Additive manufacturing (AM), or three-dimensional (3D) printing, holds the promise of direct fabricating products of highly complex and individualized geometric shapes. However, shape deviation suffered by fabricated products remains one of the most concerned quality issues that hinders the wider application of AM technologies.

Quality control for AM involves improving the shape accuracy for any new and untried products. The objective of the thesis is to develop statistical methods for shape deviation prediction and derive compensation strategies for shape accuracy improvement. In contrast to mass production, due to the huge variety of product shapes and low volume of production in AM processes, it is usually cost-prohibitive to collect sufficient sample data, which means only...[ Read more ]

Additive manufacturing (AM), or three-dimensional (3D) printing, holds the promise of direct fabricating products of highly complex and individualized geometric shapes. However, shape deviation suffered by fabricated products remains one of the most concerned quality issues that hinders the wider application of AM technologies.

Quality control for AM involves improving the shape accuracy for any new and untried products. The objective of the thesis is to develop statistical methods for shape deviation prediction and derive compensation strategies for shape accuracy improvement. In contrast to mass production, due to the huge variety of product shapes and low volume of production in AM processes, it is usually cost-prohibitive to collect sufficient sample data, which means only limited sample data for limited shapes are available.

Achieving high and consistent shape accuracy with limited sample data in such one-of-a-kind AM processes is a challenging task, since the shape deviation of fabricated products usually depends on the setting of certain process parameters and varies from shape to shape. To address this, three research issues are investigated and corresponding methods are developed for shape accuracy control. First, a two-step Gaussian Process and Kernel Smoothing (GPKS) scheme is proposed to predict the in-plane (x-y plane) shape deviations with the information on process parameters. Based on this prediction scheme, a shape compensation strategy is derived that greatly improves the shape accuracy of products under different settings of process parameters. Second, a novel statistical transfer learning framework is proposed to predict and compensate the in-plane shape deviations for freeform products with arbitrary untried shapes based on a small number of fabricated products. In this framework, transferring information from source shapes to new target shapes is achieved through establishing shape deviation models based on error decomposition and learning of a common representation for local shape features. Third, the statistical transfer learning framework for in-plane shape accuracy control is extended to 3D shape accuracy control by learning the additional error induced due to different layer features. Experimental studies of the fused filament fabrication processes validated the effectiveness of proposed methods.