In this paper, a state space model is developed to describe the dimensional variation propagation of multistage machining processes. A complicated machining system usually contains multiple stages. When the workpiece passes through multiple stages, machining errors at each stage will be accumulated and transformed onto the workpiece. Differential motion vector, a concept from the robotics field, is used in this model as the state vector to represent the geometric deviation of the workpiece. The deviation accumulation and transformation are quantitatively described by the state transition in the state space model. A systematic procedure that builds the model is presented and an experimental validation is also conducted. The validation result is satisfactory. This model has great potential to be applied to fault diagnosis and process design evaluation for complicated machining processes.

Additive manufacturing (AM), commonly known as three-dimensional printing, is widely recognized as a disruptive technology, and it has the potential to fundamentally change the nature of future manufacturing. Through building products layer by layer, AM represents a paradigm shift in manufacturing, with many industrial applications. It enables production of huge varieties of customized products with considerable geometric complexity, extended capabilities, and functional performances. Despite tremendous enthusiasm AM faces major research challenges for widespread adoption of this innovative technology. Specifically, addressing the unique challenges associated with quality engineering of AM processes is crucial to the eventual success of AM. This article presents an overview of quality-related issues for AM processes and products, focusing on opportunities and challenges in quality inspection, monitoring, control, optimization, and transfer learning as well as on building quality into the product through design.

Geometric fidelity of 3D printed products is critical for additive manufacturing (AM) to be a direct manufacturing technology. Shape deviations of AM built products can be attributed to multiple variation sources such as substrate geometry defect, disturbance in process variables, and material phase change. Three strategies have been reported to improve geometric quality in AM: (1) control process variables x based on the observed disturbance of process variables Δx, (2) control process variables x based on the observed product deviation Δy, and (3) control input product geometry y based on the observed product deviation Δy. This study adopts the third strategy which changes the computer-aided design (CAD) design by optimally compensating the product deviations. To accomplish the goal, a predictive model is desirable to forecast the quality of a wide class of product shapes, particularly considering the vast library of AM built products with complex geometry. Built upon our previous optimal compensation study of cylindrical products, this work aims at a novel statistical predictive modeling and compensation approach to predict and improve the quality of both cylindrical and prismatic parts. Experimental investigation and validation of polyhedrons a indicates the promise of predicting and compensating a wide class of products built through 3D printing technology.

Additive manufacturing (AM) or three-dimensional (3D) printing is a promising technology that enables the direct fabrication of products with complex shapes without extra tooling and fixturing. However, control of 3D shape deformation in AM built products has been a challenging issue due to geometric complexity, product varieties, material phase changing and shrinkage, and interlayer bonding. One viable approach for accuracy control is through compensation of the product design to offset the geometric shape deformation. This work provides an analytical foundation to achieve optimal compensation for high-precision AM. We first present the optimal compensation policy or the optimal amount of compensation for two-dimensional (2D) shape deformation. By analyzing its optimality property, we propose the minimum area deviation (MAD) criterion to offset 2D shape deformation. This result is then generalized by establishing the minimum volume deviation (MVD) criterion and by deriving the optimal amount of compensation for 3D shape deformation. Furthermore, MAD and MVD criteria provide convenient quality measure or quality index for AM built products that facilitate online monitoring and feedback control of shape geometric accuracy.