工业工程

工业工程 ›› 2024, Vol. 27 ›› Issue (2): 67-73,97.doi: 10.3969/j.issn.1007-7375.230183

• 人因工程 • 上一篇    下一篇

面向复杂装配任务的脑力负荷综合评估方法

余琦玮1, 唐为昊2, 耿洁3   

  1. 1. 中国计量大学 质量与标准化学院，浙江 杭州 310018;
    2. 浙江工业大学 机械工程学院，浙江 杭州 310023;
    3. 浙江财经大学 中国政府管制研究院，浙江 杭州 310018
  • 收稿日期:2023-10-05 发布日期:2024-04-29
  • 作者简介:余琦玮(1978-),女,浙江省人,讲师,硕士,主要研究方向为人因工程
  • 基金资助:
    浙江省自然科学基金资助项目(LQ19E050007)

A Synthetic Evaluation Method for Mental Workload in Complex Assembly Task

YU Qiwei1, TANG Weihao2, GENG Jie3   

  1. 1. College of Quality and Standardization, China Jiliang University, Hangzhou 310018, China;
    2. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310018, China;
    3. China Institute of Regulation Research, Zhejiang University of Finance & Economics, Hangzhou 310018, China
  • Received:2023-10-05 Published:2024-04-29

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摘要： 复杂装配作业脑力负荷过高会降低装配绩效和质量，甚至引发安全事故。因此，对装配任务脑力负荷进行准确测量与评估，确定作业人员的脑力负荷水平，是生产系统优化的重要依据。本文针对复杂装配任务的脑力负荷评估问题，设计了基于Lego积木模拟的装配任务实验，使用主观测量法、绩效测量法和生理测量法采集了27项指标数据，分析了各项数据对脑力负荷变化的敏感性，发现绩效测量指标、主观测量指标、注视次数、总注视时间、扫视次数、总扫视时间和平均瞳孔直径这7项指标是复杂装配任务脑力负荷的有效测量指标。基于有效测量指标，采用BP (back propagation) 神经网络和贝叶斯线性判别两种建模方法，构建了装配任务脑力负荷综合评估模型。研究结果表明，使用因子分析将7维指标转化为二维综合指标作为主成分输入，使用归一化共轭梯度法为训练算法的BP神经网络模型是装配作业脑力负荷的最佳综合评估模型，其判别准确率达到84.80%。本文提出的脑力负荷测量与评估方法可为装配作业脑力负荷评定和优化提供参考依据。

关键词: 复杂装配, 脑力负荷, 综合评估

Abstract: Excessive mental workload in complex assembly operations can reduce assembly performance and quality, and may even lead to safety accidents. Therefore, accurately measuring and evaluating the mental workload of assembly tasks to determine the level of mental workload for operators is an important basis for optimizing production systems. In order to evaluate the mental workload in complex assembly tasks, assembly task experiments based on Lego block simulation are designed. Data from 25 indicators are collected by subjective measurement, performance measurement and physiological measurement to analyze the sensitivity of each indicator to changes of mental workload. Results show that seven indicators comprised of the performance measurement indicator, subjective measurement indicator, fixation times, total fixation duration, scanning times, total scanning duration and average pupil diameter are the effective measurement ones for the mental workload in complex assembly tasks. Based on these effective measurement indicators, a synthetic evaluation model for assembly task mental workload is established by two modeling methods, namely BP neural network and Bayesian linear discrimination. Findings show that the BP neural network model, which uses factor analysis to transform the 7-D indicators into the 2-D synthetic indicators as the principal component input, and employs the normalized conjugate gradient method as the training algorithm, is the optimal synthetic evaluation model for mental workload in assembly tasks, with the accuracy of 84.80%. The proposed method for measuring and evaluating mental workload can provide reference for the evaluation and optimization of mental workload in assembly operations.

Key words: complex assembly, mental workload, synthetic evaluation

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