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Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic...
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Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 18257

Special Issue Editors

Dr. Honggang Chen

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
Interests: image/video restoration; image/video understanding; image/video coding
Special Issues, Collections and Topics in MDPI journals
Dr. Yuan Li

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: electrical engineering; high-voltage and insulation technology; power transmission and distribution; energy storage
Dr. Junyu Guo

E-Mail Website
Guest Editor
School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
Interests: reliability assessments; condition monitoring; fault diagnosis; residual life prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) technologies into the fields of energy, manufacturing, and automatic control processes is transforming these industries by enhancing efficiency, accuracy, and innovation. As the volume and complexity of data grow, AI's role in these sectors becomes increasingly vital.

This Special Issue focuses on showcasing cutting-edge research where AI technologies are applied to optimize energy systems, revolutionize manufacturing processes, and refine automatic control mechanisms. Contributions to this Special Issue will highlight how AI not only improves operational efficiencies but also drives the evolution of these crucial sectors toward a more innovative and sustainable future.

The topics covered may include, but are not limited to, the following:

  • AI for condition monitoring;
  • AI for fault diagnosis;
  • AI for decision support;
  • AI for risk prediction;
  • AI for process management.

Dr. Honggang Chen
Dr. Yuan Li
Dr. Junyu Guo
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

  • artificial intelligence
  • deep learning
  • machine learning
  • energy
  • manufacturing
  • automatic control

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

Published Papers (18 papers)

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Research

19 pages, 3480 KiB  
Article
Theory-Driven Multi-Output Prognostics for Complex Systems Using Sparse Bayesian Learning
by Jing Yang, Gangjin Huang, Hao Liu, Yunhe Ke, Yuwei Lin and Chengfeng Yuan
Processes 2025, 13(4), 1232; https://doi.org/10.3390/pr13041232 - 18 Apr 2025
Viewed by 148
Abstract
Complex systems often face significant challenges in both efficiency and performance when making long-term degradation predictions. To address these issues, this paper proposes a predictive architecture based on multi-output sparse probabilistic model regression. An adaptive health index (HI) extraction method was also introduced, [...] Read more.
Complex systems often face significant challenges in both efficiency and performance when making long-term degradation predictions. To address these issues, this paper proposes a predictive architecture based on multi-output sparse probabilistic model regression. An adaptive health index (HI) extraction method was also introduced, which leverages unsupervised deep learning and variational mode decomposition to effectively extract health indicators from multiple measurements of complex systems. The effectiveness of the proposed method was validated using both the C-MAPSS and FLEA datasets. The case study results demonstrate that the proposed prognostic method delivered an outstanding performance. Specifically, the feature extraction method effectively reduced the measurement noise and produced robust HIs, while the multi-output sparse probabilistic model achieved lower prediction errors and a higher accuracy. Compared to traditional single-step forward-prediction methods, the proposed approach significantly reduced the time required for long-term predictions in complex systems, thus improving support for online status monitoring. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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18 pages, 2729 KiB  
Article
Machine Learning and Industrial Data for Veneer Quality Optimization in Plywood Manufacturing
by Mario Ramos-Maldonado, Felipe Gutiérrez, Rodrigo Gallardo-Venegas, Cecilia Bustos-Avila, Eduardo Contreras and Leandro Lagos
Processes 2025, 13(4), 1229; https://doi.org/10.3390/pr13041229 - 18 Apr 2025
Viewed by 366
Abstract
The plywood industry is one of the most significant sub-sectors of the forestry industry and serves as a cornerstone of sustainable construction within a bioeconomy framework. Plywood is a panel composed of multiple layers of wood sheets bonded together. While automation and process [...] Read more.
