Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g., aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in-situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in-situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for insitu monitoring of Powder Bed Fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in-situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.

https://iopscience.iop.org/article/10.1088/1361-6501/aa5c4f/pdf

Process defects and in situ monitoring methods in metal powder bed fusion: a review

Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.

https://link.springer.com/content/pdf/10.1186%2Fs40192-016-0045-4.pdf

In-process sensing in selective laser melting (SLM) additive manufacturing

https://spiral.imperial.ac.uk/bitstream/10044/1/67871/2/J291_Leung_Acta_as_accepted_181212.pdf

The effect of powder oxidation on defect formation in laser additive manufacturing

• Common defects and anomalies in powder bed fusion metal additive manufacturing. • Formation mechanism and practical mitigation strategies are discussed. • Defects/anomalies are classified as powder-related, processing-related, and post-processing related issues. • Properties such as mechanical behavior and corrosion resistance of defective parts are discussed. • Current challenges, gaps, and future trends are discussed. Metal additive manufacturing is a disruptive technology that is revolutionizing the manufacturing industry. Despite its unrivaled capability for directly fabricating metal parts with complex geometries, the wide realization of the technology is currently limited by microstructural defects and anomalies, which could significantly degrade the structural integrity and service performance of the product. Accurate detection, characterization, and prediction of these defects and anomalies have an important and immediate impact in manufacturing fully-dense and defect-free builds. This review seeks to elucidate common defects/anomalies and their formation mechanisms in powder bed fusion additive manufacturing processes. They could arise from raw materials, processing conditions, and post-processing. While defects/anomalies in laser welding have been studied extensively, their formation and evolution remain unclear. Additionally, the existence of powder in powder bed fusion techniques may generate new types of defects, e.g., porosity transferring from powder to builds. Practical strategies to mitigate defects are also addressed through fundamental understanding of their formation. Such explorations enable the validation and calibration of models and ease the process qualification without costly trial-and-error experimentation. 

Defects and anomalies in powder bed fusion metal additive manufacturing

Ni superalloys are widely used for hot section components in jet engines because they are very resistant to corrosion and maintain reasonably high strength at elevated temperature. However, the repair cost of the parts is high, partly due to the complexities of process variable optimization and control in laser cladding. In particular, optimizing the process parameters by experiments is time-consuming and costly. The microstructure and properties of the metal deposit are significantly influenced by values temperature gradient G and solidification rate R at the weld pool solidification boundary. Optimized values can help to reduce defects and improve properties of laser deposits. Optimization is hindered by the fact that the clad melt pool is hot and small, making in situ measurement of such solidification conditions difficult. Numerical simulation of the laser deposition process is a possible alternative to experimental measurement to obtain values of clad solidification parameters. In this investigation, G and R values at the weld pool solidification boundary were obtained from a three dimensional numerical simulation of laser deposition process and melt pool. The primary dendrite arm spacing and cooling rate of the deposited material were then correlated to these solidification conditions.

C. Kwakernaak

Papers

The effect of oxygen on transitional Marangoni flow in laser spot welding