On developing a green and intelligent manufacturing system

Abstract

This paper aims to present an autonomous intelligent system that improves the performance of conventional manufacturing processes to make them friendly to the environment. This improvement is made considering objectives that involve a reduction of supplies, use of tools, and low energy consumption; but maintaining desirable characteristics of machined parts in quality, mechanical properties, and dimensions. We present an architecture that integrates modeling with neural networks, validation using metrical statistics, and multi-objective optimization based on MOEA/D which takes experimental or historical data of the process and generates a set of parameters to satisfy all three criteria of sustainability, efficiency, and quality. Two cases for welding and machining process are presented to illustrate the use of the proposed approach which results in evidence of the feasibility of having green and intelligent manufacturing systems. In the welding process, the user can select a low amperage but increase lightly the cycles of operation to reduce energy consumption but satisfy the quality objective. In machining, the user can select a solution to avoid the use of cooling maintaining quality and efficiency in acceptable levels of operation.