Contribute to Materials Evaluation

Deadline: January 2027
Publication Date: July 2027

Scope:

Additive manufacturing (AM) has become an essential manufacturing technology for aerospace, energy, biomedical, and defense applications. Its ability to fabricate complex geometries, reduce material waste, and enable rapid design iteration has accelerated its adoption for producing both prototype and end-use components. As AM parts are increasingly deployed in safety-critical applications, ensuring their quality, reliability, and structural integrity has become a major challenge. Consequently, there is a growing need for reliable in-situ nondestructive testing (NDT) for defect detection during fabrication, process monitoring techniques capable of assessing build quality and enabling closed-loop process control, and ex-situ NDT for post-fabrication quality assurance.

This special issue aims to provide a comprehensive overview of recent advances in in-situ and ex-situ NDT techniques for additive manufacturing, highlighting emerging sensing technologies, data analytics, artificial intelligence, and industrial implementation.

Topics of Interest

Authors are invited to submit original research papers or review articles related to, but not limited to, the following topics:

• In-situ NDE and quality monitoring during additive manufacturing

• Ultrasonic, acoustic emission, optical (laser, infrared and others), thermal, electromagnetic, X-ray, and other sensing technologies

• Ex-situ NDT and quality assurance of additively manufactured components

• Process monitoring developed specifically for laser powder bed fusion (LPBF), directed energy deposition (DED), wire arc additive manufacturing (WAAM), binder jetting, and other AM processes

• Porosity, cracking, residual stress, and microstructure characterization

• Geometry, layer height, and surface quality monitoring

• Machine learning and artificial intelligence for defect detection and quality prediction

• New sensor development for additive manufacturing processes

• Melt pool monitoring and process control

• Industrial case studies and qualification of AM parts

• Standards, certification, and reliability of NDT methods for AM

Submission Information:

Authors are encouraged to submit high-quality original research articles, review papers, and industrial case studies. Review articles are expected to provide critical insights, identify current challenges, and discuss future research directions rather than simply summarizing published literature. To facilitate the submission process, interested authors are encouraged to send a tentative title and a brief abstract to the Guest Editors for preliminary assessment and feedback at their earliest convenience.

All submissions will undergo the journal's standard peer-review process.

Submission Deadline: 1 January 2027

Guest Editors:

• Jin-Yeon Kim, Georgia Institute of Technology (jykim@gatech.edu)

• Hossein Taheri Georgia Southern University (htaheri@georgiasouthern.edu)

We look forward to receiving your contributions and showcasing the latest advances in nondestructive testing technologies for additive manufacturing.

Deadline: 15 September 2026 (Please include in the cover letter that you are submitting for the Edge Computing issue.)
Publication Date: January 2027

Recent advancements in artificial intelligence (AI) and data-driven technologies are creating new opportunities and revolutionizing the field of nondestructive evaluation (NDE) in many ways. Improvements in sensing, data acquisition, computing hardware, and communication infrastructure—combined with modern machine learning (ML) and deep learning (DL) methods—are enabling faster, more reliable, and increasingly autonomous inspection systems. Edge computing, in which data processing and decision-making occur at the point of acquisition, is emerging as a critical enabler of real-time NDE for in-service and production environments where latency, bandwidth, power, and operational constraints are paramount. It presents unique opportunities to transform traditional NDE workflows by enabling on-device signal processing, defect detection, and asset health management without reliance on continuous cloud connectivity.

The focus of this upcoming special Technical Focus Issue of Materials Evaluation is to consolidate recent research activities and practical advancements that leverage edge computing to advance the state of NDE through the local or on-board application of AI/ML. These include but are not limited to: (a) the design and development of novel algorithms and data processing strategies for edge computing, including NDE data acquisition, analysis, modeling, or prediction; (b) implementation and evaluation of edge computing hardware for asset health monitoring; and (c) data-driven methods for extracting relevant information from edge computing datasets compared to conventional approaches in terms of speed, accuracy, robustness, or deployment.

Manuscripts submitted to this Technical Focus Issue must demonstrate a clear and significant contribution to the field of NDE, with carefully documented methodology, results, and reporting of model performance compared to conventional approaches. In keeping with best practices in the AI/ML community, authors are strongly encouraged to make relevant code, models, or workflows available through public repositories (e.g., GitHub), where appropriate. Journal homepage and author guidelines are available at asnt.org/me.

Scope:

• Edge AI architectures for NDE

• Real-time NDE signal and image processing at the edge

• Edge-enabled deep learning for defect detection

• Applications for asset inspections

• Edge–cloud collaboration in NDE systems

• Autonomous and robotic NDE

• Multimodal sensor fusion

• Digital twins powered by edge devices

• Reliability, safety, and explainability at the edge

Editors:

• Anish Poudel, PhD, MxV Rail: anish_poudel@aar.com

• Matthew R. Webster, NASA: matthew.r.webster@nasa.gov