Diffusion Wear of Al2O3 Ceramic Tools when Cutting Materials Based on Thermodynamics

Shao, Fang; Wang, Yuting; Xiao, Long; Xiao, Yingqun; Yang, Renzeng; Wang, Renwei; Cai, Qun

doi:10.12783/dtetr/apetc2017/11123

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Experiment showed that with the increase in the cutting speed, carbide cobalt element will decompose into tungsten and carbon diffusing to the steel when cutting temperature is over 800 °C, which makes the tool wear exacerbation. 30…”

Section: Experiments and Discussionmentioning

confidence: 99%

A novel monitoring method for turning tool wear based on support vector machines

Yang

Wang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Tool wear monitoring is critical for ensuring product quality and productivity. This article presents a novel tool wear prediction model based on improved least squares support vector machine method, combined with leave-one-out technique and Nelder–Mead technique. Leave-one-out is applied to tune the regularization factor and radial basis function kernel parameter of least squares support vector machine for enhancing the global search ability. Nelder–Mead is applied to raise the local search ability. The optimized least squares support vector machine based tool wear prediction model is constructed by learning the highly nonlinear correlationships between tool cutting conditions and actual tool wear. The effectiveness of the proposed prediction model is validated by experiments. Compared with particle swarm optimization algorithm-based least squares support vector machine and basic least squares support vector machine, Nelder–Mead-leave-one-out-based least squares support vector machine demonstrates a better performance in prediction accuracy, generalization, robustness, and convergence. The average accuracy obtained in tests for tool wear prediction is above 97%. This model provides theoretical basis for the machining condition configuration in the actual processing.

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Section: Experiments and Discussionmentioning

confidence: 99%