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Processes
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Recent Trends in Advanced Manufacturing Technologies for Materials Processing and...
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Recent Trends in Advanced Manufacturing Technologies for Materials Processing and Production

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 1 November 2025 | Viewed by 1965

Special Issue Editors

Dr. Shahrokh Hatefi

E-Mail Website
Guest Editor
Department of Mechatronics, Faculty of Engineering, The Built Environment and Technology, Nelson Mandela University, Port Elizabeth 6000, South Africa
Interests: manufacturing processes; optics; ultra-precision manufacturing; hybrid processes; 3D printing technologies; mechatronics; advanced control systems; medical devices
Special Issues, Collections and Topics in MDPI journals
Dr. Yimesker S. Yihun

E-Mail Website
Guest Editor
Department of Mechanical Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
Interests: robotics; control system design; human-robot interaction, mechanism synthesis; applied mechanics
Prof. Dr. Farouk Smith

E-Mail Website
Guest Editor
Department of Mechatronics, Faculty of Engineering, The Built Environment and Technology, Nelson Mandela University, Port Elizabeth 6000, South Africa
Interests: mechatronics; satellite systems; satellite onboard computers; radiation effects on electronics; radiation testing of electronics; micro-devices; robotics; electronics

Special Issue Information

Dear Colleagues,

Advanced manufacturing describes the use of innovative technology to improve products or processes, with the relevant technology being described as advanced, innovative, or cutting edge. Advanced manufacturing is a multi-disciplinary research area that boosts productivity and efficacy in both products and processes in several ways. Advanced processes/products have a wide range of applications in various fields including aerospace, military, biomedical engineering, agricultural systems, etc.

This Special Issue focuses on recent trends and advanced manufacturing technologies for materials processing and production. Advanced control systems as well as advanced processes are key elements in the development of new methods, non-conventional manufacturing techniques, and production methods. It is important to develop novel systems and methods to promote green manufacturing while optimizing the outcome of the processes. Hybrid methods can catalyze innovation, reduce production costs, and advance the development of cutting-edge materials and processes, establishing a paradigm shift in the landscape of advanced manufacturing while promoting green manufacturing. Furthermore, the application of artificial intelligence (AI) and atomistic modeling (Molecular Dynamics) can optimize different manufacturing processes for more efficiency and precision.

This Special Issue seeks research papers on the recent advances and frontiers in advanced manufacturing technologies in different fields related to engineering and industry. We call for papers in advanced manufacturing, material processing, and production methods that contribute to the advances of new principles, mechanisms, and methods. Original research papers, review articles, and short communications are all welcome.

Dr. Shahrokh Hatefi
Dr. Yimesker S. Yihun
Prof. Dr. Farouk Smith
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • green processing and synthesis
  • sustainable manufacturing
  • hybrid manufacturing processes
  • non-conventional machining methods
  • automatic assembly
  • ultra-high-precision manufacturing
  • optics
  • 3D printing technologies
  • biocompatible materials
  • micro- and nano-manufacturing
  • robotics and mechatronic systems
  • applied mechanical systems
  • advanced control systems
  • artificial intelligence in manufacturing processes
  • smart agricultural processes (smart production)
  • metrology systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

