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Special Issue Information

Dear Colleagues,

Additive manufacturing (AM) is a new type of manufacturing engineering with less than 35 years of history. The real value of additive manufacturing is in identifying those applications where reductions in lead time, manufacturing cost, weight, tooling, and so on can lead to huge benefits across a part’s lifecycle in many applications from industry to medicine.

Additive manufacturing has evolved rapidly in the last few years. It has been embraced by major industrial companies looking for ways to improve their products. The ability to deliver near-instant part production and fully custom designs that cannot be replicated with other manufacturing techniques has accelerated investment and research in additive engineering.

A number of different metals are now available in powdered form to suit exact processes and requirements. Titanium, steel, stainless steel, aluminum, and copper-, cobalt chrome-, titanium- and nickel-based alloys are available in powdered form, as are precious metals such as gold, platinum, palladium, and silver.

This Special Issue will cover fundamental studies of additive manufacturing process, optimizations, new additive processes, rapid tooling, and applications from industry to medicine using metal powders as raw materials.

I hope that the present Special Issue will be an opportunity for creating a strong network between authors and users, working in some different sectors, for smart applications from industry to medicine.

Prof. Dr. Petru Berce
Guest Editor

Manuscript Submission Information

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Related Special Issue

Published Papers (1 paper)

Research

21 pages, 33254 KiB

Open AccessArticle

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

by Haitao Zhang, Xingwang Bai, Honghui Dong and Haiou Zhang

Metals 2024, 14(5), 567; https://0-doi-org.brum.beds.ac.uk/10.3390/met14050567 - 11 May 2024

Viewed by 449

Abstract

Wire arc additive manufacturing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposition rate. One of the basic tasks in WAAM is to determine appropriate process parameters, which will directly affect the morphology and quality of the weld bead. However, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expenditures. This paper employed different machine learning models, including SVR, BPNN, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR model was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing algorithm as the training data. The K-fold cross-validation strategy was applied to train and validate machine learning models. The comparison results showed that PSO–SVR predicted process parameters with the highest precision, with the RMSE, R², and MAE being 1.1670, 0.9879, and 0.8310, respectively. Based on the process parameters produced by PSO–SVR, an optimal process parameter combination was chosen by taking into comprehensive consideration the impacts of power consumption and efficiency. The effectiveness of the process parameter optimization method was proved through three groups of validation experiments, with the energy consumption of the first two groups decreasing by 10.68% and 11.47%, respectively. Full article

(This article belongs to the Special Issue Advances in Additive Manufacturing and Their Applications (2nd Edition))

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