Dissertation in the field of mechanical engineering M.Sc. (Tech) Jan Akmal

Opponent David W. Rosen, Agency for Science, Technology and Research (A*STAR), Singapore

Custos Professor Mika Salmi, Aalto University, School of Engineering, Department of Mechanical Engineering

Establishing a new frontier for 3D printing of accurate, personalized and smart end-use parts 3D printing (3DP), industrially known as additive manufacturing, is rapidly emerging as a general-purpose technology akin to dynamos and computers that serve as the basis for a modern industrial setting. Today, 3DP represents a group of technologies that deposit, fuse, dispense, bond, and cure a wide range of feedstocks, consisting of polymers, ceramics, and metals, on a layer-by-layer principle. The advent and proliferation of the additive process, triggering Industry 4.0, is challenging academics and practitioners to establish new practices, designs, and modes of creating and supplying end-use parts for a wide range of industries. Contributing to this emerging stream of research on 3DP technologies, the overarching purpose of this doctoral thesis is to discover situations and ways in which companies can benefit from implementing 3DP in conjunction with conventional manufacturing. To this end, this doctoral thesis establishes a new digital operational practice—dynamic supplier selection—using a novel build-to-model mode of manufacturing. It estimates the uncertainty and the worst-case error in creating an end-use part made by radiologic images, thresholding, digital design, and 3DP. It also develops process interruption-based embedding to create smart parts. Empirical evidence from a case company confirms that 3DP can radically shift the performance frontier for problematic parts in conventional supply. The generative mechanism of successful outcome is triggered by the encapsulation of design and 3DP-process instructions. This reduces mundane transaction costs and enables highly interactive model-based supplier relationships for decentralized manufacturing. The achievable accuracy of 3DP aids practitioners and authorities in substantiating appropriate practices that meet the required quality standards. The development of process interruption-based embedding opens a direction for creating smart parts facilitating condition monitoring, machine learning, and preventive maintenance for Industry 4.0. This doctoral thesis aids researchers and practitioners in switching parts over to 3DP technologies from large end-use part repositories with a dynamic response as opposed to a static choice with conventional manufacturing involving increasing minimum order quantities, costs, and lead-times. It can allow a dynamic response for accurate, personalized, and smart end-use parts.