Advanced Artificial Intelligence Solutions for Modern Engineering Applications

Topic Information

Dear Colleagues,

Artificial intelligence plays an increasingly important role in modern engineering by enabling automated analysis, intelligent monitoring, and data-driven decision-making across complex systems. This Topic aims to highlight recent advances in artificial intelligence techniques and their effective application to contemporary engineering problems, with an emphasis on practical relevance and real-world impact.

We invite original research and review papers that present innovative AI models, algorithms, and frameworks, as well as studies demonstrating their use in monitoring, inspection, diagnosis, prediction, and optimization tasks. Application areas may include, but are not limited to, infrastructure and structural monitoring, industrial systems, transportation, energy and environmental monitoring, smart cities, manufacturing, robotics, and autonomous systems.

Contributions that address challenges such as data efficiency, model robustness, interpretability, scalability, and deployment in real operational environments are particularly encouraged. By bringing together diverse perspectives and application domains, this Topic seeks to provide a focused yet broad overview of how advanced artificial intelligence can enhance the performance, reliability, and sustainability of modern engineering applications.

Dr. Qasim Zaheer
Prof. Dr. Shi Qiu
Prof. Dr. Mohammad AlHamaydeh
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Buildings buildings	3.4	5.6	2011	14.7 Days	CHF 2600	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
ISPRS International Journal of Geo-Information ijgi	3.2	6.7	2012	34.9 Days	CHF 1900	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Smart Cities smartcities	6.6	13.0	2018	25.1 Days	CHF 2000	Submit

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Published Papers (12 papers)

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45 pages, 4877 KB  

Open AccessArticle

Data-Efficient Degradation Progression Modeling in Industrial Compressors via Baseline-Referenced Deep Feature Learning and Unsupervised Clustering

by Gonca Öcalan and İbrahim Türkoğlu

Appl. Sci. 2026, 16(14), 6895; https://doi.org/10.3390/app16146895 - 9 Jul 2026

Abstract

Accurate modeling of degradation progression in rotating machinery remains challenging in real industrial systems, where data are inherently limited and imbalanced because of safety-critical operations and associated risks, and the cost of acquiring fault data is high. These conditions make it difficult for [...] Read more.

Accurate modeling of degradation progression in rotating machinery remains challenging in real industrial systems, where data are inherently limited and imbalanced because of safety-critical operations and associated risks, and the cost of acquiring fault data is high. These conditions make it difficult for data-driven approaches to reliably capture the evolution of degradation over time. To address this challenge, this study proposes a hybrid framework that models degradation progression as a set of distinct behavioral regimes driven by loss of lubrication. The proposed framework first applies adaptive scaling guided by an $\alpha$ parameter derived from Root Mean Square (RMS) deviation of the vibration signals relative to the baseline condition, aiming to mitigate data leakage during preprocessing while improving robustness to data imbalance. It then performs baseline-referenced deep feature learning using a lightweight Long Short-Term Memory (LSTM) model trained only on baseline data. The trained model is subsequently used to encode the entire dataset into latent representations, which are finally clustered using Mini-Batch K-Means to organize distinct degradation-related behavioral regimes. Results on both real-world and experimental datasets demonstrate that the learned latent representations strongly agree with the degradation regimes associated with baseline characterization and $\alpha$-guided progression patterns, achieving an Adjusted Rand Index (ARI) of 1.0 across both datasets with respect to the internally defined reference stages. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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24 pages, 18037 KB  

Open AccessArticle

Damage Classification in Historical Buildings Through Transfer Learning Approaches

by Nuray Beyza Avcı and Betül Bektaş Ekici

Buildings 2026, 16(13), 2689; https://doi.org/10.3390/buildings16132689 - 7 Jul 2026

Viewed by 203

Abstract

Historical buildings are important cultural assets that reflect the identity of cities and preserve the collective memory of societies. However, these structures are increasingly exposed to environmental degradation and human-induced impacts, making their systematic documentation and condition assessment essential for effective conservation strategies. [...] Read more.

