Submission Deadline: 30 December 2024 View: 418 Submit to Special Issue
Additive manufacturing (AM) or 3D printing with potential benefits to the automation, low cost, rapid prototyping, and customizability can greatly outperform conventional manufacturing technologies that suffer from time-consuming and labor-intensive problems during operation. However, the complex underlying physics of AM processes also brings challenges in understanding the relationship between process parameters and quality of the product, thereby hindering industrial applications that harness the full potential of AM.
With the increasing availability of digital AM data and rapid development of data-driven modeling techniques, especially machine learning (ML), data-driven AM modeling is emerging as an effective approach towards this end. It allows for automatic exploration of pattern and trend in the data, construction of quantitative process-structure-property (P-S-P) relationship over the parameter space and prediction at unseen points without having to perform new physical modeling or experiments. In this way, data-driven methods can reduce the expensive experiments and time-consuming physics-simulations to create the P-S-P database which can quantify the quality of the product and inform the design of materials and manufacturing processes to optimize the quality of the product. In addition, data-driven approaches, typically off-line trained, can be used to achieve real-time and online prediction of AM processes that can be used as the backbone to real-time control or optimize the process parameters for smart AM manufacturing. Here, we just name two applications of data-driven approaches which have potential to benefit AM technologies from many aspects. Therefore, this special issue aims to highlight data-driven modeling of additive manufacturing, including development of novel data-driven approaches, application of existing data-driven approaches and the hybrid to promote the AM technologies. This special issue will be a collection of peer-reviewed contributions that present original breakthrough research, comprehensive reviews, perspectives, or highlights to advance the frontier of data-driven modeling of additive manufacturing.
Contributed articles are sought in the following areas (not limited):
· Advances in development of novel data-driven approaches for additive manufacturing
· Advances in data-driven modeling of process-structure-property relation for additive manufacturing
· Advances in data-driven material selection and design for additive manufacturing
· Advances in data-driven modeling of topology optimization for additive manufacturing
· Advances in big (sensor) data analytics using data-driven modeling for smart additive manufacturing
· Advances in data collection, denoising and sorting for training of additive manufacturing
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