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<p>Despite their widespread adoption in manufacturing, industrial robots remain constrained by the ‘Six Losses’, leading to significant inefficiencies throughout production processes. To address this challenge, this study proposes and implements an integrated optimization scheme applicable to diverse robot types and multi-application scenarios. The scheme employs real-time modulation of robot motion speed to enhance operational performance and increase Overall Equipment Effectiveness (OEE).</p><p>First, a digital twin (DT) system was developed using Unity3D and Visual Studio to facilitate visualization and simulation-based validation of robotic operations. The DT system demonstrated robust performance, achieving real-time responsiveness within 110s and synchronization accuracy within 1&#xa0;mm. Subsequently, a robotic performance optimization model based on the Hybrid Genetic Algorithm (HGA) was developed. This model enables real-time adjustment of robot speed based on sensor-acquired environmental parameters, particularly the negative pressure, thereby maintaining near-optimal operational performance. To address synchronization discrepancies between the digital twin and the physical system, an error compensation mechanism was introduced. Compensated speed commands were first validated virtually within the DT environment and subsequently deployed to the robots upon validation. Case study results demonstrated that this integrated optimization scheme significantly enhanced the OEE of an assembly robot workstation, yielding improvements of 3.5% to 9.7%. Although the study is framed as optimizing OEE, the primary focus is on improving the performance rate through robot motion speed optimization, while the availability and quality rate remain stable throughout the study.</p>

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Hybrid genetic algorithm and digital twin for OEE optimization for industrial robots

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Ruixu Liang,
  • Wenjuan Zhang,
  • Jin Zhao,
  • Boyang Zhao,
  • Xiang Yu,
  • Jianbo Fan

摘要

Despite their widespread adoption in manufacturing, industrial robots remain constrained by the ‘Six Losses’, leading to significant inefficiencies throughout production processes. To address this challenge, this study proposes and implements an integrated optimization scheme applicable to diverse robot types and multi-application scenarios. The scheme employs real-time modulation of robot motion speed to enhance operational performance and increase Overall Equipment Effectiveness (OEE).

First, a digital twin (DT) system was developed using Unity3D and Visual Studio to facilitate visualization and simulation-based validation of robotic operations. The DT system demonstrated robust performance, achieving real-time responsiveness within 110s and synchronization accuracy within 1 mm. Subsequently, a robotic performance optimization model based on the Hybrid Genetic Algorithm (HGA) was developed. This model enables real-time adjustment of robot speed based on sensor-acquired environmental parameters, particularly the negative pressure, thereby maintaining near-optimal operational performance. To address synchronization discrepancies between the digital twin and the physical system, an error compensation mechanism was introduced. Compensated speed commands were first validated virtually within the DT environment and subsequently deployed to the robots upon validation. Case study results demonstrated that this integrated optimization scheme significantly enhanced the OEE of an assembly robot workstation, yielding improvements of 3.5% to 9.7%. Although the study is framed as optimizing OEE, the primary focus is on improving the performance rate through robot motion speed optimization, while the availability and quality rate remain stable throughout the study.