Statistical modeling and machine learning for shape accuracy control in additive manufacturing

2018-07-09Additive manufacturing (AM), or three-dimensional (3D) printing, refers to a new class of technologies associated with the direct fabrication of physical products from Computer-Aided Design (CAD) models by a layered manufacturing process. It has been widely recognized as a disruptive technology with the potential to fundamentally change the nature of future manufacturing, and the changes can amount to a third industrial revolution. ❧ Despite the vigorous development of different 3D printing techniques, the end-part quality of 3D printing however, is still not comparable to traditional manufacturing which continues to be one of the most significant issues in adoption. As an essential aspect of end-part quality, the shape accuracy still requires better control. Therefore, development of quantitative models that could predict process behaviors or end-part shape deviation is fundamental to inform both part design and process control. ❧ Developing the quantitative models and achieving high and consistent shape accuracy in AM is a challenging task. Those challenges include three aspects: (i) process physics is complex and more fundamental process knowledge is required to enable more precision control, but they are not yet fully understood; (ii) quality of the same product vary in different machines due to variations of machine properties, making the generic quality prediction and improvement pretty difficult; (iii) low volume and high geometric complexity due to frequent change of product geometries in discrete printing process challenges the modeling. There are a great amount of research efforts on the shape accuracy control of AM. Yet the accuracy enhancements are not yet satisfactory. ❧ In this dissertation, we put forth several statistical modeling and machine learning based methods to improve the shape accuracy of 3D printed parts from two perspectives: shape deviation modeling and process behavior modeling. The shape deviation modeling predicts shape deviation directly from the design without considering the process specifications, while the process behavior modeling involves process information. Our ultimate objective is to build both product quality and process behavior models to guide the shape accuracy improvement. To achieve this objective, we include four tasks. ❧ Built upon our previous prescriptive modeling and optimal compensation study for cylinder and polyhedron shapes, the first task develops circular approximation with selective cornering (CASC) strategy to extend the basic models to freeform shapes. Polynomial approximation is first adopted to approximate an arbitrary shape to a polygon with large number of sides. Then each side is approximated by an arc, to which the cylindrical base is added. The corners with actual sharp transitions are then selected to add cookie-cutter function. Experimental investigation using projection-based stereolithography (SLA) process successfully validates the proposed methodology and the optimal compensation. ❧ Then the second task extends the prescriptive modeling and compensation of shape devi- ation to the field of statistical process control (SPC) scheme for shape to shape deviation monitoring. We put forth the statistic and capability index for AM process monitoring. Then we adopt Exponentially Weighted Moving Average (EWMA) control chart for shape to shape monitoring. Only a limited number of test shapes are required to establish control limits. Experimental investigation is still conducted on projection-based SLA process. ❧ The third task further extends the framework to selective laser melting (SLM) process. The framework extension decouples different error sources and achieves a comprehensive model to predict shape deviations. Build upon our prescriptive modeling framework, the non-negligible surface roughness is filtered out from the geometric deviation profile to guarantee confident prediction. We establish spatial models to quantify laser beam positioning error in both x− and y− directions. Then we estimate and model the other machine-dependent location effects. Both the above two effects are transfered into equivalent shape design error following equivalent error concept and compensated along with geometric deviation. ❧ Different from the first three tasks, which build general models without considering process parameters, our last task looks into the process behaviors and adopts HP’s Multi Jet Fusion (MJF) as a study case. We apply deep learning to achieve fast and accurate thermal behavior prediction and knowledge discovery. With the physical insight, we establish two deep neural networks (DNN). The first network predicts fusing layer thermal behavior from the principle energy driver and heat transfer. The other one learns the thermal diffusivity as deep neural network. The experiments report promising model performance in both prediction quality (e.g., accuracy) and the computational cost