SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra

Multi-Scale Multi-Physics Modeling of Laser Powder Bed Fusion Additive Manufacturing

Laser Powder Bed Fusion (LPBF) is a fast-developing metal additive manufacturing process offering unique capabilities including geometric freedom, flexibility, and part customization. The process induces complicated thermal histories with high temperature gradients and cooling rates, leading to rapid solidification microstructures with anisotropic properties as different from those produced conventionally. In addition, the LPBF parts exhibit to a large extent of in-sample and sample-to-sample variabilities in the microstructure and consequently part performance. The high variability in the microstructure and properties is considered the major obstacle against the widespread adoption of LPBF as a viable manufacturing technique. Therefore, a more in depth understanding and control of the solidification microstructure is needed to achieve the LPBF fabricated parts with desired properties. Since the solidification microstructure is highly influenced by the thermal input, it is essential to have an accreditable thermal model first. Therefore, a portion of this dissertation was devoted to developing an accurate thermal model through various methods including code-to-code verification and experimental validation. The materials used in this portion include Ti-6Al-4V, NiTi-SMA (Shape Memory Alloy). Next, a multi-scale multi-physics modeling framework which couples a finite element (FE) thermal model to a non-equilibrium phase field (PF) model was developed to investigate the rapid solidification microstructure during LPBF. The framework was utilized to predict the spatial variation in the morphology, size and micro-segregation in the single-track deposition of binary NiNb alloy during LPBF and a very good agreement with the experimental measurements was achieved