Author
Listed:
- Feiyan Han
- Chuanwei Zhang
- Wu Zhang
- Wei Guo
- Bin Feng
Abstract
In order to overcome the problem that the existing tool selection method only considers how to select the tool on a given single or multiple process surfaces, and the tool sequence of the whole machining process obtained by this existing method is not necessarily optimal. In this paper, a synchronous optimisation method about the tool sequence selection and the depth distribution of multi-state intermediate surfaces is proposed. Firstly, a mathematical model is established to optimise the depth distribution of the process surfaces with the minimum processing time as the objective function, and then it is transformed into an optimisation model which is related to the tool sequence through the parametric analysis, and the constraint equation is established based on the relationship between the parameter variables which associates with the tool, here the parameter variables contain the machinable area of a tool, the number of tool reuse times and the maximum cutting depth. Secondly, a two-step method of calculation the largest available tool set and the optimal tool sequence is given for solving this optimisation model. Finally, the impeller machining is taken as an example, the optimised depth distribution of process surfaces for impeller channel machining is calculated. A comparison machining of the optimised process surfaces and un-optimised process surfaces is carried out. The result shows that the optimised method presented in this paper reduces the total processing time by 126 s and improves the machining efficiency by 6.5% for a single impeller channel.
Suggested Citation
Feiyan Han & Chuanwei Zhang & Wu Zhang & Wei Guo & Bin Feng, 2020.
"A dynamic synchronous optimisation method of tool sequence selection and multi-state process models depth distribution for high efficiency machining,"
International Journal of Production Research, Taylor & Francis Journals, vol. 58(20), pages 6145-6158, October.
Handle:
RePEc:taf:tprsxx:v:58:y:2020:i:20:p:6145-6158
DOI: 10.1080/00207543.2019.1668069
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