Historical buildings are important cultural assets that reflect the identity of cities and preserve the collective memory of societies. However, these structures are increasingly exposed to environmental degradation and human-induced impacts, making their systematic documentation and condition assessment essential for effective conservation strategies. Recent advances in artificial intelligence have provided powerful tools for image-based analysis in the field of heritage preservation. In particular, transfer learning enables the adaptation of pre-trained deep learning models to domain-specific tasks with limited labeled data. In this study, a deep transfer learning-based framework is proposed for automatic damage detection and classification in historical buildings. A new near-balanced dataset of 20,000 images spanning six deterioration categories was developed and made publicly available. Ten convolutional neural network and transformer architectures pre-trained on ImageNet were systematically compared under a unified Bayesian optimization protocol. Experimental results on a held-out test set show that EfficientNetB3 achieves the highest classification accuracy (97.65%), while AlexNet obtains the lowest performance (83.89%); the validation set was used exclusively for hyperparameter tuning. The results demonstrate that transfer learning-based models can effectively identify visually observable deterioration patterns and provide reliable support for automated documentation processes. The proposed framework contributes to the development of data-driven decision-support tools for digital documentation and condition assessment in heritage conservation. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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30 pages, 3085 KB  

Open AccessArticle

Customer Baseline Credibility in Constrained Reinforcement Learning for Incentive-Based Demand Response

by Jiyong Li and Kaiyue Wang

Sensors 2026, 26(13), 3986; https://doi.org/10.3390/s26133986 - 23 Jun 2026

Viewed by 275

Abstract

Incentive-based demand response is an important flexibility resource for power systems with high-renewable energy penetration. However, practical incentive allocation depends not only on flexible capacity and user response uncertainty, but also on the credibility of customer baseline load (CBL), which directly affects response [...] Read more.

Incentive-based demand response is an important flexibility resource for power systems with high-renewable energy penetration. However, practical incentive allocation depends not only on flexible capacity and user response uncertainty, but also on the credibility of customer baseline load (CBL), which directly affects response measurement, verification, and incentive settlement. To address this issue, this paper proposes a constrained reinforcement learning method with customer baseline credibility for dynamic resource allocation in incentive-based demand response. Based on user-side load measurements and demand response event records, the proposed framework evaluates user resources using flexible capacity, response reliability, response cost, and CBL credibility. The CBL credibility score reflects the measurement quality of the delivered response and is used as a pre-event allocation factor. Users are then grouped into different resource levels, and a group-level reinforcement learning agent dynamically determines incentive multipliers and response task allocation ratios. To improve feasibility, an action correction module revises raw policy outputs under budget, price, response capacity, and CBL risk constraints before implementation. Case studies are conducted using public industrial demand response measurements and open electricity-system time-series data. The results show that the proposed CBL-CRL method reduces the normalized total operating cost to 0.897, reduces the response tracking error to 0.108, and lowers CBL risk exposure to 0.087 under the normal scenario. Relative to the No-DR reference, CBL-CRL reduces the normalized total operating cost by 10.3 percent. Compared with MAPPO, the strongest learning-based baseline, CBL-CRL reduces the response tracking error by 10.7 percent and the CBL risk exposure by 40.8 percent, while maintaining the same renewable accommodation rate of 0.970. Compared with rule-based and learning-based baselines, CBL-CRL achieves a better balance between operational performance, incentive efficiency, action feasibility, and baseline-related settlement reliability. The results demonstrate that CBL credibility should not only be used for post-event settlement, but can also serve as an effective pre-event resource allocation factor for measurement-driven demand response programs. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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20 pages, 5971 KB  

Open AccessArticle

ML-Driven Automated Functional Verification Framework for Digital Designs

by Krutthika Hirebasur Krishnappa, Madhura R and Laxmikant Chavan

Electronics 2026, 15(12), 2687; https://doi.org/10.3390/electronics15122687 - 17 Jun 2026

Viewed by 419

Abstract

Ensuring functional correctness in digital circuitry is arguably the most labor-intensive stage of hardware creation, routinely accounting for upwards of 70% of a project’s total resource allocation. While traditional coverage-driven verification (CDV) attempts to validate every operational state, reaching full coverage closure via [...] Read more.

