Innovation in Additive Manufacturing Intelligent Adaptive Processes

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Additive manufacturing (AM) has been gaining tremendous popularity in the public and industrial domains. The industrial landscape is undergoing a paradigm shift towards AM, as layer-by-layer manufacturing offers freedom of design by enabling production of shapes with complex internal and external features that enhance product functionality without the need for specialized tooling. For high-value-low-volume parts, or when a high degree of customization is needed, AM has been shown to decrease costs by reducing the design to fabrication cycle, by consolidating of multipart assemblies, and by reducing material waste. Thus AM has dramatically reshaped the industrial landscape, having a projected global revenue growth from $3.07B in 2013 to $21B by 2020 (Wohlers Report 2014), with key investment opportunities in related transformative research. The Canadian manufacturing sector has a unique opportunity to establish and maintain leadership through innovation in AM technologies. This proposal supports the above vision of excellence by targeting the ongoing technology gaps that need to be bridged before economically sustainable AM adoption can occur, with a direct focus on powder bed binder jetting (PBBJ) and powder bed laser fusion (PBLF) of metals. Some of the current limitations in AM of metals are related to part quality in terms of poor dimensional tolerances, high surface roughness, occurrence of internal defects, and inconsistencies in composition and mechanical properties. To address these limitations, this research focuses on developing advanced intelligent monitoring and adaptive control strategies for PBBJ and PBLF. To achieve this goal, two synergistic complementary long-term objectives are proposed: (1) development of advanced monitoring strategies for detection of relevant process characteristics, (2) advancement of robust adaptive control and calibration strategies to enable reliable part fabrication. The short-term focus will be to develop two Dynamic Sensing Clusters (DSC). The first, for PBLF composed of monitoring systems for thermometry, spectrometry, and vision-based measurements. The second, for PBBJ will be using vision-based sensing. The DSC systems will enable a comprehensive correlation between process input parameters, process characteristics, and part qualities and will be deployed to meet the two longer-term objectives for PBBJ and PBLF. The proposed research vision will facilitate the development of the next generation of intelligent powder bed AM systems and the training of highly qualified personnel. The research outcomes will lead to an increase in the Canadian manufacturing competitive advantage, with a meaningful economic impact through the development of technologies by which to offer high value services for metal part manufacturing and presenting commercialization opportunities for intelligent digital manufacturing strategies.