The plywood industry is one of the most significant sub-sectors of the forestry industry and serves as a cornerstone of sustainable construction within a bioeconomy framework. Plywood is a panel composed of multiple layers of wood sheets bonded together. While automation and process monitoring have played a crucial role in improving efficiency, data-driven decision-making remains underutilized in the industrial sector. Many industrial processes continue to rely heavily on the expertise of operators rather than on data analytics. However, advancements in data storage capabilities and the availability of high-speed computing have paved the way for data-driven algorithms that can support real-time decision-making. Due to the biological nature of wood and the numerous variables involved, managing manufacturing operations is inherently complex. The multitude of process variables, and the presence of non-linear physical phenomena make it challenging to develop accurate and robust analytical predictive models. As a result, data-driven approaches—particularly Artificial Intelligence (AI)—have emerged as highly promising modeling techniques. Leveraging industrial data and exploring the application of AI algorithms, particularly Machine Learning (ML), to predict key performance indicators (KPIs) in process plants represent a novel and expansive field of study. The processing of industrial data and the evaluation of AI algorithms best suited for plywood manufacturing remain key areas of research. This study explores the application of supervised Machine Learning (ML) algorithms in monitoring key process variables to enhance quality control in veneers and plywood production. The analysis included Random Forest, XGBoost, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Lasso, and Logistic Regression. An initial dataset comprising 49 variables related to the maceration, peeling, and drying processes was refined to 30 variables using correlation analysis and Lasso variable selection. The final dataset, encompassing 13,690 records, categorized into 9520 low-quality labels and 4170 high-quality labels. The evaluation of classification algorithms revealed significant performance differences; Random Forest reached the highest accuracy of 0.76, closely followed by XGBoost. K-Nearest Neighbors (KNN) demonstrated notable precision, while Support Vector Machine (SVM) exhibited high precision but low recall. Lasso and Logistic Regression showed comparatively lower performance metrics. These results highlight the importance of selecting algorithms tailored to the specific characteristics of the dataset to optimize model effectiveness. The study highlights the critical role of AI-driven insights in improving operational efficiency and product quality in veneer and plywood manufacturing, paving the way for enhanced industrial competitiveness. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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21 pages, 2879 KiB  
Article
Real-Time Classification of Chicken Parts in the Packaging Process Using Object Detection Models Based on Deep Learning
by Dilruba Şahin, Orhan Torkul, Merve Şişci, Deniz Demircioğlu Diren, Recep Yılmaz and Alpaslan Kibar
Processes 2025, 13(4), 1005; https://doi.org/10.3390/pr13041005 - 27 Mar 2025
Viewed by 342
Abstract
Chicken meat plays an important role in the healthy diets of many people and has a large global trade volume. In the chicken meat sector, in some production processes, traditional methods are used. Traditional chicken part sorting methods are often manual and time-consuming, [...] Read more.
Chicken meat plays an important role in the healthy diets of many people and has a large global trade volume. In the chicken meat sector, in some production processes, traditional methods are used. Traditional chicken part sorting methods are often manual and time-consuming, especially during the packaging process. This study aimed to identify and classify the chicken parts for their input during the packaging process with the highest possible accuracy and speed. For this purpose, deep-learning-based object detection models were used. An image dataset was developed for the classification models by collecting the image data of different chicken parts, such as legs, breasts, shanks, wings, and drumsticks. The models were trained by the You Only Look Once version 8 (YOLOv8) algorithm variants and the Real-Time Detection Transformer (RT-DETR) algorithm variants. Then, they were evaluated and compared based on precision, recall, F1-Score, mean average precision (mAP), and Mean Inference Time per frame (MITF) metrics. Based on the obtained results, the YOLOv8s model outperformed the other models developed with other YOLOv8 versions and the RT-DETR algorithm versions by obtaining values of 0.9969, 0.9950, and 0.9807 for the F1-score, mAP@0.5, and mAP@0.5:0.95, respectively. It has been proven suitable for real-time applications with an MITF value of 10.3 ms/image. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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14 pages, 4199 KiB  
Article
Lightweight Helmet-Wearing Detection Algorithm Based on StarNet-YOLOv10
by Hongli Wang, Qiangwen Zong, Yang Liao, Xiao Luo, Mingzhi Gong, Zhenyao Liang, Bin Gu and Yong Liao
Processes 2025, 13(4), 946; https://doi.org/10.3390/pr13040946 - 22 Mar 2025
Viewed by 339
Abstract
The safety helmet is the equipment that construction workers must wear, and it plays an important role in protecting their lives. However, there are still many construction workers who do not pay attention to the wearing of helmets. Therefore, the real-time high-precision intelligent [...] Read more.
