Research Article
Predicting Optimum Work Force for a Tool and Cutter Grinding Machine using Posture Analysis
@INPROCEEDINGS{10.4108/eai.7-12-2021.2314627, author={Madhan Mohan G and Vigneswaran C}, title={Predicting Optimum Work Force for a Tool and Cutter Grinding Machine using Posture Analysis}, proceedings={Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India}, publisher={EAI}, proceedings_a={ICCAP}, year={2021}, month={12}, keywords={work posture rula dhm tool and cutter grinder work fatigue regression analysis}, doi={10.4108/eai.7-12-2021.2314627} }- Madhan Mohan G
Vigneswaran C
Year: 2021
Predicting Optimum Work Force for a Tool and Cutter Grinding Machine using Posture Analysis
ICCAP
EAI
DOI: 10.4108/eai.7-12-2021.2314627
Abstract
In India, small and medium-scale industries depend on traditional conventional machine tools for their manufacturing activities. Amongst them, ‘Tool and cutter grind- ing machine’ is economically significant and also a bottleneck machine. The machine feeds cutting tools and cutters for other machine tools after completion of the re-grinding process. The Absenteeism of an operator in the machine affects the industry productivity. The study analyzes the reasons for absenteeism and its remedial actions. Among various reasons stated by operators of the machine, fatigue draws critical attention and scope for an ergonomic study. The novelty of this work is to determine the optimal anthropometric dimensions of the workforce that can operate the tool and cutter grinder machine without absenteeism and less fatigue. Therefore, work-study has been performed to identify repetitive upper limb movements of the operators during machine operations. The observation results call for a postural investigation. Rapid Upper Limb Assessment method (RULA), a popular upper limb postural assessment method, has been selected for the investigation. 35 operators from a small-scale industry have been inducted for the investigation. Anthropometric data were collected from the operators. Digital Human Models (DHM) have been built for all the postures occupied by the operators, to assess the RULA score.