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Latest Developments in Advanced Machining Technologies for Materials
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Latest Developments in Advanced Machining Technologies for Materials

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 4736

Special Issue Editors

Dr. Jiaming Zhan

E-Mail Website
Guest Editor
School of Advanced Manufacturing, Sun Yat-sen University, Shenzhen 518107, China
Interests: ultra-precision machining; computational solid mechanics; ductile-regime cutting; nanomechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Yuchao Bai

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Interests: additive manufacturing; advanced manufacturing; hybrid additive/subtractive manufacturing; ultra-precision machining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of the rapid development of modern manufacturing, advanced machining technologies play a pivotal role in enhancing the precision, efficiency, and sustainability of material processing. The latest innovations in these technologies continuously break traditional limitations by integrating intelligent control, digital management, and novel tools and equipment, enabling the efficient processing of a wide range of materials including metals, ceramics, and composites. In-depth research into the relationship between machining parameters and material properties not only optimizes production processes but also provides the scientific foundation and practical guidance necessary for achieving green manufacturing.

This Special Issue of Materials titled "Latest Developments in Advanced Machining Technologies for Materials" is designed to showcase cutting-edge advancements in the field, with a particular focus on key technologies such as hybrid machining, micro/nano machining, laser-assisted machining, and digital manufacturing. We cordially invite researchers and engineering experts to submit their contributions, exploring both the practical applications and future trends of advanced machining technologies and fostering continuous innovation and progress in material processing.

Dr. Jiaming Zhan
Dr. Yuchao Bai
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 250 words) can be sent to the Editorial Office for assessment.

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. Materials is an international peer-reviewed open access semimonthly 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 2600 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

  • advanced machining
  • hybrid machining
  • micro/nano machining
  • digital manufacturing
  • process optimization
  • sustainable manufacturing

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Published Papers (5 papers)

