Smart Process-Aware Tolerance Design and Quality Assurance for Additive and Hybrid Manufacturing

The emergence of Additive Manufacturing (AM) has paved the way for fabrication of highly complex geometries and internal structures, owing to its unique layer-wise manufacturing approach. The AM technology also holds great potential for improving material efficiency and reducing life cycle environmental impacts and carbon footprint, which helps with promoting a sustainable “green” manufacturing strategy. Recently, the AM trend has shifted from fabrication of prototypes to functional end-use metallic or polymeric products in various critical industries including aerospace, automotive and healthcare. Consequently, ensuring the final quality of these single-component or multi-component products has become more important than ever. As a result, prediction, control, and enhancing the dimensional, geometric and mechanical properties of additively manufactured products have attracted significant research interest. A promising approach to overcome the limitations of AM in terms of quality is the adoption of hybrid additive-subtractive manufacturing processes. Nonetheless, this approach introduces new challenges for quality assurance and sustainable production planning, as multiple processes with different characteristics are involved. In this dissertation, a set of analytical models and decision-making tools are established, both at the process and product levels, to help designers and manufacturers with quality assurance and tolerance design of single- and multi-component products fabricated by additive and hybrid additive-subtractive manufacturing processes. Some of the discussed topics include surface roughness and porosity characterization, process-aware tolerancing, and application of data-driven and artificial intelligence techniques – towards smarter manufacturing. Furthermore, the development procedure and capabilities of a robotic hybrid additive-subtractive manufacturing platform are presented and discussed. The outcomes of this research will contribute towards the innovation of smart additive and hybrid manufacturing design software, machines, and equipment.