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Metal additive manufacturing paves the way for industries to create new applications through unique design capabilities. The powder bed fusion process is one among many metal additive manufacturing technologies that are commercially successful. Despite its numerous advantages and application in various fields, defects may occur during processing, which causes premature failure of components. Distortion is one of the major defects, and it depends on process settings, geometry, and orientation related. These distortions and dimensional deviations should be predicted faster for part qualification for many industrial applications. This work attempts to predict distortions based on shape descriptors to address this issue. Shape descriptors are definitions used to identify the details of the shape of a model to be printed. It can be either two dimensional or three dimensional. In this work, 2D shape descriptors are selected for analysis. These 2D shape descriptors can help identify how the design features significantly affect the part distortion in the PBF process. In this work, a few 2D shape descriptors are defined and modelled as a design feature to achieve the objective. Then the respective models are subjected to distortion analysis. The relationship between shape descriptors and distortion are studied through inherent strain method based simulation of distortion. It is observed from the results that most shape descriptors defined in this work can be used to predict the distortion. This work serves as a base and can help create knowledge for proposing design guidelines for the metal powder bed fusion process and helps in redesigning to prevent distortions.

The present work proposes a data-driven method to address the issue of increased computational time in predicting the thermal field in the Powder Bed Fusion process (PBF). Understanding the temperature field evolution is essential in metal Additive Manufacturing (AM) process like PBF technology to produce the right quality parts. As the process of metal AM involves physical phenomena, including fluid flow, heat and mass transfer, as well as structural loads, the underlying physics is complex and difficult to predict temperature fields in reasonable time with increased accuracy. Physical experiments and numerical simulations can be expensive and time-consuming. Hence a data-driven approach has been introduced to address the issue of increased computational time to predict the thermal field developed during the process. The methodology proposed in this work consists of a thermal model and a surrogate model based on Gaussian Process Regression (GPR). Initially, the transient thermal behaviour is studied based on Finite Element Analysis (FEA). Later, a surrogate model based on the Gaussian process is developed from the FE simulated data to decrease the computational costs of high-fidelity physics-based simulations. The GPR model has predicted the thermal fields in less time than that of the physics-based FEA model. To validate our approach, both the numerical simulations and GPR-based model are justified by conducting single-track experimentations using Inconel 625 (IN625). The results have shown that the proposed GPR model predictions are in good agreement with the temperature measured from experiments. The studies have revealed the influence of process parameters like scan speed and laser power on the thermal field. Single track dimension for laser exposure seem to correlate with GPR predicted track width. This prediction could be employed to predict distortion, residual stress and other process response in a shorter time.

In some additive manufacturing processes, support structures are required while building parts, for structural stability as well as for some functional aspects. Support structure should be designed in a way that it should withstand the weight of the part especially overhanging features of the part to avoid distortion and mishap. This paper deals with design and fabrication of tailored support structures for some special cases. From the understanding obtained through various literature, initially, a broad classification of support structures is presented. Based on that, application-specific support structures were designed. Two different support structures were designed, analyzed and fabricated successfully. After fabrication, support structures were easily removed from the part manually.

Metal based additive manufacturing (AM) techniques are continuously adopted by aerospace, automobile, defense, and healthcare industries. The primary concern for the components used in these applications is high precision. Despite being able to produce complex shapes, they lack in precision due to the distortions in shape and size of the part during and after fabrication. Distortion is the deviation of the part from its actual shape or dimension. Eventually, shape deviation has an unfavorable effect on the part functional performance which will hinder their use for critical technological applications. Even though, the temperature gradients during the process build affect these errors; they need to be studied in detail to fix the causation of these errors. In this paper, we review and classify the causation for shape and size distortion that occurs in metal additive processes. The approach to classification is multi-faceted and are based on the geometry of the fabricated part, the material used, process-related parameters, part orientation and physical phenomenon that occurs during the process. This work would help understand the root cause of distortion in major commercially successful metal AM processes and eliminate the need for costly trials.