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Research

15 pages, 6439 KB  
Article
Multi-Objective Process Optimization of Micro-Milling Titanium Alloy Ti6Al4V for Microgrooves
by Yabo Zhang, Chenyang Wang, Qingshun Bai, Qiqin Zhang and Xin He
Materials 2026, 19(10), 2142; https://doi.org/10.3390/ma19102142 - 20 May 2026
Viewed by 170
Abstract
High-quality microgrooves obtained in micro-milling titanium alloy Ti6Al4V are still challenging work due to the dependence of burr formation and surface roughness on cutting parameters. In this paper, the systematic analysis of the micro-milling process was conducted to obtain high-quality titanium alloy Ti6Al4V [...] Read more.
High-quality microgrooves obtained in micro-milling titanium alloy Ti6Al4V are still challenging work due to the dependence of burr formation and surface roughness on cutting parameters. In this paper, the systematic analysis of the micro-milling process was conducted to obtain high-quality titanium alloy Ti6Al4V microgrooves, which is based on single-factor experiments, orthogonal experiments, intuitive analysis, range analysis, regression analysis, and multi-objective optimization. The range of factors and factors of orthogonal experiments were determined by single-factor experiments. Orthogonal experiments were conducted with a three-factor three-level design, which regards the total top-burr width and the bottom surface roughness of microgrooves as the response variables, and factors are spindle speed, feed per tooth, and the axial depth of cut. The optimal cutting parameters, which minimize the surface roughness and burr formation, and the main influence factor were determined by intuitive analysis, range analysis, regression analysis, and NSGA-II multi-objective optimization. Simultaneously, high-quality complex microgrooves were achieved with the optimal cutting parameters. The method of systematic experimental design and data analysis in this paper can provide the theoretical guideline and technical support for the processing development of complex parts. Full article
(This article belongs to the Special Issue Latest Developments in Advanced Machining Technologies for Materials)
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21 pages, 5711 KB  
Article
A Study on High-Precision Dimensional Measurement of Irregularly Shaped Carbonitrided 820CrMnTi Components
by Xiaojiao Gu, Dongyang Zheng, Jinghua Li and He Lu
Materials 2026, 19(8), 1491; https://doi.org/10.3390/ma19081491 - 8 Apr 2026
Viewed by 342
Abstract
For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous [...] Read more.
For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article
(This article belongs to the Special Issue Latest Developments in Advanced Machining Technologies for Materials)
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27 pages, 11421 KB  
Article
An Improved Multi-Objective Grey Wolf Optimizer for Bi-Objective Parameter Optimization in Single Point Incremental Forming of Al1060 Sheet
by Xiaojing Zhu, Xinyue Zhang, Jianhai Jiang, Xiaotao Wu, Shenglong Liao, Jianfang Huang and Yuhuai Wang
Materials 2026, 19(3), 616; https://doi.org/10.3390/ma19030616 - 5 Feb 2026
Viewed by 646
Abstract
To address the issues of excessive sheet metal thinning and geometric deviation in single point incremental forming (SPIF), this paper proposed a bi-objective process parameter optimization framework for Al1060 sheet based on a multilayer perceptron (MLP) surrogate model and an improved multi-objective grey [...] Read more.
To address the issues of excessive sheet metal thinning and geometric deviation in single point incremental forming (SPIF), this paper proposed a bi-objective process parameter optimization framework for Al1060 sheet based on a multilayer perceptron (MLP) surrogate model and an improved multi-objective grey wolf optimization (IMOGWO) algorithm. Finite element simulations based on ABAQUS were conducted to generate a dataset considering variations in tool radius, initial sheet thickness, tool path strategy, step depth and forming angle. The trained MLP was used as the objective function in the optimization process to enable the rapid prediction of forming quality. The IMOGWO algorithm, enhanced by the Spm chaotic mapping initialization, an improved convergence coefficient updating mechanism and associative learning mechanism, was then employed to efficiently search for Pareto optimal solutions. For a truncated conical component case, optimal parameter sets were selected from the Pareto front via the entropy-weighted TOPSIS method for order preference by similarity to an ideal solution. Experimental verification showed close agreement with the simulated results, with relative errors of only 0.58% for the thinning rate and 3.10% for the geometric deviation. This validation demonstrates the feasibility and potential of the proposed method and its practical potential for improving the quality of SPIF forming. Full article
(This article belongs to the Special Issue Latest Developments in Advanced Machining Technologies for Materials)
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15 pages, 2158 KB  
Article
A Data-Driven Approach for Internal Crack Prediction in Continuous Casting of HSLA Steels Using CTGAN and CatBoost
by Mengying Geng, Haonan Ma, Shuangli Liu, Zhuosuo Zhou, Lei Xing, Yibo Ai and Weidong Zhang
Materials 2025, 18(15), 3599; https://doi.org/10.3390/ma18153599 - 31 Jul 2025
Cited by 3 | Viewed by 1139
Abstract
Internal crack defects in high-strength low-alloy (HSLA) steels during continuous casting pose significant challenges to downstream processing and product reliability. However, due to the inherent class imbalance in industrial defect datasets, conventional machine learning models often suffer from poor sensitivity to minority class [...] Read more.
Internal crack defects in high-strength low-alloy (HSLA) steels during continuous casting pose significant challenges to downstream processing and product reliability. However, due to the inherent class imbalance in industrial defect datasets, conventional machine learning models often suffer from poor sensitivity to minority class instances. This study proposes a predictive framework that integrates conditional tabular generative adversarial network (CTGAN) for synthetic minority sample generation and CatBoost for classification. A dataset of 733 process records was collected from a continuous caster, and 25 informative features were selected using mutual information. CTGAN was employed to augment the minority class (crack) samples, achieving a balanced training set. Feature distribution analysis and principal component visualization indicated that the synthetic data effectively preserved the statistical structure of the original minority class. Compared with the other machine learning methods, including KNN, SVM, and MLP, CatBoost achieved the highest metrics, with an accuracy of 0.9239, precision of 0.9041, recall of 0.9018, and F1-score of 0.9022. Results show that CTGAN-based augmentation improves classification performance across all models. These findings highlight the effectiveness of GAN-based augmentation for imbalanced industrial data and validate the CTGAN–CatBoost model as a robust solution for online defect prediction in steel manufacturing. Full article
(This article belongs to the Special Issue Latest Developments in Advanced Machining Technologies for Materials)
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17 pages, 5457 KB  
Article
Multiphysics Modeling of Heat Transfer and Melt Pool Thermo-Fluid Dynamics in Laser-Based Powder Bed Fusion of Metals
by Tingzhong Zhang, Xijian Lin, Yanwen Qin, Dehua Zhu, Jing Wang, Chengguang Zhang and Yuchao Bai
Materials 2025, 18(13), 3183; https://doi.org/10.3390/ma18133183 - 5 Jul 2025
Cited by 4 | Viewed by 1881
Abstract
Laser-based powder bed fusion of metals (PBF-LB/M) is one of the most promising additive manufacturing technologies to fabricate complex-structured metal parts. However, its corresponding applications have been limited by technical bottlenecks and increasingly strict industrial requirements. Process optimization, a scientific issue, urgently needs [...] Read more.
Laser-based powder bed fusion of metals (PBF-LB/M) is one of the most promising additive manufacturing technologies to fabricate complex-structured metal parts. However, its corresponding applications have been limited by technical bottlenecks and increasingly strict industrial requirements. Process optimization, a scientific issue, urgently needs to be solved. In this paper, a three-phase transient model based on the level-set method is established to examine the heat transfer and melt pool behavior in PBF-LB/M. Surface tension, the Marangoni effect, and recoil pressure are implemented in the model, and evaporation-induced mass and thermal loss are fully considered in the computing element. The results show that the surface roughness and density of metal parts induced by heat transfer and melt pool behavior are closely related to process parameters such as laser power, layer thickness, scanning speed, etc. When the volumetric energy density is low, the insufficient fusion of metal particles leads to pore defects. When the line energy density is high, the melt track is smooth with low porosity, resulting in the high density of the products. Additionally, the partial melting of powder particles at the beginning and end of the melting track usually contributes to pore formation. These findings provide valuable insights for improving the quality and reliability of metal additive manufacturing. Full article
(This article belongs to the Special Issue Latest Developments in Advanced Machining Technologies for Materials)
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