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Enhancing optimization and reducing machining time of freeform shapes through modeling, simulation, and Taguchi design of experiments with artificial neural networks

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Abstract

Freeform machining is one of the trickier machining operations characterized by prolonged processing durations, which lead to high energy consumption. Addressing this issue is imperative for enhancing industrial efficacy and minimizing energy consumption. This article presents modeling, simulation, optimizing, and an algorithm for reducing the long machining time of freeform geometric shapes using the Taguchi optimization technique and an artificial neural network (ANN). Firstly, the models (i.e., stock and impeller) were designed in Solidworks and imported into simulation software (SolidCAM) for machining simulation. The Taguchi L9 (3^3) was used to arrange the data using spindle speed, feed rate, and cutter diameter as the cutting conditions, and the simulation was conducted using the corresponding settings, and the machining times were recoded. Then the optimized settings were obtained, and other statistical analyses were conducted and discussed. Also, the data generated through the application of the Taguchi method was utilized to develop an advanced ANN algorithm. In addition, a second-order regression model was constructed using the Bayesian approach. This algorithm achieved a perfect 100% prediction accuracy for both the training and testing phases. These methods resulted in reducing the machining time for roughing operations from 69.68 to 33.07 min, and the ANN and other statistical results shown and discussed in this article show the effectiveness of the methods.

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Acknowledgements

The authors thank the National Natural Science Foundation of China for providing financial support, and the Digital Twins Modeling Theory of Intelligent Operation and Maintenance for High-end Equipment.

Funding

This work was funded by the National Natural Science Foundation of China (NSFC) (Grant Nos. 51975407 and 51975402) and the Digital Twins Modeling Theory of Intelligent Operation and Maintenance for High-end Equipment (Grant No. 2022YFB3303601).

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Authors and Affiliations

  1. School of Mechanical Engineering, Tianjin University, No.135 YaGuan Road, Jinnan District, Tianjin, 300354, People’s Republic of China

    Usman Haladu Garba, Taiyong Wang, Ying Tian & Chong Tian

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  1. Usman Haladu Garba
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  2. Taiyong Wang
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  3. Ying Tian
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Contributions

All authors contributed to the study conception and design. Conceptualization, software, validation, formal analysis, data curation, and writing (original draft, review, and editing) were performed by Usman Haladu Garba. Funding acquisition, supervision, resources data curation, and methodology investigation were carried out by Taiyong Wang and Ying Tian. Validation and investigation were performed by Chong Tian. All authors read and approved the final manuscript.

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Correspondence to Usman Haladu Garba.

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Garba, U.H., Wang, T., Tian, Y. et al. Enhancing optimization and reducing machining time of freeform shapes through modeling, simulation, and Taguchi design of experiments with artificial neural networks. Int J Interact Des Manuf (2024). https://doi.org/10.1007/s12008-024-01872-5

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