Study On The Human Error Mechanism And Performance Optimization Method Of Manual Autonomous Assembly Unit Considering The Mental Workload

In today's highly competitive market,product quality is the lifeblood of manufacturing companies.With the development of science and technology,the improvement equipment reliability and operating environment,the proportion of the quality of accidents caused by the hardware products directly is decreasing,while the quality accidents triggered by the operator actually is an upward momentum,human error played an increasingly important role in the accident.Since the human error has been raised,most of the scholars focused on human error on the mode and cause analysis.At present,the mechanisms of human errors people were caused by mental workload was poorly understood.Therefore it is necessary to study on the affect of human error caused by mental workload and what is the level of mental workload to maintain to obtain the performance optimal of the manufacturing enterprise.Firstly,by drawing on the NASA-TLX scale and the methods of use it,design the site questionnaire,and conducting a survey for the autonomous assembly unit's operation staff.The purpose of this study is to investigate the factors that affect the mental workload of the autonomous assembly unit,which is the basis for the design of the simulation experiment.Secondly,the reaction time and correct rate of the personnel in the experimental operation were studied under the dual task experiments.The results showed that the complexity of the task,the reaction time of the test,and the individual factors all had a quantitative effect on the level of the mental workload.In order to make the performance optimization of the manual autonomous assembly unit,considering the complexity of the autonomous assembly unit,the time pressure model is used to model the mental workload,and the experience of the operator is considered.A balanced optimization objective function is established for the quality and efficiency of the algorithm.The application of adaptive weight particle swarm optimization algorithm is used to solve the function.Finally,a numerical example is used to verify the model.