Surface Quality Advancement of Additively Manufactured Components for 4th Industrial Revolution

Dear Colleagues,

Cracks and other anomalies can be detected in additively manufactured parts/components via in situ monitoring, even during operation. Final parts’ quality can be assessed and determined via a real-time monitoring process able to quickly detect anomalies in additive manufacturing (AM). Despite recent breakthroughs in AM techniques, their impact has not yet been seen extensively in manufacturing industries. This could be linked to the many technical drawbacks and challenges that must be overcome for the extended application of AM in various industries. These challenges can be grouped into two categories: the lack of an in situ process for control and monitoring, and a still insufficient understanding of metal AM techniques. Another factor that comes into play is the lack of knowledge surrounding process parameters, which hugely impact the quality performance of finished products due to microstructural evolution. It has been reported in the literature that analytical and numerical models alone cannot elucidate the relationship among the process, structure, property and performance of AM products. Artificial intelligence (AI) has been noted as one of the principal ways of conducting complex pattern recognition and regression analysis to solve underlying physical models. The potential of using AI to detect anomalies and drawbacks can aid in enhancing the quality performance of AM products, leading to a decrease in wastage of materials, operation time and in-process intervention and enhanced quality and cost effectiveness.

The detection and characterization of anomalies are critical for meeting the standards of quality for produced parts and additively manufactured parts. The different types of anomalies and defects in manufacturing must first be grounded, and their role in enhancing the performance and quality of AM products must also be well-understood. Powder quality must be ascertained first, followed by the manufacturing process. Moreover, advanced tools and equipment to evaluate the mechanical properties, quality control and detection of anomalies must also be developed.

Dr. Fatoba Olawale Samuel
Guest Editor

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• additive manufacturing
• monitoring
• surface quality
• process-structure-property-performance
• process parameters
• 4th Industrial Revolution (4IR)
• residual stresses
• temperature gradient