The safety helmet is the equipment that construction workers must wear, and it plays an important role in protecting their lives. However, there are still many construction workers who do not pay attention to the wearing of helmets. Therefore, the real-time high-precision intelligent detection of construction workers’ helmet wearing is crucial. To this end, this paper proposes a lightweight helmet-wearing detection algorithm based on StarNet-YOLOv10. Firstly, the StarNet network structure is used to replace the backbone network part of the original YOLOv10 model while retaining the original Spatial Pyramid Pooling Fast (SPPF) and Partial Self-attention (PSA) parts. Secondly, the C2f module in the neck network is optimised by combining the PSA attention module and the GhostBottleneckv2 module, which improves the extraction of feature information and the expression ability of the model. Finally, optimisation is performed in the head network by introducing the Large Separable Kernel Attention (LSKA) attention mechanism to improve the detection accuracy and detection efficiency of the detection head. The experimental results show that compared with the existing Faster R-CNN, YOLOv5s, YOLOv6, and the original YOLOv10 models, the StarNet-YOLOv10 model proposed in this paper has a greater degree of improvement in the accuracy, recall, average precision mean, computational volume, and frame rate, in which the accuracy is as high as 83.36%, the recall rate can be up to 81.17%, and the average precision mean can reach 78.66%. Meanwhile, compared with the original YOLOv10 model, this model improves 1.7% in accuracy, 1.62% in recall, and 4.43% in mAP. Therefore, the present model can well meet the detection requirements of helmet wearing and can effectively reduce the safety hazards caused by not wearing helmets on construction sites. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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23 pages, 3562 KiB  
Article
A Transformer Oil Temperature Prediction Method Based on Data-Driven and Multi-Model Fusion
by Lin Yang, Liang Chen, Fan Zhang, Shen Ma, Yang Zhang and Sixu Yang
Processes 2025, 13(2), 302; https://doi.org/10.3390/pr13020302 - 22 Jan 2025
Cited by 2 | Viewed by 787
Abstract
A power transformer is an important part of the power system, and the oil temperature of the transformer is an important state parameter that reflects the operation state of the transformer. The accurate prediction of the oil temperature of the transformer can ensure [...] Read more.
A power transformer is an important part of the power system, and the oil temperature of the transformer is an important state parameter that reflects the operation state of the transformer. The accurate prediction of the oil temperature of the transformer can ensure the safe and stable operation of the transformer. Given the lack of a practical and effective data processing process and the problem that most of the current research is conducted on small-scale ideal datasets, this paper proposes a transformer oil temperature prediction method based on data-driven and multi-model fusion. The method first analyses and processes the actual transformer inspection data; it then uses the multi-model fusion method to model and predict the transformer oil temperature. The base model was trained using the machine learning method, and the secondary learning model was trained using the improved TSSA-BP neural network. The improved sparrow search algorithm (TSSA) was used to optimise the parameters of the BP neural network to improve the convergence accuracy and prediction performance of the model. The transformer data are classified according to cooling mode, operating voltage, and other indicators, and then eight groups of experimental datasets under different actual conditions are constructed for modelling and prediction. The experimental results show that the maximum root mean square error and the mean absolute percentage error of this method on different datasets are 1.0877 and 1.58%, and compared with other prediction methods, the prediction accuracy of this method is better than other methods, which verifies the practicability and feasibility of modelling and predicting for the actual transformer inspection data. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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17 pages, 4801 KiB  
Article
The Implementation of Simulation in Designing Production Expansion
by Marek Kliment, Jana Kronová, Miriam Pekarčíková, Peter Trebuňa and Michal Baluch
Processes 2025, 13(2), 299; https://doi.org/10.3390/pr13020299 - 22 Jan 2025
Viewed by 903
Abstract
This paper addresses the design of production expansion by using simulation to optimize production and storage capacities. The primary objective is to apply the Siemens Tecnomatix Plant Simulation software version 2404 to analyze the current production process, identify potential bottlenecks, and propose improvements. [...] Read more.
This paper addresses the design of production expansion by using simulation to optimize production and storage capacities. The primary objective is to apply the Siemens Tecnomatix Plant Simulation software version 2404 to analyze the current production process, identify potential bottlenecks, and propose improvements. The methodology involves the use of simulation to create digital models of the production process, allowing for the detection of issues and evaluation of alternative solutions without interfering with ongoing production. The novelty of this approach lies in its ability to test and optimize the production flow digitally, ensuring a smoother transition to expanded capacities. Several alternative scenarios for production expansion were developed using Siemens Tecnomatix Plant Simulation, with a focus on verifying production volumes, identifying bottlenecks, and proposing the necessary machines and technological equipment. The results of the simulations are used to assess the proposed improvements, while the layout of the process is optimized for efficient material flow. The paper also presents 3D models as part of the solution proposal. The findings demonstrate the effectiveness of simulation-based design for expanding production systems, offering a methodology that can be applied to similar manufacturing processes. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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17 pages, 3712 KiB  
Article
Prediction of the Effect of Fly Ash on the Unconfined Compressive Strength of Basalt Fiber Reinforced Clay Using Artificial Neural Networks
by Yasemin Aslan Topçuoğlu
Processes 2025, 13(1), 157; https://doi.org/10.3390/pr13010157 - 8 Jan 2025
Cited by 1 | Viewed by 816
Abstract
In this study, the effects of fly ash (FA) and basalt fiber (BF) additives on the unconfined compressive strength (qu) of kaolin clay were experimentally investigated, and a dataset was created based on the results. This dataset was used in an [...] Read more.
