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Multi-response optimization and machinability research of forging and heat treatment parameters in piston production

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Abstract

In this study, the effect of sulfur content of the material, forging temperature, quenching medium, tempering temperature and cooling rate on mechanical properties and machinability in forged DIN42CrMoS4 steel used as engine part were investigated. Hardness measurements, Charpy V notch impact tests and tensile tests were performed to determine the mechanical properties of the investigated pistons. The effects of forging parameters on machinability were analyzed by evaluating the cutting forces. The cutting force measurements were applied with a Kistler piezoelectric drill dynamometer and amplifier. The response surface method (RSM) was carried out to evaluate the results of the experiments and to reveal the relationship between them. The accuracy of the model based on the ANOVA tables was investigated, and regression equations were obtained. A multi-response optimization was performed to optimize forging and heat treatment parameters for minimum cutting force and optimum mechanical properties.

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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This study was supported by Tubitak project number 3150896 within the scope of 1501 Tubitak Research Project. The authors thank the Advanced Material Laboratory at Kocaeli University Technopark for supporting experiments. In addition, the authors remember Prof. Dr. Mustafa Çöl, who contributed to this study, with respect.

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E.Y., F.K., F.A., and T.M designed the study. E.Y. and Ş.D. performed the experiments. E.Y modeled the experimental results statistically. F.K carried out microstructure analysis. E.Y. and F.K. wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Eser Yarar.

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Yarar, E., Koç, F.G., Angigün, F. et al. Multi-response optimization and machinability research of forging and heat treatment parameters in piston production. Multiscale and Multidiscip. Model. Exp. and Des. 6, 305–317 (2023). https://doi.org/10.1007/s41939-022-00134-9

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  • DOI: https://doi.org/10.1007/s41939-022-00134-9

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