Evolutionary Path Generation for Reduction of Thermal Variations in Thermal Spray Coating

被引:0
|
作者
Hegels, Daniel [1 ]
Mueller, Heinrich [1 ]
机构
[1] TU Dortmund Univ, Dept Comp Sci 7, D-44227 Dortmund, Germany
来源
GECCO'13: PROCEEDINGS OF THE 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2013年
关键词
Path planning; Spray coating; Optimization; Evolutionary Algorithms;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Thermal spraying is a production process which consists of spraying hot material onto a workpiece surface in order to form a coating of a desired thickness. This paper describes a path generation algorithm for industrial robot-based thermal spraying which generates the desired coating as well as keeps the thermal variation on the object surface during the process low. The problem is formulated as a discrete optimization problem which includes the quality of the particle coating and the physics of heat induction, heat diffusion and cooling of the surface. The optimization problem is solved by an Evolutionary Algorithm. By specific mutation operators, self-adaptation, and dropping the concept of generations, an improvement of the quality of the results of over 25% compared to standard operations is achieved. The evolutionary results overall outperform the solutions generated by the often-used strategy of direction-parallel paths.
引用
收藏
页码:1277 / 1284
页数:8
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