In this study, the effects of fly ash (FA) and basalt fiber (BF) additives on the unconfined compressive strength (qu) of kaolin clay were experimentally investigated, and a dataset was created based on the results. This dataset was used in an artificial neural network (ANN) model to predict the qu based on the additive ratio, water content, and curing time. For this purpose, samples were prepared by adding 1% BF with a length of 24 mm and FA at ratios of 3%, 6%, 9%, 12%, and 15% to the clay, followed by the addition of 25% and 30% water. Unconfined compressive tests were performed before curing and after 28, 42, and 56 days of curing to determine the qu values. The evaluation of the obtained experimental results was carried out by creating an ANN model. To validate the prediction capabilities of the ANN, a comparative analysis was performed using various artificial intelligence models, and the model’s overall performance was assessed with a 5-fold cross-validation technique. The evaluations revealed that the ANN model, using data from experimental studies, demonstrated the highest prediction accuracy and was in close agreement with the experimental results. According to the results obtained, the R value of the ANN model was calculated as 0.97, while the RMSE values were found as 0.09, 0.10, 0.06 and 0.04 for pre-curing, 28th day, 42nd day and 56th day, respectively. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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20 pages, 1132 KiB  
Article
Photodiode Signal Patterns: Unsupervised Learning for Laser Weld Defect Analysis
by Erkan Caner Ozkat
Processes 2025, 13(1), 121; https://doi.org/10.3390/pr13010121 - 5 Jan 2025
Viewed by 917
Abstract
Laser welding, widely used in industries such as automotive and aerospace, requires precise monitoring to ensure defect-free welds, especially when joining dissimilar metallic thin foils. This study investigates the application of machine learning techniques for defect detection in laser welding using photodiode signal [...] Read more.
Laser welding, widely used in industries such as automotive and aerospace, requires precise monitoring to ensure defect-free welds, especially when joining dissimilar metallic thin foils. This study investigates the application of machine learning techniques for defect detection in laser welding using photodiode signal patterns. Supervised models, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF), were employed to classify weld defects into sound welds (SW), lack of connection (LoC), and over-penetration (OP). SVM achieved the highest accuracy (95.2%) during training, while RF demonstrated superior generalization with 83% accuracy on validation data. The study also proposed an unsupervised learning method using a wavelet scattering one-dimensional convolutional autoencoder (1D-CAE) network for anomaly detection. The proposed network demonstrated its effectiveness in achieving accuracies of 93.3% and 87.5% on training and validation datasets, respectively. Furthermore, distinct signal patterns associated with SW, OP, and LoC were identified, highlighting the ability of photodiode signals to capture welding dynamics. These findings demonstrate the effectiveness of combining supervised and unsupervised methods for laser weld defect detection, paving the way for robust, real-time quality monitoring systems in manufacturing. The results indicated that unsupervised learning could offer significant advantages in identifying anomalies and reducing manufacturing costs. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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33 pages, 5054 KiB  
Article
Estimated Tardiness-Based Reinforcement Learning Solution to Repeatable Job-Shop Scheduling Problems
by Chi Yeong Heo, Jun Seo, Yonggang Kim, Yohan Kim and Taewoon Kim
Processes 2025, 13(1), 62; https://doi.org/10.3390/pr13010062 - 31 Dec 2024
Viewed by 818
Abstract
Intelligent manufacturing promises to revolutionize production processes, and it is expected to enhance efficiency and productivity. In this paper, we study the job-shop scheduling problem, which is a key enabler to the realization of intelligent manufacturing. Compared to previous studies, the proposed solution [...] Read more.
