Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling

Wiciak-Pikuła, Martyna; Felusiak-Czyryca, Agata; Twardowski, Paweł

doi:10.3390/s20205798

Cited by 18 publications

(11 citation statements)

References 35 publications

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“…Neural networks are one of the popular ML algorithms with variants such as feed-forward neural networks, backpropagation neural networks, convolutional neural networks, etc. 135 In the recent past, neural networks are commonly applied in the prediction of failure stages of bolted joints, 136 concrete, 137 delamination evaluation in composites, 138 tool wear, 139 detection of looseness in bolted joints, 133 etc. Artificial neural network.…”

Section: Neural Networkmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

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The integrity of bolted joints is still a challenging problem owing to the gross or localized slip at the interfacial surfaces of the joints when subjected to external disturbances such as vibrations. This slip escalates the interfacial movement and, thus, leads to a decrease in preload levels, that is, looseness of the bolted assemblies. In the last decade, modal analysis, wave propagation, and percussion methods were traditionally used to detect looseness in bolted connections. With an increase in computational power, machine learning algorithms such as neural networks, random forests, decision trees, and support vector machines complemented the traditional methods in accurate looseness estimation. Subsequently, this integration paved the path for real-time health monitoring of bolted joints. This paper summarizes recent investigations on looseness detection in bolted assemblies based on traditional methods and machine learning algorithms. The working principle, advantages, challenges, and applications of the aforementioned methods are also detailed in this paper. Apart from these investigations, the latest studies on the Internet of Things-based health monitoring of structures are also reviewed to explore their adaptability in remote monitoring of bolted connections for damage detection in the future.

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Section: Neural Networkmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

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“…An adaptive neurofuzzy inference system (ANFIS) was configured using a strain gauge sensor for TCM, and the model was demonstrated to be accurate [20]. According to the work of Wiciak-Pikuła et al [21], Matrix composites materials that are difficult to cut. One of the tools used to forecast tool wear or surface roughness in machining is artificial neural networks (ANN).…”

Section: Introductionmentioning

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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“…In this study, the scale-reduced parts of the brake drum were taken as the research object. Under the premise of no metal coating on the solid, SiC p /A357 composite with excellent wear resistance [ 36 ] as the outer wear-resisting layer and 7050 aluminum alloy as the inner skeleton were used for composite casting, so that the two can be fully wetted into a metallurgical combination. The influence of different wear-resisting layer sizes and thicknesses on surface temperature change was simulated by ProCAST software.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation and Experimental Study on Compound Casting of Layered Aluminum Matrix Composite Brake Drum

2021

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The requirements of high-strength, wear-resistance and lightweight of brake drums have been continually increasing in recent years and any specific aluminum alloy or particle-reinforced aluminum matrix composites may not satisfy all the demands. Combining dissimilar materials to play their respective advantages is a solution to this problem. In this study, a compound casting method was used to combine solid SiCp/A357 composite and a liquid 7050 aluminum alloy to prepare an aluminum matrix composite with a layered structure. The ProCAST numerical simulation software was used to predict the heat transfer in compound casting process and guide the preheating temperature of the wear-resistant ring in the experiment. An Optical Microscope (OM) and Scanning Electron Microscope (SEM) were used to observe microstructures around the solid–liquid bonding interface, the element distribution and phase component of which were analyzed by Energy Dispersive Spectroscopy (EDS) and mechanical properties were evaluated by microhardness and shear tests. The results showed that the interface of the layered aluminum matrix composite prepared by this method achieved complete metallurgical bonding and a transition zone formed on the solid surface. After T6 heat treatment, the average shear strength of the interface increased from 19.8 MPa to 33.8 MPa.

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Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling

Cited by 18 publications

References 35 publications

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

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

Numerical Simulation and Experimental Study on Compound Casting of Layered Aluminum Matrix Composite Brake Drum

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