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In the recession, the lack of potential labour was the most important concern for the small- and medium-scale bus body fabrication industries. In the pandemic covid-19 situation, the productivity of these fabrication industries was turndown due to deprivation of labour productivity, postponement of work and cost overshoot, which may pilot to argue. In India, Central Tamil Nadu fabrication industries are buttress by immense labour intensity. In this panic situation, these industries are struggling to obtain better productivity. Diversity of earlier works on the detection of the factors with the intention of impacting labour productivity in structural industries and manufacturing sectors has been carried out by research in many countries. Nevertheless, those attempt characteristics are restricted to credentials and implemented in subjective techniques for analysis in declining bus body fabrication sectors. Ambition of this particular research is to recommend an artificial intelligence-based model to predict the “uncertainty factors” influencing labour productivity and safety performance of bus body fabrication industries related to small and medium scale in the pandemic and post pandemic situation.

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This paper is investigated the dry sliding wear behavior of Al Metal Matrix Composites reinforced with MoS 2 and SiC particulates. Taguchi's Method is applied to study the effects of reinforcement, load, sliding speed and distance of Al-SiC-MoS 2 hybrid composite made by Stir Casting Method. It is found that addition of SiC particles increases the mechanical strength and wears resistance of Al alloy significantly. In addition to that increase in hardness of the composite resulting the machining a difficult one is reduced by the addition of MoS 2 particles and also results wear reduction.

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