The arrival on the market of a new kind of CNC machines which can both add and remove material to an object paved the way to a new approach to the problem of repairing damaged components. The additive operation is performed by a Direct Laser Deposition (DLD) tool, while the subtractive one is a machining task. Up to now, repair operations have been carried out manually and for this reason they are errors prone, costly and time consuming. Refurbishment can extend the life of a component, saving raw materials and resources. For these reasons, using a precise and repeatable CNC machine to repair valuable objects is therefore very attractive for the sake of reliability and repeatability, but also from an economical and environmental point of view. One of the biggest obstacles to the automation of the repairing process is represented by the fact that the CAM software requires a solid CAD model of the damage to create the toolpaths needed to perform additive operations. Using a 3D scanner the geometry of the damaged component can be reconstructed without major difficulties, but figuring out the damage location is rather difficult. The present work proposes the use of octrees to automatically detect the damaged spot, starting from the 3D scan of the damaged object. A software named DUOADD has been developed to convert this information into a CAD model suitable to be used by the CAM software. DUOADD performs an automatic comparison between the 3D scanned model and the original CAD model to detect the damaged area. The detected volume is then exported as a STEP file suitable to be used directly by the CAM. The new workflow designed to perform a complete repair operation is described placing the focus on the coding part. DUOADD allows to approach the repairing problem from a new point of view which allows savings of time and financial resources. The successful application of the entire process to repair a damaged die for injection molding is reported as a case study. In the last part of this work the strategies used to apply new material on the worn area are described and discussed. This work also highlights the importance of using optimal parameters for the deposition of the new material. The procedures to find those optimal parameters are reported, underlying the pros and cons. Although the DLD process is very energy efficient, some issues as thermal stresses and deformations are also reported and investigated, in an attempt to minimize their effects.

Quality assurance has been one of the major challenges in laser-based additive manufacturing (AM) processes. This study proposes a novel process modeling methodology for layer-wise in-situ quality monitoring based on image series analysis. An image-based autoregressive (AR) model has been proposed based on the image registration function between consecutively observed thermal images. Image registration is used to extract melt pool location and orientation change between consecutive images, which contains sensing stability information. Subsequently, a Gaussian process model is used to characterize the spatial correlation within the error matrix. Finally, the extracted features from the aforementioned processes are jointly used for layer-wise quality monitoring. A case study of a thin wall fabrication by a Directed Laser Deposition (DLD) process is used to demonstrate the effectiveness of the proposed methodology.

Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is improving the quality of the fabricated parts. While there are several ways of approaching this problem, developing data-driven methods that use AM process signatures to identify these part anomalies can be rapidly applied to improve the overall part quality during the build. The objective of this dissertation is to model multiple processes within the AM to quantify the quality of the parts and reduced the uncertainty due to variation in input process parameters. The objective of first study is to build a new layer-wise process signature model to characterize the thermal-defect relationship. Based on melt pool images, we propose novel layer-wise key process signatures, which are calculated using multilinear principal component analysis (MPCA) and are directly correlated with layer-wise quality of the part. Second study broadens the spectrum of the dissertation to include mechanical properties, where a novel two-phase modeling methodology is proposed for fatigue life prediction based on in-situ monitoring of thermal history. In final study, our objective is to pave the way toward a better understanding of the uncertainty in the process-defect-structures relationship using an inverse robust design exploration method. The method involves two steps. In the first step, mathematical models are developed to characterize and model the forward flow of information in the intended additive manufacturing process. In the second step, inverse robust design exploration is carried out to investigate satisfying design solutions that meet multiple AM goals.