Intelligent Precision in Additive Manufacturing and Post-Processing
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 636
Special Issue Editors
Interests: robotics and drones; industrial aerodynamics and composite materials
Special Issue Information
Dear Colleagues,
Additive manufacturing (AM) is a fast-growing technology which is now commercially used by industries on a large scale. Due to its ability to manufacture customised designs and the free-form fabrication of complex shapes, it has received tremendous attention. Although industries are now using AM processes, the precision of AM parts is still a major hurdle to full adoption and wider market acceptance. Lack of precision in additively manufactured parts has led to additional post-processing, which requires employing conventional subtractive manufacturing or non-conventional techniques to achieve a good surface finish and geometric accuracy. However, minimising post-processing operations on the AM parts is dependent on the precision of the AM process. Several other factors that affect precision include poor performance of the equipment, inappropriate selection of process parameters, lack of monitoring and controlling, and lack of understanding of the nature of AM processes. This Special Issue aims to provide a platform for the AM research community to contribute state-of-the-art developments in the field of precision for AM and its influence on post-processing. For this Special Issue, we invite papers that provide insights into precision and post-processing in AM through experimental and/or theoretical aspects covering one or more of the example areas detailed below:
- Precision challenges in the additive manufacturing process;
- Surface finish and dimension control;
- Process monitoring and controlling;
- Process parameter optimisation;
- AI technologies for precision and post-processing;
- Integration of post-processing methods for, e.g., hybrid AM processes;
- Prediction performance through modelling and simulation.
Dr. Brijesh Patel
Dr. Sagar Nikam
Guest Editors
Manuscript Submission Information
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Keywords
- hybrid AM
- AI for process improvement
- post-processing methods
- surface finish and dimension accuracy
- digital technology
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