Abstract

Additive manufacturing has evolved from the concept of merely rapid prototyping tools to that of a viable technology capable of manufacturing industrial grade parts and components. As a result, the use of metal additive manufacturing techniques has expanded in recent years, fueled by the rapid growth of manufacturing sectors amidst the 4.0 industrial revolution. Within the metal-AM domain, the concept of gas metal arc welding – wire arc additive manufacturing (GMAW-WAAM) has gained increasing traction due to its low operational costs, high deposition rates, and its ability for large scale manufacturing.  The demand of stainless-steel parts has increased owing to their optimal mechanical properties and corrosion resistance amidst harsh environmental conditions.  This research aims to investigate the effect of arc voltage, wire feed rate and progressive post-deposition heat treatment conditions on the mechanical performance of GMAW-WAAM 316L stainless steel parts. The output responses investigated were ultimate tensile stress (UTS), yield strength (YS), toughness and strain at fracture. Multi-response optimization was conducted using Grey Relational Analysis and Analysis of Variance (ANOVA) was used to study the significant contribution of the main effects and their interactions on the output responses. Lastly, an Adaptive Neuro-Fuzzy Interference System (ANFIS) was modeled to be used as a preliminary predictive model in predicting the strength related properties of the current system from the process parameters.  Statistical analysis from GRA and ANOVA revealed the presence of interaction effects among the parameters, indicating that a combination of input parameters need to be optimized simultaneously to obtain optimal mechanical properties. While post processing had a major effect on the output responses, the severity of post processing was highly influenced by the deposition parameters and the thermal history associated with it. GRA revealed that the sample printed with and arc voltage of 20V, wire feed rate of 4m/min and subjected to a heat treatment of 1100 was the optimum multi-response condition. The sample with the optimal combination had a UTS of 617.55 MPa, YS of 429.08 MPa, Toughness of 411.17MJ/m3 and a strain at fracture of 85%. The developed ANFIS model demonstrated reasonable results for predicting the UTS and YS with a MAPE of 6.50% and MAPE of 8.05% respectively.

Publication Date

2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Mechanical Engineering (MS)

Department, Program, or Center

Mechanical Engineering

Advisor

Salman Pervaiz

Advisor/Committee Member

Wael Abdel Samad

Advisor/Committee Member

Umer Javed

Comments

This thesis has been embargoed. The full-text will be available on or around 7/22/2027.

Campus

RIT Dubai

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