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Machines
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Artificial Intelligence in Mechanical Engineering Applications
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Artificial Intelligence in Mechanical Engineering Applications

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4068

Special Issue Editors

Prof. Dr. Panagiotis Kyratsis

E-Mail Website
Guest Editor
Department of Product and Systems Design Engineering, University of Western Macedonia, 50100 Kila Kozani, Greece
Interests: manufacturing/machining; CAD/CAM/CAE systems; computational design; artificial intelligence (AI); additive manufacturing (AM); reverse engineering and prototyping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Cheng

E-Mail Website
Guest Editor
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, London UB8 3PH, UK
Interests: design of high precision machines; ultraprecision machining systems; micro-cutting mechanics and physics; multiscale multiphysics modelling and analysis; smart cutting tools and smart machining; physical AI for manufacturing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue entitled “Artificial Intelligence in Mechanical Engineering Applications” in Machines. The aim of this Special Issue is to bring together high-quality contributions in the areas of mechanical design and manufacturing that employ artificial intelligence tools and algorithms. All accepted papers will undergo rigorous peer review and, upon acceptance, will be published with full open access.

Potential topics include, but are not limited to, the following:

  • AI-driven design optimization in mechanical engineering design;
  • Artificial intelligence in smart manufacturing and Industry 4.0;
  • AI-based structural health monitoring and predictive maintenance;
  • Generative AI in conceptual design and prototyping;
  • CAD/CAM/CAE and FEA (finite element analysis);
  • Digital twins in mechanical engineering and cyberphysical systems;
  • AI in additive manufacturing and 3D printing optimization;
  • Computer vision for quality inspection and defect detection in mechanical products;
  • Intelligent scheduling/process planning in mechanical production systems and reinforcement learning for autonomous mechanical systems;
  • Data-driven optimization of energy systems in mechanical engineering and smart building mechanical systems;
  • AI-assisted material science and smart alloys development;
  • Intelligent control systems for robotics and mechatronics;
  • AI for multi-scale modeling of mechanical materials and structures;
  • AI-enhanced human–robot collaboration in mechanical tasks;
  • AI for Lightweight structural design and topology optimization;
  • AI-powered human–machine interaction in mechanical systems;
  • Humanoid robotic manufacturing systems and physical AI.

Prof. Dr. Panagiotis Kyratsis
Prof. Dr. Kai Cheng
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. Machines 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

  • artificial intelligence
  • AI-assisted manufacturing
  • human–machine interaction
  • multi-scale modeling
  • data-driven optimization
  • intelligent scheduling/process planning
  • generative AI
  • AI-driven applications
  • AI in additive manufacturing
  • physical AI for mechanical engineering
  • humanoid robotic manufacturing systems

