AI-based approach for predicting the machinability under consideration of material batch deviations in turning processes

Lutz B, Kißkalt D, Regulin D, Franke J (2020)


Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 93

Pages Range: 1382-1387

Conference Proceedings Title: Procedia CIRP

Journal Issue: 53rd CIRP Conference on Manufacturing Systems 2020

URI: https://www.sciencedirect.com/science/article/pii/S2212827120307344?via=ihub

DOI: 10.1016/j.procir.2020.04.100

Open Access Link: https://www.sciencedirect.com/science/article/pii/S2212827120307344?via=ihub

Abstract

A significant difference in the machinability of hard-to-machine materials can be observed among different batches of the same specified material. Thus, for cost-efficient machining, the ideal point of operation may vary for each batch. To investigate this, more than 1000 experiments at different cutting conditions with ten material batches are carried out. This data is used to train a support vector machine, capable of predicting a machinability index based on the material batch and the cutting conditions. The derived model is implemented as a cloud-based service, enabling its integration into a smart manufacturing assistant. It can be used for process optimization by predicting the machinability for given situations and, thus, finding the point of operation with favorable machinability.

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How to cite

APA:

Lutz, B., Kißkalt, D., Regulin, D., & Franke, J. (2020). AI-based approach for predicting the machinability under consideration of material batch deviations in turning processes. Procedia CIRP, 93(53rd CIRP Conference on Manufacturing Systems 2020), 1382-1387. https://doi.org/10.1016/j.procir.2020.04.100

MLA:

Lutz, Benjamin, et al. "AI-based approach for predicting the machinability under consideration of material batch deviations in turning processes." Procedia CIRP 93.53rd CIRP Conference on Manufacturing Systems 2020 (2020): 1382-1387.

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