual manufacturing, especially in extreme processing conditions for milling difficult-to-cut materials. With the degradation of cutting performance, the undesirable tool status could trigger instability in machining process that causes negative effect on machined surface and dimension accuracy of the precision components. Therefore, tool condition monitoring for machining process is imperative. With distinctive characteristics on easy acquisition and non-interference in machining process, the power consumption of machine tool has been proved to be a reliable indicator for reflecting the changes of cutting process in industry. Thus, the present study proposes a novel method for monitoring tool condition using net cutting power consumption. The result indicates that the developed approach performs better than traditional approach which uses original total power to predict tool wear. This can be attributed to the changes of net cutting power consumption is more relevant with tool wear evolution. Furthermore, predicting tool wear under variable cutting parameters is achieved using Gaussian process regression, because it has good generalization performance and also explains the uncertainty of prediction results in the form of probability. This study reveals that low-cost sensor like power meter, can be used as an important supplement to monitor tool condition in the industry, and also provides a research basis for predicting tool wear under different cutting conditions.

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Prediction and optimization of tool wear rate during electric discharge machining of Al/Cu/Ni alloy using adaptive neuro-fuzzy inference system

Cited by 8 publications

References 19 publications

Multi-response Optimization and Analysis of Al/B4Cp EDM using Grey Relational Analysis

Multi-response Optimization and Analysis of Al/B4Cp EDM using Grey Relational Analysis

Computer numerical control machine tool wear monitoring through a data-driven approach

Application of cutting power in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

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