Additive Manufacturing: Experiments, Simulations and Data-Driven Modelling (Second Edition)

Dear Colleagues,

Additive manufacturing (AM) utilizes a layer-upon-layer technique to produce three-dimensional (3D) components via a computer-aided design (CAD) model, promising manufacturing advantages compared to conventional approaches, such as manufacturing intricate geometries, controlling the heat-affected zone, and removing numerous technological steps, thus reducing the final manufacturing cost. AM has been explored in automotive, biomedical, aerospace, and industrial applications based on the facts mentioned above, and it has been widely applied to various materials, including metals, ceramics, polymers, and ceramic-reinforced metal matrix composites (CMMCs), a mixture of metals and ceramics. In AM, operating conditions influence the properties of the manufactured parts and the in-service life. One way to determine the optimum operating conditions is to conduct a series of experiments by utilizing the trial-and-error method, increasing the manufacturing direct and indirect costs. On the other hand, this can be performed by using an experimentally validated simulation model linked with data-driven models (machine learning), leading towards smart manufacturing.

The primary purpose of this Special Issue of Crystals, titled “Additive Manufacturing: Experiments, Simulations and Data-driven Modelling”, is to deliver an intercontinental forum for ground-breaking research on process parameters’ optimization, the development of new alloys and compounds, ceramics and CMMCs for AM experiments, sophisticated simulation model development, and the implementation of machine learning in AM processes. 

Dr. Muhammad Arif Mahmood
Dr. Asif Ur Rehman
Prof. Dr. Marwan Khraisheh
Prof. Dr. Metin U. Salamci
Dr. Rashid Ur Rehman
Dr. Uzair Sajjad
Dr. Carmen Ristoscu
Dr. Andrei C. Popescu
Dr. Mihai Oane
Prof. Dr. Ion N. Mihailescu
Prof. Dr. Frank Liou
Guest Editors

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