Smart Process-Aware Tolerance Design and Quality Assurance for Additive and Hybrid Manufacturing
thesisposted on 01.08.2020, 00:00 by Azadeh Haghighi
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.