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This paper aims to identify the optimum level of factors or parameters that affect the energy absorption of conoidal structures by grey relational examination. To optimize crashworthiness parameters of conical structures, the L9 orthogonal array has been adopted to design the experiments. The tailor-made thin-walled conical structures were fabricated by three most important factors, such as base diameter, height, and thickness, as design variables, and they were subjected to axial compression in a quasi-static method. The important responses of crashworthiness indicators such as the mean crushing force and specific energy absorption (SEA) were calculated with the help of a load-displacement curve. Experimental results showed that the crushing behaviours of conical structures were fairly significant. Grey relational analysis (GRA) and analysis of variance are used toobtain the optimal levels of parameters. From the results, the optimum levels of parameters are found to be a base diameter of 180 mm, a height of 120 mm, and a thickness of 1.5 mm. [ABSTRACT FROM AUTHOR]

Byline: P. Nelson Raja, S.M. Kannan, V. Jeyabalan Competition is worldwide and markets are fast becoming price sensitive. These challenges are forcing companies to implement various productivity improvement efforts to meet the needs of ever changing market demand. The total productive maintenance (TPM) has provided quantitative metric overall equipment effectiveness (OEE) for measuring the productivity of individual production equipment. In future, an extremely important objective is to improve the performance of the whole process or line instead of concentrating only on a single machine. The traditional metrics like throughput and utilisation rate measures, only the part of the performance of manufacturing equipment. They are not helpful in identifying the problems underlying improvements needed to increase productivity. In this paper, an attempt is made to use overall line effectiveness (OLE) as an index of performance evaluation in the production line of a manufacturing system. A new approach is proposed to assess the quality rate of the manufacturing system using principal component analysis (PCA). A detailed methodology for determining the overall line availability, overall line performance and overall line quality is presented. A case study is carried out and the results are discussed.

Develop a multi-target exhibit by considering the workstation reliability for preventive maintenance perspective, the general availability of the framework for production purposes, and total operational expenses for both preventive support and production arranging decisions. Despite that, the greater parts of the reviews in upkeep optimization do not consider the creation necessities experienced eventually. In this paper, hybrid inspired optimization model for the performance analysis in the manufacturing industry is utilized. This forecast investigation neural Network considered for weight streamlining procedure alongside parameters, for example, Total Operational Cost (TOC), availability and reliability of assembling framework. Weight examination krill and swarm intelligence are used to limit Mean Square Error (MSE) for all parameters. All the perfect outcomes show the way that the refined slip-up qualities between the output of the trial values and the foreseen qualities are solidly proportionate to zero in the arranged framework. From the results, the proposed Modified Krill herd Swarm Optimization (MKHSO) based perfect neural framework exhibits a precision of 98.23%, which diverges from the existing methodology. [ABSTRACT FROM AUTHOR]