Optimization and Machine Learning in Metal Additive Manufacturing

Dear Colleagues,

Unlike traditional subtractive manufacturing methods that involve cutting or shaping a material to obtain the desired object, additive manufacturing (AM) builds three-dimensional objects by adding successive layers of material until the final product is formed. This technology has gained significant attention and has various applications across industries, owing to its versatility and ability to produce complex geometries.

Although AM has seen significant advancements in recent years, it still faces several challenges that impact its widespread adoption and implementation. This includes issues related to material limitations, process control and reproducibility, post-processing requirements, scale and speed, and design and cost.

In response to these challenges, machine learning has emerged as an active area of research and development, aiming to improve efficiency, reliability, and overall capabilities of additive manufacturing processes.

In this Special Issue, we welcome articles on advances in the application of artificial intelligence and machine learning in metal additive manufacturing, related among other to:

• Material characterization and selection, including through the analysis of material properties, performance data, and historical trends to identify suitable materials for specific applications and to predict their behavior in AM processes.
• Design optimization by leveraging machine learning algorithms to generate and evaluate numerous design iterations to identify optimal geometries, lattice structures, and support strategies that improve performance, reduce weight, or enhance specific properties.
• Process variable optimization, where machine learning models can be used to identify the optimal combination of variable settings and process parameters for achieving desired outcomes.
• Process monitoring, defect detection, and quality control, where machine learning models can exploit images, sensor data, or acoustic signals to identify defects and assist in corrective action.

Prof. Dr. Chris Aldrich
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.