Multicriteria Decision Making of a Life Cycle Engineered Rack and Pinion System

The automotive industry has been developed over the years to build lighter and more efficient vehicles; however, the manufacturing processes still have an important environmental impact starting from the acquisition of the raw material until the end of its useful life. In this sense, life cycle engineering contributes to solve the environmental issues produced by the traditional manufacturing industry by considering a process that evaluates the technical aspects of the product but also weights the importance of the environmental impact. However, since there are some alternatives that can be considered as suitable for their technical, environmental, or cost qualities, the multicriteria decision methods used as an engineering tool have been useful to balance all the needed criteria in order to make the best selection. In this sense, this research provided an analysis of five materials that could be used in a rack and pinion system and were submitted to a process of life cycle analysis to consider the environmental parameters as part of the criteria to be assessed by the multicriteria decision methods such as entropy, the Technique of Order Preference Similarity method, the complex proportional assessment method, and the multicriteria optimization and compromise solution, leading to the selection of the best material to be considered for a rack and pinion system. In this sense, the process allowed us to conclude that some materials that are useful can be evaluated by multicriteria decision methods regarding the life cycle analysis, contributing to the application of these methods to make a more environmentally responsible material selection for automotive parts. Furthermore, among all the materials, the best suited for the rack and pinion system was the AISI 4340, which was validated by finite elements simulation, showing that the selection was optimal with a maximum stress of 216.14 MPa, a maximum deformation of 0.0081 mm, and a minimum safety factor of 3.56. In this sense, the simulation validated the selection made before, guaranteeing that the methods used are feasible for automotive applications.