Artificial Neural Network models for tool wear prediction during Aluminium Matrix Composite milling

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

doi:10.1109/metroaerospace48742.2020.9160064

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…The researchers [71,72] developed a multilayer perceptron (MLP) artificial neural network (ANN) models to forecast tool wear during Al/SiC milling. In micro milling machining of SiC/Al composites, SiC particles result in fast abrasive wear on the cutting tool [73,74].…”

Section: Aluminium Reinforced With Silicon (Al/si)mentioning

confidence: 99%

Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing – Present Scenario and Future Prospects

Gutema,

Lemu

2024

Preprint

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This study provides an in-depth review of the available literature on metal composite material machining, focusing on traditional machining operations such as turning, milling, shaping, drilling, and grinding. The motivation for the study is that this composite type has emerged as a vital material in applications requiring functionality, resilience, cost-effective operation, low consumption of energy and lightweight. Despite its superior characteristics, the manufacturing complexity and poor machinability of metal matrix composites have been the primary barriers to its adoption. Nevertheless, there is a reduction of exceptional efficiency, increase in surface roughness, tool wear and so on in metal matrix composites throughout machining. As a result, this review gives a comprehensive investigation of metal matrix composite machining and effect on process parameters and procedures. Furthermore, this study distinguishes metal matrix composites towards sustainable manufacturing and the metals employed by the researchers, as well as the gaps discovered for future recommendations in metal-based composites.

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Section: Aluminium Reinforced With Silicon (Al/si)mentioning

confidence: 99%

Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing – Present Scenario and Future Prospects

Gutema,

Lemu

2024

Preprint

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“…Wiciak-Pikuła et al [41] employed neural networks to predict the wear of cutting tools during milling of aluminum matrix composites. MLPs were employed to associate the wear level of the tool with acceleration and cutting forces.…”

Section: Scime and Beuthmentioning

confidence: 99%

Short Survey of Artificial Intelligent Technologies for Defect Detection in Manufacturing

Papageorgiou

Θεοδοσίου

Margetis

et al. 2021

2021 12th International Conference on Information, Intelligence, Systems &Amp; Applications (IISA)

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Zero Defect Manufacturing (ZDM) can be described as the set of methodologies and strategies for the elimination of defective components during production, and is one of the main goals of Industry 4.0. ZDM is very appealing to industries grace to the reduction of operational costs associated with defective components. Efficient defect detection in modern production lines may benefit from Artificial Intelligence (AI) technologies in numerous stages of the manufacturing process. This paper presents a short review of AI technologies employed during product inspection and quality assessment. Indicative applications are reported to demonstrate that AI in its many flavors may be efficiently integrated into production environments and pave the way towards ZDM.

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“…This difficulty stems from the complex nature of the composites and the effects of various compositional transformations, particularly those involving the integration of silicon carbide (SiC). Existing forecasting models, such as simple artificial neural networks (ANNs), have shown some success in this area [22][23][24][25], but there is a pressing need to explore new and potentially more accurate computational modeling approaches for predicting wear rates in these Al/SiC metal matrix composites (MMCs). The previous literature has focused on the implementation of using conventional machine learning models for predicting wear rate [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

Mishra

Jatti

2023

Lubricants

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This research paper delves into an innovative utilization of neurosymbolic programming for forecasting wear rates in aluminum-silicon carbide (Al/SiC) metal matrix composites (MMCs). The study scrutinizes compositional transformations in MMCs with various weight percentages of SiC (0%, 3%, and 5%), employing comprehensive spectroscopic analysis. The effect of SiC integration on the compositional distribution and ratio of elements within the composite is meticulously examined. In a novel move for this field of research, the study introduces and applies neurosymbolic programming as a novel computational modeling approach. The performance of this cutting-edge methodology is compared to a traditional simple artificial neural network (ANN). The neurosymbolic algorithm exhibits superior performance, providing lower mean squared error (MSE) values and higher R-squared (R2) values across both training and validation datasets. This highlights its potential for delivering more precise and resilient predictions, marking a significant development in the field. Despite the promising results, the study recognizes that the performance of the model might vary based on specific characteristics of the composite material and operational conditions. Thus, it encourages future studies to authenticate and expand these innovative findings across a wider spectrum of materials and conditions. This research represents a substantial advancement towards a more profound understanding of wear rates in Al/SiC MMCs and emphasizes the potential of the novel neurosymbolic programming in predictive modeling of complex material systems.

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Artificial Neural Network models for tool wear prediction during Aluminium Matrix Composite milling

Cited by 9 publications

References 10 publications

Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing – Present Scenario and Future Prospects

Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing – Present Scenario and Future Prospects

Short Survey of Artificial Intelligent Technologies for Defect Detection in Manufacturing

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

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