Intelligent manufacturing promises to revolutionize production processes, and it is expected to enhance efficiency and productivity. In this paper, we study the job-shop scheduling problem, which is a key enabler to the realization of intelligent manufacturing. Compared to previous studies, the proposed solution tackles generalized configurations by allowing the repetition of jobs and by assuming the limited capacity of machines and a sequence of operations constituting a job. The proposed solution is fully controlled by the trained reinforcement learning agent that finds the optimal match between the job and machine to schedule. In addition, by introducing the expected tardiness (ETD) metric, the agent can enhance the scheduling performance while effectively handling dynamic action space changes with the action masking technique. To effectively adapt to the particular manufacturing site, we propose a novel training approach that utilizes the average order arrival distribution learned from the historical logs. Such data-driven optimization can train an agent that effectively captures the general and site-specific characteristics of job arrivals, leading to improved generalization performance and a finely tuned model, respectively. To validate the proposed approach, we implement a custom environment with which extensive performance evaluation and comparison are carried out. The evaluation results show that the proposed approach can outperform the conventional heuristic priority dispatching rules under the desired performance criteria such as total tardiness and the total manufacturing cost. To be specific, in terms of the total cost metric, the proposed approach outperforms the considered approaches by 31.69% on average. In addition, the use of ETD can enhance the performance of the conventional approaches by 27.74% on average. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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18 pages, 6838 KiB  
Article
A Parallel Prognostic Method Integrating Uncertainty Quantification for Probabilistic Remaining Useful Life Prediction of Aero-Engine
by Rongqiu Wang, Ya Zhang, Chen Hu, Zhengquan Yang, Huchang Li, Fuqi Liu, Linling Li and Junyu Guo
Processes 2024, 12(12), 2925; https://doi.org/10.3390/pr12122925 - 20 Dec 2024
Viewed by 771
Abstract
Remaining useful life (RUL) prediction plays a fundamental role in the prognostics and health management of mechanical equipment. Consequently, extensive research has been devoted to estimating the RUL of mechanical equipment. Owing to the development of modern advanced sensor technologies, a significant amount [...] Read more.
Remaining useful life (RUL) prediction plays a fundamental role in the prognostics and health management of mechanical equipment. Consequently, extensive research has been devoted to estimating the RUL of mechanical equipment. Owing to the development of modern advanced sensor technologies, a significant amount of monitoring data is recorded. Traditional methods, such as machine-learning-based methods and statistical-data-driven methods, are ineffective in matching when faced with big data thus leading to poor predictions. As a result, deep-learning-based methods are extensively utilized due to their efficient capability to excavate deep features and realize accurate predictions. However, most deep-learning-based methods only provide point estimations and ignore the prediction uncertainty. To address this limitation, this paper proposes a parallel prognostic network to sufficiently excavate the degradation features from multiple dimensions for more accurate RUL prediction. In addition, accurate calculation of model evidence is extremely difficult when dealing with big data so the Monte Carlo dropout is employed to infer the model weights under low computational cost and high scalability to obtain a probabilistic RUL prediction. Finally, the C-MAPSS aero-engine dataset is employed to validate the proposed dual-channel framework. The experimental results illustrate its superior prediction performance compared to other deep learning methods and the ability to quantify prediction uncertainty. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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17 pages, 3789 KiB  
Article
Automated Dual-Side Leather Defect Detection and Classification Using YOLOv11: A Case Study in the Finished Leather Industry
by Nikola Banduka, Katarina Tomić, Jovan Živadinović and Marko Mladineo
Processes 2024, 12(12), 2892; https://doi.org/10.3390/pr12122892 - 17 Dec 2024
Cited by 2 | Viewed by 2054
Abstract
This study explores the optimization of leather defect detection through the advanced YOLOv11 model, addressing long-standing challenges in quality control within the leather industry. Traditional inspection methods, reliant on human accuracy ranging between 70% and 85%, have limited leather utilization rates and contributed [...] Read more.
