Different Phases in Manual Materials Handling Have Different Performance Criteria: Evidence From Multi-Objective Optimization

Author:

Zheng Size1,Li Tong2,Li Qingguo3,Liu Tao1

Affiliation:

1. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China

2. Department of Biomedical Engineering, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077

3. Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON K7L3N6, Canada

Abstract

Abstract A manual material handling task involves the phases of reaching, lifting, unloading, and standing up (RLUS). Understanding the mechanisms of manual material handling is important for occupational health and the development of assist devices. Predictive models are becoming popular in exploring which performance criterion is appropriate in the lifting phase. However, limited attempts have been performed on the other phases. The associated performance criterion for predicting other phases is unknown. In this study, an optimization model for predicting RLUS has been developed with the multi-objective optimization method. Two performance criteria (minimum dynamic effort and maximum balance) were studied to explore their importance in each phase. The result shows that maximum balance leads to joint angle errors 27.6% and 40.9% smaller than minimum dynamic effort in reaching and unloading phases, but 40.4% and 65.9% larger in lifting and standing up phases. When the two performance criteria are combined, the maximum balance could help improve the predicting accuracy in the reaching, lifting, and unloading phases. These findings suggest that people prefer different performance criteria in different phases. This study helps understand the differences in motion strategies in manual materials handling (MMH), which would be used to develop a more accurate predictive model.

Funder

National Natural Science Foundation of China

Publisher

ASME International

Subject

Physiology (medical),Biomedical Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Integrated Lifting Predictive Model for Lumbar Injury Risk Assessment;2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON);2023-12-08

2. Multi-phase optimisation model predicts manual lifting motions with less reliance on experiment-based posture data;Ergonomics;2022-11-25

3. Optimization-based biomechanical lifting models for manual material handling: A comprehensive review;Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine;2022-07-26

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