Constrained groove pressing for sheet metal processing

2016 ◽  
Vol 84 ◽  
pp. 403-462 ◽  
Author(s):  
Amit Kumar Gupta ◽  
Tejveer Simha Maddukuri ◽  
Swadesh Kumar Singh
Keyword(s):  
Sheet Metal ◽  
Metal Processing

scholarly journals Study on nesting scheduling for sheet metal processing (Improvement of nesting method by parts reallocation)

2015 ◽  
Vol 81 (825) ◽  
pp. 14-00640-14-00640 ◽  
Author(s):  
Tatsuhiko SAKAGUCHI ◽  
Kohki MATSUMOTO ◽  
Yoshiaki SHIMIZU
Keyword(s):  
Sheet Metal ◽  
Metal Processing

302 Study on Integration of Operation Planning and Scheduling for Sheet Metal Processing

2013 ◽  
Vol 2013 (0) ◽  
pp. 29-30
Author(s):  
Tatsuro Murakami ◽  
Tatsuhiko Sakaguchi ◽  
Yoshiaki Shimizu
Keyword(s):  
Sheet Metal ◽  
Operation Planning ◽  
Planning And Scheduling ◽  
Metal Processing

scholarly journals Cloud manufacturing system for sheet metal processing

2017 ◽  
Vol 28 (6-8) ◽  
pp. 524-537 ◽  
Author(s):  
Petri Helo ◽  
Yuqiuge Hao
Keyword(s):  
Sheet Metal ◽  
Manufacturing System ◽  
Cloud Manufacturing ◽  
Metal Processing

Nesting Scheduling in Sheet Metal Processing Based on Coevolutionary Genetic Algorithm in Different Environments

2018 ◽  
Vol 12 (5) ◽  
pp. 730-738 ◽  
Author(s):  
Tatsuhiko Sakaguchi ◽  
◽  
Kohki Matsumoto ◽  
Naoki Uchiyama
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Processing Time ◽  
Factors Affecting ◽  
Fitness Value ◽  
Evolutionary Genetic ◽  
Scheduling Method ◽  
Grouping Method ◽  
Metal Processing ◽  
Coevolutionary Genetic Algorithm

In sheet metal processing, nesting and scheduling are important factors affecting the efficiency and agility of manufacturing. The objective of nesting is to minimize the waste of material, while that of scheduling is to optimize the processing sequence. As the relation between them often becomes a trade-off, they should be considered simultaneously for the efficiency of the total manufacturing process. In this study, we propose a co-evolutionary genetic algorithm-based nesting scheduling method. We first define a cost function as a fitness value, and then we propose a grouping method that forms gene groups based on the processing layout and processing time. Finally, we validate the effectiveness of the proposed method through computational experiments.

Sign in / Sign up

Export Citation Format

Share Document