随机性因素对机器人单元性能的影响分析

doi:10.3969/j.issn.1007-7375.e17-1009

工业工程 ›› 2017, Vol. 20 ›› Issue (6): 84-89,95.doi: 10.3969/j.issn.1007-7375.e17-1009

随机性因素对机器人单元性能的影响分析

谢洁明, 毛宁, 陈庆新

广东工业大学广东省计算机集成制造系统重点实验室, 广东广州 510006

收稿日期:2017-01-04 出版日期:2017-12-30 发布日期:2018-01-09
作者简介:谢洁明(1993-),男,广东省人,硕士研究生,主要研究方向为制造系统规划设计.
基金资助:
国家自然科学基金资助项目（51375098，61573109）；广东省教育厅联合培养研究生示范基地资助项目（2013JDXM30）

Modeling Robotic Cell and Analyzing the Influences of Stochastic Factors

XIE Jieming, MAO Ning, CHEN Qingxin

Key Laboratory of Computer Integrated Manufacturing System of Guangdong Province, Guangdong University of Technology, Guangzhou 510006, China

Received:2017-01-04 Online:2017-12-30 Published:2018-01-09

摘要/Abstract

摘要： 为了分析机器人单元性能参数的随机性对系统性能的影响，针对一个典型的机器人制造单元，利用eM-Plant仿真平台建立仿真模型，并根据不同情形设计和实施了一系列实验。仿真结果显示工件到达时间等参数的随机性降低了系统性能。通过改变输入缓冲区容量等系统配置方法，可提高随机环境下系统的性能。说明可通过调整配置参数提高具有不确定环节的机器人制造单元的系统性能指标。

关键词: 机器人单元, 仿真建模, 随机因素, 性能指标

Abstract: To analyze the influence that random factors of robotic cell's capabilities produce on system performance, a simulation model is established on the platform of simulation software eM-Plant for a classic robotic manufacturing cell and a series of experiments have been carried out. The results show that the systematic properties of the robotic cell will decline with the influence of random parameters such as arriving interval of workpiece, but it declines through changing system configuration methods such as inputting buffer's capacity. It is concluded that systematic capacity indexes of robotic manufacturing cell with indeterminate step can be improved through adjusting configuration parameters.

Key words: robotic cell, simulation and modeling, random factors, capacity index

中图分类号:

TP391

谢洁明, 毛宁, 陈庆新. 随机性因素对机器人单元性能的影响分析[J]. 工业工程, 2017, 20(6): 84-89,95.

XIE Jieming, MAO Ning, CHEN Qingxin. Modeling Robotic Cell and Analyzing the Influences of Stochastic Factors[J]. Industrial Engineering Journal , 2017, 20(6): 84-89,95.

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随机性因素对机器人单元性能的影响分析

Modeling Robotic Cell and Analyzing the Influences of Stochastic Factors

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