Understanding Machining Process Parameters and Optimization of High-Speed Turning of NiTi SMA Using Response Surface Method (RSM) and Genetic Algorithm (GA)

Zhao, Yanzhe; Cui, Li; Sivalingam, Vinothkumar; Sun, Jie

doi:10.3390/ma16175786

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…The physical, thermal, and mechanical properties of the β-NiTi of the material are shown in Table 3, respectively. The research plan to determine the impact of three independent factors, namely, feed per tooth ( f z [mm/tooth]), radial depth of cut (a e [mm]), and cutting speed (v c [m/min]), on the values of the selected factors Sa and Sz (i.e., 3D areal surface texture parameters), of the surface roughness and cutting force components, was developed according to Response Surface Design [9,26]. The Box-Behnken experimental design approach was adopted during this research.…”

Section: Dmentioning

confidence: 99%

“…Historically, the industry's priority has been obtaining high-quality parameters for products made of the NiTi alloy, with production cost as a secondary concern. It can be anticipated that, in the future, due to the need to reduce and optimize production costs, research efforts will be focused on shaping NiTi alloy elements using methods that ensure maximum efficiency while maintaining the quality of the machined surface [19,21,[23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Cutting Forces and Geometric Surface Structures in the Milling of NiTi Alloy

Kowalczyk

2024

Materials

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This paper presents a study of the total cutting force used and selected parameters of the geometric structure of the surface (e.g., Sa, Sz) during the end milling process of NiTi alloy. The input parameters included are cutting speed (vc), feed per tooth (fz), and radial depth of cut (ae). A Box–Behnken experimental design was employed to conduct the research. The obtained experimental results were utilized within the framework of a response surface methodology (RSM) to develop mathematical and statistical models capable of predicting cutting force components and selected 3D surface parameters. These models provide valuable insights into the relationships between the cutting parameters and the output variables, facilitating the optimization of the NiTi alloy milling process. The findings of this study contribute to a better understanding of the behavior of NiTi alloy during the milling process and offer information for process optimization. By employing a Box–Behnken experimental design, it was possible to investigate the effects of different parameter combinations on the components of total cutting force and selected 3D surface parameters according to ISO 25178, thus aiding in the identification of optimal milling conditions to achieve desired outcomes in the machining of NiTi alloy.

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Section: Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Cutting Forces and Geometric Surface Structures in the Milling of NiTi Alloy

Kowalczyk

2024

Materials

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Advancing engineering frontiers with NiTi shape memory alloys: A multifaceted review of properties, fabrication, and application potentials

Amadi,

Mohyaldinn,

Ridha

et al. 2024

Journal of Alloys and Compounds

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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques

Val,

Lambán,

Lucia

et al. 2024

Applied Sciences

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Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. This method ensures greater efficiency and accuracy than conventional techniques, providing a scalable, resource-efficient solution for reliable and insightful tool wear predictions. The results obtained from the dataset of an insert tool can be extrapolated to other milling inserts and demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. The integration of uncertainty awareness in the predictive outputs is a unique feature of this research and enhances decision-making for smarter manufacturing. This proactive approach enhances operational efficiency and reduces overall production costs. Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction.

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Understanding Machining Process Parameters and Optimization of High-Speed Turning of NiTi SMA Using Response Surface Method (RSM) and Genetic Algorithm (GA)

Cited by 4 publications

References 39 publications

Analysis of Cutting Forces and Geometric Surface Structures in the Milling of NiTi Alloy

Analysis of Cutting Forces and Geometric Surface Structures in the Milling of NiTi Alloy

Advancing engineering frontiers with NiTi shape memory alloys: A multifaceted review of properties, fabrication, and application potentials

Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques

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