Benefits of Publishing in a Special Issue

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

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Research

31 pages, 9451 KB  
Article
Quantitative Microstructure Characterization in Additively Manufactured Nickel Alloy 625 Using Image Segmentation and Deep Learning
by Tuğrul Özel, Sijie Ding, Amit Ramasubramanian, Franco Pieri and Doruk Eskicorapci
Machines 2026, 14(4), 366; https://doi.org/10.3390/machines14040366 - 26 Mar 2026
Viewed by 428
Abstract
Laser Powder Bed Fusion for metals (PBF-LB/M) is a complex additive manufacturing process in which metal powder is selectively melted layer-by-layer to fabricate 3D parts. Process parameters critically influence the resulting microstructure in nickel alloys, with features such as melt pool marks, grain [...] Read more.
Laser Powder Bed Fusion for metals (PBF-LB/M) is a complex additive manufacturing process in which metal powder is selectively melted layer-by-layer to fabricate 3D parts. Process parameters critically influence the resulting microstructure in nickel alloys, with features such as melt pool marks, grain size and orientation, porosity, and cracks serving as key process signatures. These features are typically analyzed post-process to identify suboptimal conditions. This research aims to develop automated post-process measurement and analysis techniques using image processing, pattern recognition, and statistical learning to correlate process parameters with part quality. Optical microscopy images of build surfaces are analyzed using machine learning algorithms to evaluate porosity, grain size, and relative density in fabricated test coupons. Effect plots are generated to identify trends related to increasing energy density. A novel deep learning approach based on Mask R-CNN is used to detect and segment melt pool regions in optical microscopy images. From the segmented regions, melt pool dimensions—such as width, depth, and area—are extracted using bounding geometry coordinates. Manually labeled images (Type I and Type II) are used to train the model. A comparison between ResNet-50 and ResNet-101 backbones shows that the ResNet-50-based model (Model 2) achieves superior performance, with lower training loss (0.1781 vs. 0.1907) and validation loss (8.6140 vs. 9.4228). Quantitative evaluation using the Jaccard index, precision, and recall metrics shows that the ResNet-101 backbone outperforms ResNet-50, achieving about 4% higher mean Intersection-over-Union, with values of 0.85 for Type I and 0.82 for Type II melt pools, where Type I is detected more accurately due to its more regular morphology and clearer boundaries. By extending Faster R-CNNs with a mask prediction branch, the method allows for precise melt pool measurements, providing valuable insights into process quality and dimensional accuracy, and aiding in the detection of defects in PBF-LB-fabricated parts. Full article
(This article belongs to the Special Issue Artificial Intelligence in Mechanical Engineering Applications)
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32 pages, 5650 KB  
Article
Low-Carbon and Energy-Efficient Dynamic Flexible Job Shop Scheduling Method Towards Renewable Energy Driven Manufacturing
by Yao Lu, Qicai Zhu, Changhao Tian, Erbao He and Taihua Zhang
Machines 2026, 14(1), 88; https://doi.org/10.3390/machines14010088 - 10 Jan 2026
Cited by 1 | Viewed by 699
Abstract
As one of the major sources of global carbon emissions, the manufacturing industry urgently requires green transformation. The utilization of renewable energy in production workshop offers a promising route toward zero-carbon manufacturing. However, renewable energy fluctuations and dynamic workshop events make efficient scheduling [...] Read more.
As one of the major sources of global carbon emissions, the manufacturing industry urgently requires green transformation. The utilization of renewable energy in production workshop offers a promising route toward zero-carbon manufacturing. However, renewable energy fluctuations and dynamic workshop events make efficient scheduling increasingly challenging. This paper introduces a low-carbon and energy-efficient dynamic flexible job shop scheduling problem oriented towards renewable energy integration, and develops a multi-agent deep reinforcement learning framework for dynamic and intelligent production scheduling. Inspired by the Proximal Policy Optimization (PPO) algorithm, a routing agent and a sequencing agent are designed for machine assignment and job sequencing, respectively. Customized state representations and reward functions are also designed to enhance learning performance and scheduling efficiency. Simulation results demonstrate that the proposed method achieves superior performance in multi-objective optimization, effectively balancing production efficiency, energy consumption, and carbon emission reduction across various job shop scheduling scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence in Mechanical Engineering Applications)
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23 pages, 3997 KB  
Article
Cutting Tool Remaining Useful Life Prediction Using Multi-Sensor Data Fusion Through Graph Neural Networks and Transformers
by Xin Chen and Kai Cheng
Machines 2025, 13(11), 1027; https://doi.org/10.3390/machines13111027 - 6 Nov 2025
Cited by 3 | Viewed by 1493
Abstract
In the context of Industry 4.0 and smart manufacturing, predicting cutting tool remaining useful life (RUL) is crucial for enabling and enhancing the reliability and efficiency of CNC machining. This paper presents an innovative predictive model based on the data fusion architecture of [...] Read more.
In the context of Industry 4.0 and smart manufacturing, predicting cutting tool remaining useful life (RUL) is crucial for enabling and enhancing the reliability and efficiency of CNC machining. This paper presents an innovative predictive model based on the data fusion architecture of Graph Neural Networks (GNNs) and Transformers to address the complexity of shallow multimodal data fusion, insufficient relational modeling, and single-task limitations simultaneously. The model harnesses time-series data, geometric information, operational parameters, and phase contexts through dedicated encoders, employs graph attention networks (GATs) to infer complex structural dependencies, and utilizes a cross-modal Transformer decoder to generate fused features. A dual-head output enables collaborative RUL regression and health state classification of cutting tools. Experiments are conducted on a multimodal dataset of 824 entries derived from multi-sensor data, constructing a systematic framework centered on tool flank wear width (VB), which includes correlation analysis, trend modeling, and risk assessment. Results demonstrate that the proposed model outperforms baseline models, with MSE reduced by 26–41%, MAE by 33–43%, R2 improved by 6–12%, accuracy by 6–12%, and F1-Score by 7–14%. Full article
(This article belongs to the Special Issue Artificial Intelligence in Mechanical Engineering Applications)
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