25 pages, 3041 KiB  
Article
Investigation of Surface Quality and Productivity in Precision Hard Turning of AISI 4340 Steel Using Integrated Approach of ML-MOORA-PSO
by Adel T. Abbas, Neeraj Sharma, Khalid F. Alqosaibi, Mohamed A. Abbas, Rakesh Chandmal Sharma and Ahmed Elkaseer
Processes 2025, 13(4), 1156; https://doi.org/10.3390/pr13041156 - 10 Apr 2025
Viewed by 409
Abstract
AISI 4340 steel has applications in gun barrels, where the surface quality of the barrel is the prime factor. This study explores the application of a machine learning (ML) approach to optimize the precision turning of an AISI 4340 steel alloy using both [...] Read more.
AISI 4340 steel has applications in gun barrels, where the surface quality of the barrel is the prime factor. This study explores the application of a machine learning (ML) approach to optimize the precision turning of an AISI 4340 steel alloy using both conventional and wiper tool nose inserts under varying cutting parameters, such as cutting speed, depth of cut, and feed rate. The analytical framework integrates experimental machining data with computational algorithms to predict key output parameters: surface roughness (Ra) and material removal rate (MRR). A Multi-Objective Optimization based on Ratio Analysis (MOORA) method is used for data normalization. Particle swarm optimization (PSO) further refines the process by optimizing the input parameters to achieve superior machining performance. Results show that under optimized conditions, a 118 m/min cutting speed, 0.22 mm depth of cut, and 0.2 mm/rev feed, wiper inserts provide a 50% improvement in Ra compared to conventional inserts, highlighting their potential for enhancing both productivity and efficiency. At the suggested setting, the surface roughness values are 0.59 µm for wiper inserts and 1.30 µm for conventional inserts, with a material removal rate of 4996.96 mm3/min. The developed empirical model serves as a powerful tool for improving precision hard-turning processes across manufacturing sectors. The present work employs the XGBoost model of ML along with MOORA and PSO to predict and optimize machining outcomes, advancing hard-turning practices by delivering quantifiable improvements in surface quality, material removal rate, and operational efficiency. Full article
(This article belongs to the Special Issue Recent Trends in Advanced Manufacturing Technologies for Materials Processing and Production)
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18 pages, 3218 KiB  
Article
Optimized Machining Parameters for High-Speed Turning Process: A Comparative Study of Dry and Cryo+MQL Techniques
by Nabil Jouini, Jaharah A. Ghani, Saima Yaqoob and Afifah Zakiyyah Juri
Processes 2025, 13(3), 739; https://doi.org/10.3390/pr13030739 - 4 Mar 2025
Viewed by 686
Abstract
Hard turning is a precision machining process used to cut materials with hardnesses exceeding 45 HRC using single-point tools. It offers an efficient alternative to traditional grinding for finishing operations in manufacturing. This paper explores the machinability of hardened AISI 4340 steel for [...] Read more.
Hard turning is a precision machining process used to cut materials with hardnesses exceeding 45 HRC using single-point tools. It offers an efficient alternative to traditional grinding for finishing operations in manufacturing. This paper explores the machinability of hardened AISI 4340 steel for a hard turning process utilizing dry and cryogenic (Cryo) plus minimum quantity lubrication (MQL) (Cryo+MQL) techniques, focusing on critical machinability aspects such as cutting force, surface roughness, and tool life. The orthogonal dry turning was performed with a cutting speed (V) ranging from 300–400 m/min, a feed rate (f) between 0.05 and 1 mm/rev, and a depth of cut (doc) from 0.1 to 0.3 mm. A statistical analysis of the obtained results revealed that the feed rate was the most influential parameter, contributing 50.69% to the main cutting force and 80.03% to surface roughness. For tool life, cutting speed was identified as the dominant factor, with a contribution rate of 39.73%. Multi-objective optimization using Grey relational analysis (GRA) identified the optimal machining parameters for the hard turning of AISI 4340 alloy steel as V = 300 m/min, f = 0.05 mm/rev, and doc = 0.1 mm. The Cryo+MQL technique was subsequently applied to these parameters, yielding significant improvements, with a 48% reduction in surface roughness and a 184.5% increase in tool life, attributed to enhanced lubrication and cooling efficiency. However, a slight 4.6% increase in cutting force was observed, likely due to surface hardening induced by the low-temperature LN2 cooling. Furthermore, reduced adhesion and tool fracture on the principal cutting edge under Cryo+MQL conditions justify the superior surface quality and extended tool life achieved. This research highlights the industrial relevance of hybrid lubrication in addressing challenges associated with hard turning processes. Full article
(This article belongs to the Special Issue Recent Trends in Advanced Manufacturing Technologies for Materials Processing and Production)
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24 pages, 9671 KiB  
Article
Surface Topography Analysis and Surface Roughness Prediction Model of Diamond Wire-Sawed NdFeB Magnet Based on Optimized Back Propagation Neural Network
by Guanzheng Li, Xingchun Zhang, Yufei Gao, Fan Cui and Zhenyu Shi
Processes 2025, 13(2), 546; https://doi.org/10.3390/pr13020546 - 15 Feb 2025
Viewed by 410
Abstract
Wire sawing is an important process in the cutting of NdFeB magnets and the sawed surface topography and surface roughness (SR) are important indicators for assessing surface quality. This paper analyzed the effects of process parameters on the sawed NdFeB surface topography and [...] Read more.
Wire sawing is an important process in the cutting of NdFeB magnets and the sawed surface topography and surface roughness (SR) are important indicators for assessing surface quality. This paper analyzed the effects of process parameters on the sawed NdFeB surface topography and SR based on orthogonal experiments and then presented an SR prediction model called ISSA-BP, which was based on a BP neural network using an improved sparrow search algorithm (ISSA). For the problem of insufficient optimization capability of the traditional sparrow search algorithm (SSA), Cubic chaotic mapping, Latin hypercube sampling, the sine–cosine algorithm, Levy flight, and Cauchy mutation were introduced to improve the traditional sparrow search algorithm (SSA) to obtain ISSA, improving algorithm convergence speed and global optimization. The ISSA was then used to optimize the initial weights and thresholds of the BP neural network for predicting Ra. Research shows that the sawed surface topography reflects a combination of brittle and ductile material removal. As the workpiece feed speed and size decrease and the wire speed increases, there is a reduction in SR. Compared with the SSA-BP and traditional BP models, the ISSA-BP prediction model has reduced various error indicators such as mean absolute error (MAE) and mean square error (MSE). The mean absolute error (MAE) of the prediction model optimized by the ISSA is 0.064475, the mean square error (MSE) is 0.0072297, the root mean square error (RMSE) is 0.085028, and the mean absolute percentage error (MAPE) is 3.7171%. The research results provide an experimental basis and technical support for predicting the SR and optimizing the process parameters in diamond wire-sawing NdFeB. Full article
(This article belongs to the Special Issue Recent Trends in Advanced Manufacturing Technologies for Materials Processing and Production)
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