Advances in Manufacturing and Mechanics of Materials

Topic Information

Dear Colleagues,

This Topic aims to provide a multidisciplinary platform for recent advances in the processing, fabrication, and mechanical behavior of materials across various manufacturing domains. We welcome original research and review articles on topics including, but not limited to, the following:

• Novel manufacturing processes (e.g., additive manufacturing, joining technologies, surface engineering);
• Microstructure–property relationships in metallic, ceramic, polymeric, and composite materials;
• Process–structure–performance modeling and simulation;
• Mechanics and failure analysis under complex loading conditions;
• Integration of data-driven approaches (AI/ML) in materials design and process optimization;
• Sustainable and energy-efficient approaches in advanced manufacturing.

This Topic encourages collaboration between materials scientists, mechanical engineers, and manufacturing experts to accelerate innovation in both academic and industrial applications.

Dr. Young-Min Kim
Dr. Minjung Kang
Dr. Duck Bong Kim
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Mechanics applmech	1.5	3.5	2020	20.4 Days	CHF 1400	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Journal of Manufacturing and Materials Processing jmmp	3.3	5.2	2017	16.2 Days	CHF 1800	Submit
Materials materials	3.2	6.4	2008	15.2 Days	CHF 2600	Submit

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13 pages, 3880 KB  

Open AccessArticle

Investigation of Cutting Forces and Temperature in Face Milling of Wood–Plastic Composite Using Radial Basis Function Neural Network

by Feng Ji and Zhaolong Zhu

Materials 2025, 18(20), 4731; https://doi.org/10.3390/ma18204731 - 15 Oct 2025

Viewed by 444

Abstract

Wood–plastic composite (WPC) is being increasingly adopted in construction and furniture applications due to its durability and recyclability. This study investigates face-milling responses—resultant cutting force and cutting temperature—under systematically varied cutting parameters, and develops a radial basis function neural network for predictive modeling. [...] Read more.

Wood–plastic composite (WPC) is being increasingly adopted in construction and furniture applications due to its durability and recyclability. This study investigates face-milling responses—resultant cutting force and cutting temperature—under systematically varied cutting parameters, and develops a radial basis function neural network for predictive modeling. Experiments were conducted on a computer numerical control machining center using a polycrystalline diamond end-milling cutter for face milling with fixed axial depth of cut. Feed speed, radial depth of cut, and spindle speed were selected as input factors. The results indicate that feed speed and radial depth of cut generally increase all force components, whereas higher spindle speed tends to reduce force magnitudes while elevating temperature. The radial basis function neural network yields acceptable accuracy for resultant cutting force (coefficient of determination R² ≈ 0.91) and acceptable accuracy for cutting temperature (R² ≈ 0.81). These findings demonstrate the feasibility of radial basis function neural network based prediction for WPC face milling and provide guidance for parameter selection. Full article

(This article belongs to the Topic Advances in Manufacturing and Mechanics of Materials)

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