Additive manufacturing offers significant advantages over conventional manufacturing, with potential to fundamentally transform the state-of-the-art in a variety of industries. Notwithstanding the enormous progress in current additive manufacturing technologies, certain intractable quality issues persist. This leads to considerable rework and high scrap rates, and thus poses significant impediments for sustainability of additive manufacturing. Consequently, there is a vital need to advance online methods for defect detection in additive manufacturing processes, so that incipient process anomalies can be identified, and possibly prevented, at an early stage during manufacture. This Grant Opportunity for Academic Liaison with Industry (GOALI) research project is anticipated to significantly advance the process monitoring and control technology in additive manufacturing, leading to improved product quality, enhanced process productivity, and higher profitability. Thus, outcomes from this research will have substantial socioeconomic impacts. The scientific findings from this research are extensible to many other advanced manufacturing processes. Furthermore, this project also includes many educational components, such as new course modules, and research experience for undergraduate students. Exposing students to this multidisciplinary research will cultivate a diverse and qualified workforce possessing the state-of-the-art technologies in advanced manufacturing.
The goal of this research is to resolve critical quality issues in additive manufacturing by addressing two fundamental research questions based on an integration of novel multi-phenomena sensing techniques with advanced analytical approaches for sensor data fusion: (1) what is the dynamic behavior of various process attributes, and how does this behavior cause the onset of additive manufacturing process anomalies? and (2) what are the causal linkages between additive manufacturing quality performance and process variables? The objectives of the research include: (1) quantitatively elucidate the fundamental relationships that connect process abnormalities in additive manufacturing with features extracted from online spatiotemporal sensor signals, i.e., achieve a mapping between the sensor features with the evolving part defects in additive manufacturing, enabling early detection of surface topography related defects; and (2) establish a model correlating process conditions/settings with both continuous and attribute product quality variables in additive manufacturing processes, and identify the process variables which have significant effects on product quality, providing valuable strategies for defect mitigation and implementation of close loop control in future additive manufacturing systems. This research will utilize prediction based process monitoring to tackle underlying complexity and uncertainty in additive manufacturing processes.
