• 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

Defect formation is a critical challenge for powder-based metal additive manufacturing (AM). Current understanding on the three important issues including formation mechanism, influence and control method of metal AM defects should be updated. In this review paper, multi-scale defects in AMed metals and alloys are identified and for the first time classified into three categories, including geometry related, surface integrity related and microstructural defects. In particular, the microstructural defects are further divided into internal cracks and pores, textured columnar grains, compositional defects and dislocation cells. The root causes of the multi-scale defects are discussed. The key factors that affect the defect formation are identified and analyzed. The detection methods and modeling of the multi-scale defects are briefly introduced. The effects of the multi-scale defects on the mechanical properties especially for tensile properties and fatigue performance of AMed metallic components are reviewed. Various control and mitigation methods for the corresponding defects, include process parameter control, post processing, alloy design and hybrid AM techniques, are summarized and discussed. From research aspect, current research gaps and future prospects from three important aspects of the multi-scale AM defects are identified and delineated. 

Multi-scale defects in powder-based additively manufactured metals and alloys

An overview of modern metal additive manufacturing technology

Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

Residual stresses (RS) induced in machined components have substantial impact on the quality and lifetime of the final products. There are several cutting parameters and conditions that affect the generation of RS, so understanding the relationship between the RS generation and those parameters to minimize the induced tensile RS is a crucial issue. This paper presents a study on the utilization of artificial intelligence-based methods to model the RS generation during dry turning of DT4E pure iron. The experiments were designed based on central composite design method. The effects of the cutting parameters such as cutting speed, feed and cutting depth on the generated RSes in both circumferential and radial directions are investigated. Two hybrid artificial neural network (ANN) models are used to predict the process responses after training them using the experimental results. The prediction accuracy of the two models are enhanced via integration with two different metaheuristic optimization algorithms, namely particle swarm optimization (PSO) and flower pollination algorithm (FPA). These optimization algorithms are used as subroutine algorithms to determine the optimal parameters of the ANN model. The predicted results by the proposed models were compared with the experimental results as well as those obtained by standalone ANN. The accuracy of all models was evaluated using different statistical measures. The ANN-FPA had the best prediction accuracy followed by ANN-PSO. The coefficient of determination of ANN-FPA has high values of 0.996 and 0.997 for radial RS and circumferential RS, while they were 0.971 and 0.992 for ANN-PSO and 0.649 and 0.815 for standalone ANN.

Prediction of residual stresses in turning of pure iron using artificial intelligence-based methods

Additive manufacturing has become a major growing field in materials engineering, following a new tendency for custom, high precision and on-demand fabrication. Residual stresses are prone to appear in any production technique, which remain a challenge to be measured. These stresses can lead to a reduction on mechanical performance and even cause premature failure. Thus, a wide understanding of residual stress is critical for greater part reliability. Among Non-destructive Testing (NDT) techniques, acoustic and ultrasonic waves remain widely used to determine stresses, voids and defects in a wide array of parts. In this contribution, Ultrasonic Testing (UT) is highlighted as an alternative for measuring residual stress both during and after fabrication.

Residual stress analysis of additive manufacturing of metallic parts using ultrasonic waves: State of the art review

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Residual stress analysis of additive manufacturing of metallic parts using ultrasonic waves: State of the art review