This study explores the optimization of leather defect detection through the advanced YOLOv11 model, addressing long-standing challenges in quality control within the leather industry. Traditional inspection methods, reliant on human accuracy ranging between 70% and 85%, have limited leather utilization rates and contributed to substantial material waste. To overcome these limitations, we developed an automated solution leveraging controlled environmental conditions within a custom-designed light chamber. This research specifically targets common defects in leather, such as insect larvae damage and removal cuts, by analyzing both the grain and flesh sides of the material. The results reveal a notable improvement in detection accuracy on the flesh side, achieving 93.5% for grubs and 91.8% for suckout, compared to 85.8% and 87.1% on the grain side. Classification accuracy further demonstrates the advantage of dual-side analysis, with the flesh side reaching 98.2% for grubs and 97.6% for suckout, significantly outperforming the grain side. The dual-side methodology, combined with YOLOv11’s enhanced capabilities, enables the precise identification of subtle defects and offers a transformative approach to leather defect detection. By integrating cutting-edge AI models with standardized digitization environments, this research presents a scalable, highly efficient solution that reduces human error, optimizes leather utilization, and supports industrial sustainability. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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21 pages, 8333 KiB  
Article
Urban-Scale Acoustic Comfort Map: Fusion of Social Inputs, Noise Levels, and Citizen Comfort in Open GIS
by Farzaneh Zarei, Mazdak Nik-Bakht, Joonhee Lee and Farideh Zarei
Processes 2024, 12(12), 2864; https://doi.org/10.3390/pr12122864 - 13 Dec 2024
Viewed by 1003
Abstract
With advancements in the Internet of Things (IoT), diverse and high-resolution data sources, such as environmental sensors and user-generated inputs from mobile devices, have become available to model and estimate citizens’ acoustic comfort in urban environments. These IoT-enabled data sources offer scalable insights [...] Read more.
With advancements in the Internet of Things (IoT), diverse and high-resolution data sources, such as environmental sensors and user-generated inputs from mobile devices, have become available to model and estimate citizens’ acoustic comfort in urban environments. These IoT-enabled data sources offer scalable insights in real time into both objective parameters (e.g., noise levels and environmental conditions) and subjective perceptions (e.g., personal comfort and soundscape experiences), which were previously challenging to capture comprehensively by using traditional methods. Despite this, there remains a lack of a clear framework explicitly presenting the role of these diverse inputs in determining acoustic comfort. This paper contributes by (1) exploring the relationship between attributes governing the physical aspect of the built environment (sensory data) and the end-users’ characteristics/inputs/sensations (such as their acoustic comfort level) and how these attributes can correlate/connect; (2) developing a CityGML-based framework that leverages semantic 3D city models to integrate and represent both objective sensory data and subjective social inputs, enhancing data-driven decision making at the city level; and (3) introducing a novel approach to crowdsourcing citizen inputs to assess perceived acoustic comfort indicators, which inform predictive modeling efforts. Our solution is based on CityGML’s capacity to store and explain 3D city-related shapes with their semantic characteristics, which are essential for city-level operations such as spatial data mining and thematic queries. To do so, a crowdsourcing method was used, and 20 perceptive indicators were identified from the existing literature to evaluate people’s perceived acoustic attributes and types of sound sources and their relations to the perceived soundscape comfort. Three regression models—K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and XGBoost—were trained on the collected data to predict acoustic comfort at bus stops in Montréal based on physical and psychological attributes of travellers. In the best-performing scenario, which incorporated psychological attributes and measured noise levels, the models achieved a normalized mean squared error (NMSE) as low as 0.0181, a mean absolute error (MAE) of 0.0890, and a root mean square error (RMSE) of 0.1349. These findings highlight the effectiveness of integrating subjective and objective data sources to accurately predict acoustic comfort in urban environments. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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19 pages, 3906 KiB  
Article
Adaptive Enhancement of Thermal Infrared Images for High-Voltage Cable Buffer Layer Ablation
by Hao Zhan, Jing Zhang, Yuhao Lan, Fan Zhang, Qinqing Huang, Kai Zhou and Chengde Wan
Processes 2024, 12(11), 2543; https://doi.org/10.3390/pr12112543 - 14 Nov 2024
Cited by 1 | Viewed by 962
Abstract
In recent years, ablation of the buffer layer in high-voltage cables has become a prevalent issue compromising the reliability of power transmission systems. Given the internal location of these faults, direct monitoring and assessment are challenging, resulting in numerous undetected ablation hazards. Previous [...] Read more.
