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	24.5 Days	CHF 1400	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Journal of Manufacturing and Materials Processing jmmp	3.3	5.2	2017	15.9 Days	CHF 1800	Submit
Materials materials	3.2	6.4	2008	15.5 Days	CHF 2600	Submit
Metals metals	2.5	5.3	2011	18.7 Days	CHF 2600	Submit
Nanomanufacturing nanomanufacturing	-	-	2021	23.5 Days	CHF 1000	Submit

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Published Papers (3 papers)

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All Applied Mechanics Applied Sciences JMMP Materials Metals Nanomanufacturing

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28 pages, 6736 KB  

Open AccessArticle

Optimizing the Effect of Nanochitosan and Kenaf Fiber on Tensile and Impact Properties of Polylactic Acid (PLA)/Natural Rubber (SMR20) Biocomposites

by Habib Shorekandi, Nima Refahati and Meysam Nouri Niyaraki

Appl. Mech. 2026, 7(1), 12; https://doi.org/10.3390/applmech7010012 - 29 Jan 2026

Viewed by 217

Abstract

In this study, the influence of nanochitosan and kenaf fibers on the tensile strength, elastic modulus, and impact strength of polylactic acid (PLA)/natural rubber (Standard Malaysian Rubber, grade 20—SMR20) biocomposites was investigated experimentally using Response Surface Methodology (RSM). The independent variables included the [...] Read more.

In this study, the influence of nanochitosan and kenaf fibers on the tensile strength, elastic modulus, and impact strength of polylactic acid (PLA)/natural rubber (Standard Malaysian Rubber, grade 20—SMR20) biocomposites was investigated experimentally using Response Surface Methodology (RSM). The independent variables included the weight percentage of nanochitosan (2, 4, and 6 wt%), kenaf fibers (5, 10, and 15 wt%), and SMR20 natural rubber (10, 20, and 30 wt%). Composite samples were prepared by melt mixing in an internal mixer and subsequently fabricated into test samples using hot compression molding in accordance with relevant standards. Tensile tests were conducted to evaluate tensile strength and elastic modulus, while Charpy impact tests were performed to assess impact strength. The results revealed that increasing nanochitosan content up to 4 wt% enhanced tensile strength, elastic modulus, and impact strength by 39%, 22%, and 27%, respectively; however, further addition (6 wt%) led to a decline in these properties due to nanoparticle agglomeration. Increasing kenaf fiber content to 15 wt% improved tensile strength, elastic modulus, and impact strength by 44%, 26%, and 37%, respectively, demonstrating their effective reinforcing role. The incorporation of SMR20 natural rubber significantly increased impact strength by 59% (at 30 wt%), while causing a reduction of 17% in tensile strength and 20% in elastic modulus, consistent with its elastomeric nature. Furthermore, field emission scanning electron microscopy (FESEM) was employed to examine the dispersion of nanochitosan and kenaf fibers within the PLA/SMR20 matrix, providing insights into the interfacial adhesion and failure mechanisms. The findings highlight the potential of optimizing natural filler and rubber content to tailor the mechanical performance of sustainable PLA-based biocomposites. Full article

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

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15 pages, 3118 KB  

Open AccessArticle

Fracture-Toughness-Based Methodology for Determination of 3D-Printed Specimen Using Digital Image Correlation

by Ali Makke, Julien Gardan, Naman Recho and Marouene Zouaoui

Appl. Mech. 2026, 7(1), 3; https://doi.org/10.3390/applmech7010003 - 2 Jan 2026

Viewed by 343

Abstract

This methodology investigates the determination of the fracture toughness of 3D-printed specimens under monotonic loading conditions. The application is based on the use of a Single Edge Notch Bending (SENB) specimen made by a 3D-printing process (17-4PH stainless steel). The load–displacement curves exhibited [...] Read more.

This methodology investigates the determination of the fracture toughness of 3D-printed specimens under monotonic loading conditions. The application is based on the use of a Single Edge Notch Bending (SENB) specimen made by a 3D-printing process (17-4PH stainless steel). The load–displacement curves exhibited linear behavior until crack initiation, indicating that the Linear Elastic Fracture Mechanics (LEFM) can be used under a small-scale yielding assumption. This study extends a previous methodology, originally applied to a polymer, to a metal additively manufactured material. The methodology established in the paper represents a major outcome: the ability to characterize the fracture toughness of the material. This study extends our previous Digital Image Correlation-based methodology from thermoplastic polymers to 17-4PH stainless steel produced by metal additive manufacturing (ADAM). Its novelty lies in combining DIC with a finite element sub-model to evaluate fracture parameters, enabling accurate crack initiation detection in challenging metal AM specimens, and providing a methodology that can be generalized to other metals and AM processes. The aim of this study is to establish a robust DIC-based methodology for the identification of crack initiation and the determination of fracture toughness parameters (K_IC and J) in 3D-printed 17-4PH stainless steel produced by the ADAM process. Full article

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

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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

Cited by 1 | Viewed by 597

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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