PhD Defence Cindy Sithole | Quality Strategy for Metal Additive Manufacturing | A Strategy Based on Quality by Design and Design for Six Sigma for batch Production in Metal Laser Powder Bed Fusion Technology

Quality Strategy for Metal Additive Manufacturing | A Strategy Based on Quality by Design and Design for Six Sigma for batch Production in Metal Laser Powder Bed Fusion Technology

Cindy Sithole is a PhD student in the department Advanced Manufacturing, Sustainable Products & Energy Systems. (Co)Promotors are prof.dr. I. Gibson; dr.ir. S. Hoekstra and dr. A. Jalalian from the faculty Engineering Technology from the University of Twente.

This thesis contributes to the field of metal additive manufacturing (AM) by developing and validating a structured quality assurance framework for batch production using laser powder bed fusion (LPBF) technology. The research addresses critical industry challenges, particularly the lack of systematic quality control approaches and the high variability in part quality across batches.

The primary contribution lies in the novel application of Design for Six Sigma (DFSS) and Quality by Design (QbD) methodologies to metal AM which is an areas where these quality strategies have been limitedly used. By integrating DFSS and QbD, the study establishes a data-driven, proactive approach to managing process variability, improving repeatability, and aligning AM practices with industrial quality standards.

A mixed-methods approach was employed. Two industry surveys were conducted to identify prevalent quality challenges and validate the need for a structured strategy, with 75% of professionals affirming this necessity. Experimental studies were carried out to quantify process variation, focusing on surface roughness and dimensional consistency across different build positions in LPBF. These analyses highlighted key contributors to inconsistency, such as recoater motion, gas flow, and thermal gradients.

The resulting quality framework comprises five structured steps:

1. Define part quality objectives and potential design-related failures
2. Map the manufacturing process and identify potential defects
3. Determine critical process and machine parameters
4. Evaluate risks linked to key parameters
5. Develop a control plan with appropriate measurement strategies and KPIs

The framework was validated using the iFuse implant, a representative medical device with complex geometries. Results demonstrated improved dimensional accuracy (within ±0.2 mm), reduced surface variation, and minimized post-processing requirements. This confirmed the framework's effectiveness in enhancing consistency and process control in batch production.

Overall, this thesis presents a scalable, systematic quality framework that addresses pressing challenges in metal AM. It offers manufacturers a practical tool to improve production reliability, support industrial standardization, and promote broader adoption of AM technologies in high-precision, high-consistency applications.