In recent years, ablation of the buffer layer in high-voltage cables has become a prevalent issue compromising the reliability of power transmission systems. Given the internal location of these faults, direct monitoring and assessment are challenging, resulting in numerous undetected ablation hazards. Previous practice has demonstrated that detecting buffer layer ablation through surface temperature distribution changes is feasible, offering a convenient, efficient, and non-destructive approach. However, the variability in heat generation and the subtle temperature differences in thermal infrared images, compounded by noise interference, can impair the accuracy and timeliness of fault detection. To overcome these challenges, this paper introduces an adaptive enhancement method for the thermal infrared imaging of high-voltage cable buffer layer ablation. The method involves an Average Gradient Weighted Guided Filtering (AGWGF) technique to decompose the image into background and detail layers, preventing noise amplification during enhancement. The background layer, containing the primary information, is enhanced using an improved Contrast Limited Adaptive Histogram Equalization (CLAHE) to accentuate temperature differences. The detail layer, rich in high-frequency content, undergoes improved Adaptive Bilateral Filtering (ABF) for noise reduction. The enhanced background and detail layers are then fused and stretched to produce the final enhanced thermal image. To vividly depict temperature variations in the buffer layer, pseudo-color processing is applied to generate color-infrared thermal images. The results indicate that the proposed method’s enhanced images and pseudo-colored infrared thermal images provide a clearer and more intuitive representation of temperature differences compared to the original images, with an average increase of 2.17 in information entropy and 8.38 in average gradient. This enhancement facilitates the detection and assessment of buffer layer ablation faults, enabling the prompt identification of faults. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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16 pages, 12268 KiB  
Article
Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy
by Zhikun Huang, Jingchao Yan, Jianlong Zhang, Chong Han, Jingfei Peng, Ju Cheng, Zhenggang Wang, Min Luo and Pengbo Yin
Processes 2024, 12(10), 2214; https://doi.org/10.3390/pr12102214 - 11 Oct 2024
Cited by 4 | Viewed by 1344
Abstract
As industrial development drives the increasing demand for steel, accurate estimation of the material’s fatigue strength has become crucial. Fatigue strength, a critical mechanical property of steel, is a primary factor in component failure within engineering applications. Traditional fatigue testing is both costly [...] Read more.
As industrial development drives the increasing demand for steel, accurate estimation of the material’s fatigue strength has become crucial. Fatigue strength, a critical mechanical property of steel, is a primary factor in component failure within engineering applications. Traditional fatigue testing is both costly and time-consuming, and fatigue failure can lead to severe consequences. Therefore, the need to develop faster and more efficient methods for predicting fatigue strength is evident. In this paper, a fatigue strength dataset was established, incorporating data on material element composition, physical properties, and mechanical performance parameters that influence fatigue strength. A machine learning regression model was then applied to facilitate rapid and efficient fatigue strength prediction of ferrous alloys. Twenty characteristic parameters, selected for their practical relevance in engineering applications, were used as input variables, with fatigue strength as the output. Multiple algorithms were trained on the dataset, and a deep learning regression model was employed for the prediction of fatigue strength. The performance of the models was evaluated using metrics such as MAE, RMSE, R2, and MAPE. The results demonstrated the superiority of the proposed models and the effectiveness of the applied methodologies. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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23 pages, 21133 KiB  
Article
Data-Driven Feature Extraction-Transformer: A Hybrid Fault Diagnosis Scheme Utilizing Acoustic Emission Signals
by Chenggong Ma, Jiuyang Gao, Zhenggang Wang, Ming Liu, Jing Zou, Zhipeng Zhao, Jingchao Yan and Junyu Guo
Processes 2024, 12(10), 2094; https://doi.org/10.3390/pr12102094 - 26 Sep 2024
Cited by 2 | Viewed by 1406
Abstract
This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two [...] Read more.
This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two techniques: the Wavelet Kernel Network (WKN) for noise reduction and the Convolutional Block Attention Module (CBAM) for feature enhancement. The wavelet function in the WKN reduces noise, while the attention mechanism in the CBAM enhances features. The Transformer module then processes the feature vectors and sends the results to the softmax layer for classification. To validate the proposed method’s efficacy, experiments were conducted using acoustic emission datasets from NASA Ames Research Center and the University of California, Berkeley. The results were compared using the four key metrics obtained through confusion matrix analysis. Experimental results show that the proposed method performs excellently in fault diagnosis using acoustic emission signals, achieving a high average accuracy of 99.84% and outperforming several baseline models, such as CNN, CNN-LSTM, CNN-GRU, VGG19, and ZFNet. The best-performing model, VGG19, only achieved an accuracy of 88.61%. Additionally, the findings suggest that integrating noise reduction and feature enhancement in a single framework significantly improves the network’s classification accuracy and robustness when analyzing acoustic emission signals. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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18 pages, 3667 KiB  
Article
An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
by Hubin Du, Qiuyu Li, Ziqian Guan, Hengyuan Zhang and Yongtao Liu
Processes 2024, 12(9), 1978; https://doi.org/10.3390/pr12091978 - 13 Sep 2024
Cited by 1 | Viewed by 1414
Abstract
The efficacy of early fire detection hinges on its swift response and precision, which allows for the issuance of timely alerts in the nascent stages of a fire, thereby minimizing losses and injuries. To enhance the precision and swiftness of identifying minute early [...] Read more.
