{"title":"利用OpenMP并行计算加速基于粒子群算法的NURBS刀具路径进给速度优化","authors":"Rafał Szczepański, Krystian Erwiński, M. Paprocki","doi":"10.1109/MMAR.2017.8046866","DOIUrl":null,"url":null,"abstract":"Over the last few years generation of a time-optimal feedrate profile for CNC machines has recieved significant attention. This is a difficult optimization problem usually requiring long computation time. In the proposed solution, optimization is performed by parallel Particle Swarm Optimization with Augmented Lagrangian constraint handling technique. In order to decrease computation time the authors previously developed algorithm was reimplemented using Open Multi-processing. OpenMP utilizes the ability of modern CPUS to run multiple threads and reduce the algorithm's runtime by using parallel processing. The performance gain (speed-up) of the algorithm parallelized on a multi-core system has been tested. The experimental results of a time-optimal feedrate profile generated using an example toolpath are presented to illustrate the capabilities of parallel computation to improve the algorithm's performance.","PeriodicalId":189753,"journal":{"name":"2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Accelerating PSO based feedrate optimization for NURBS toolpaths using parallel computation with OpenMP\",\"authors\":\"Rafał Szczepański, Krystian Erwiński, M. Paprocki\",\"doi\":\"10.1109/MMAR.2017.8046866\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Over the last few years generation of a time-optimal feedrate profile for CNC machines has recieved significant attention. This is a difficult optimization problem usually requiring long computation time. In the proposed solution, optimization is performed by parallel Particle Swarm Optimization with Augmented Lagrangian constraint handling technique. In order to decrease computation time the authors previously developed algorithm was reimplemented using Open Multi-processing. OpenMP utilizes the ability of modern CPUS to run multiple threads and reduce the algorithm's runtime by using parallel processing. The performance gain (speed-up) of the algorithm parallelized on a multi-core system has been tested. The experimental results of a time-optimal feedrate profile generated using an example toolpath are presented to illustrate the capabilities of parallel computation to improve the algorithm's performance.\",\"PeriodicalId\":189753,\"journal\":{\"name\":\"2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR)\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMAR.2017.8046866\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMAR.2017.8046866","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Accelerating PSO based feedrate optimization for NURBS toolpaths using parallel computation with OpenMP
Over the last few years generation of a time-optimal feedrate profile for CNC machines has recieved significant attention. This is a difficult optimization problem usually requiring long computation time. In the proposed solution, optimization is performed by parallel Particle Swarm Optimization with Augmented Lagrangian constraint handling technique. In order to decrease computation time the authors previously developed algorithm was reimplemented using Open Multi-processing. OpenMP utilizes the ability of modern CPUS to run multiple threads and reduce the algorithm's runtime by using parallel processing. The performance gain (speed-up) of the algorithm parallelized on a multi-core system has been tested. The experimental results of a time-optimal feedrate profile generated using an example toolpath are presented to illustrate the capabilities of parallel computation to improve the algorithm's performance.