Graduation Year

2021

Document Type

Thesis

Degree

M.S.M.E.

Degree Name

MS in Mechanical Engineering (M.S.M.E.)

Degree Granting Department

Mechanical Engineering

Major Professor

Nancy Diaz-Elsayed, Ph.D.

Committee Member

Ashok Kumar, Ph.D.

Committee Member

Jose Zayas-Castro, Ph.D.

Keywords

Additive Manufacturing, Industry 4.0, MCDM, Surface Roughness, Sustainable Manufacturing, TOPSIS

Abstract

The fourth industrial revolution or Industry 4.0 has changed today’s manufacturing scenario. The transition to smart manufacturing technologies has been evolving over the last few years and has recently accelerated due to the pandemic. The need to make manufacturing systems agile, adaptive, resilient, and robust has expediated the adoption and implementation of smart manufacturing technologies.

Despite the interest of manufacturers in smart manufacturing, the adoption rate has been slow due to the lack of decision-making tools that can provide a clear strategic roadmap for effective execution. Small and medium sized enterprises (SMEs) are especially slow in adoption due to the narrow breadth of technological and financial infrastructure and lack of a tailored approach.

This research aims to provide the framework for decision making to enable and escalate the adoption of smart manufacturing technologies in SMEs by providing visibility of smart and sustainable technology alternatives with respect to key manufacturing criteria. A multi criteria decision making technique (MCDM) called fuzzy Technique for Order of preference by Similarity to Ideal Solution (TOPSIS) is employed to provide clarity in terms of value of these technologies to help manufacturers frame a roadmap for future adoption at a large-scale factory setting.

A smart manufacturing use case is investigated in the later part of the research due to its low rate of adoption as observed from the literature and the results obtained from the MCDM framework. The feasibility of using digital imagery to determine the surface roughness and edge dimension of 3-D printed parts is investigated to promote the use of automated part quality detection in large-scale manufacturing. The decision-making tool ultimately aims to provide a means for evaluating a large range of smart manufacturing technologies while considering the status quo.

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