The efficacy of early fire detection hinges on its swift response and precision, which allows for the issuance of timely alerts in the nascent stages of a fire, thereby minimizing losses and injuries. To enhance the precision and swiftness of identifying minute early flame targets, as well as the ease of deployment at the edge end, an optimized early flame target detection algorithm for YOLOv8 is proposed. The original feature fusion module, an FPN (feature pyramid network) of YOLOv8n, has been enhanced to become the BiFPN (bidirectional feature pyramid network) module. This modification enables the network to more efficiently and rapidly perform multi-scale fusion, thereby enhancing its capacity for integrating features across different scales. Secondly, the efficient multi-scale attention (EMA) mechanism is introduced to ensure the effective retention of information on each channel and reduce the computational overhead, thereby improving the model’s detection accuracy while reducing the number of model parameters. Subsequently, the NWD (normalized Wasserstein distance) loss function is employed as the bounding box loss function, which enhances the model’s regression performance and robustness. The experimental results demonstrate that the size of the enhanced model is 4.8 M, a reduction of 22.5% compared to the original YOLOv8n. Additionally, the mAP0.5 metric exhibits a 2.7% improvement over the original YOLOv8n, indicating a more robust detection capability and a more compact model size. This makes it an ideal candidate for deployment in edge devices. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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18 pages, 1740 KiB  
Article
Deep Integration and Innovation Development in the Logistics and Manufacturing Industries and Their Performances: A Case Study of Anhui Province, China
by Heping Ding, Yuchang Gao, Fagang Hu, Yuxia Guo and Conghu Liu
Processes 2024, 12(9), 1867; https://doi.org/10.3390/pr12091867 - 31 Aug 2024
Cited by 1 | Viewed by 1404
Abstract
The deep integration and innovative development of the logistics and manufacturing industries (LMDIIs) are crucial for reducing costs, increasing efficiency, and advancing manufacturing. To assess the development level and performance of the LMDIIs, we construct an evaluation index system, calculate the weights using [...] Read more.
The deep integration and innovative development of the logistics and manufacturing industries (LMDIIs) are crucial for reducing costs, increasing efficiency, and advancing manufacturing. To assess the development level and performance of the LMDIIs, we construct an evaluation index system, calculate the weights using the CRITIC method, and measure the comprehensive level of the LMDIIs using the TOPSIS method. We evaluate the coupling coordination of the LMDIIs and conduct a ridge regression analysis of their performance, using Anhui Province, China, as a case study. The results show that the comprehensive level of the LMDIIs in Anhui Province is low. The highest values for the logistics and manufacturing industries from 2013 to 2022 indicate great development potential. The coupling level is fluctuating upwards, and the coupling degree is growing slowly. The performance impact coefficients of the LMDIIs on the digital intelligence development of the manufacturing industry and the profit levels of the two industries indicate a significant promoting effect. However, the performance coefficient for the low-carbon transformation of the logistics industry is negative, indicating a restraining effect. Hence, we propose countermeasures and suggestions to further promote the LMDIIs and provide theoretical and methodological support for their research and management. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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14 pages, 3261 KiB  
Article
A Lightweight Safety Helmet Detection Algorithm Based on Receptive Field Enhancement
by Changpeng Ji, Zhibo Hou and Wei Dai
Processes 2024, 12(6), 1136; https://doi.org/10.3390/pr12061136 - 31 May 2024
Cited by 1 | Viewed by 1071
Abstract
Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet [...] Read more.
Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet detection algorithm called YOLOv5s-CR. First, we use a lightweight backbone, a high-resolution feature fusion network, and a small object detection layer to improve the detection accuracy of small objects while substantially decreasing the model parameters. Next, we embed a coordinate attention mechanism into the feature extraction network to improve the localization accuracy of the detected object. Finally, we propose a new receptive field enhancement module (RFEM) to substitute the SPPF module in the original network, enabling the model to acquire features under multiple receptive fields, thereby enhancing the detection precision of multi-scale objects. Using the Safety Helmet Detection dataset for validation, in contrast to the initial YOLOv5s, the parameters of the improved algorithm were reduced by 62.8% to 2.61 M, and P, R, and mAP0.5 were increased by 1.5%, 1.2%, and 2.0%, respectively. The detection speed can reach 149FPS on the RTX3070 GPU, which satisfies the accuracy and real-time requirements for detecting safety helmets. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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