Ensuring functional correctness in digital circuitry is arguably the most labor-intensive stage of hardware creation, routinely accounting for upwards of 70% of a project’s total resource allocation. While traditional coverage-driven verification (CDV) attempts to validate every operational state, reaching full coverage closure via manual intervention or constrained–random techniques requires significant engineering time and domain knowledge. To overcome this bottleneck, this study introduces an automated testing architecture that leverages the Advantage Actor–Critic (A2C) Reinforcement Learning (RL) algorithm. This agent intelligently navigates functional coverage closure across five diverse hardware designs: an Advanced Peripheral Bus Universal Asynchronous Receiver-Transmitter (APB UART), an Serial Peripheral Interface (SPI) Memory unit, a synchronous First-In First-Out (FIFO) queue, an APB RAM, and an Advanced High-performance Bus (AHB) Slave interface. By interfacing QuestaSim 2024.1 with a Python-based intelligent agent via a SystemVerilog DPI-C socket, the system dynamically produces test vectors informed by real-time coverage metrics. Based on evaluations across five distinct random seeds, the methodology successfully attains 95.1% to 100% coverage across all testbenches, with three designs achieving 100% and two reaching 95–98%. Notably, the RL-guided system achieved target coverage using approximately 35% fewer simulation cycles than an unguided random baseline, and 22% fewer cycles compared to a traditional constrained–random setup utilizing expert-defined rules. Ultimately, this framework bypasses the necessity for manual constraint formulation and seamlessly scales to novel hardware environments with negligible setup overhead. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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26 pages, 4334 KB  

Open AccessArticle

RKF-YOLO: A Lightweight Dual-Task Model for Illegal Parking Detection and License Plate Recognition on Edge Devices

by Hao Chen, Yao Li, Yong Jia, Guangle Yao and Ruipeng Zhu

Electronics 2026, 15(12), 2638; https://doi.org/10.3390/electronics15122638 - 15 Jun 2026

Viewed by 315

Abstract

To address the joint requirements of illegal parking detection and license plate recognition under complex traffic scenarios and limited edge-device resources, this study proposes RKF-YOLO, a lightweight dual-task model based on improved YOLOv11n that integrates Rep-CSP structural optimization, knowledge-transfer-enhanced training (KTET), and Focal-CIoU [...] Read more.

To address the joint requirements of illegal parking detection and license plate recognition under complex traffic scenarios and limited edge-device resources, this study proposes RKF-YOLO, a lightweight dual-task model based on improved YOLOv11n that integrates Rep-CSP structural optimization, knowledge-transfer-enhanced training (KTET), and Focal-CIoU loss. Compared with YOLOv11n, RKF-YOLO reduces parameters and FLOPs by 38.2% and 38.1%, respectively, while improving $\mathrm{mAP}@0.5$ and $\mathrm{mAP}@0.5:0.95$ by 0.6 and 1.1 percentage points for parking detection; for plate detection, Focal-CIoU improves $\mathrm{mAP}@0.5:0.95$ by 1.3 percentage points and contributes to a recognition accuracy of 95.7%. The unified framework uses a shared backbone and task-oriented detection heads to support vehicle-level illegal parking detection and license-plate-oriented localization. Rep-CSP enhances multi-scale feature representation, asymmetric channel reduction with feature compensation reduces redundant computation, and KTET improves convergence through optimizer and learning-rate migration. Deployment on RK3588 achieves 59.5 FPS for parking detection and 95.1% recognition accuracy, demonstrating real-time performance and practical applicability on resource-constrained edge devices. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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23 pages, 2042 KB  

Open AccessArticle

High-Precision Thickness Prediction for Medium and Heavy Plate Based on Multi-Model Ensemble and Bayesian Optimization

by Jianzhao Cao, Yangyang Yin and Jingwei Zhang

Electronics 2026, 15(12), 2523; https://doi.org/10.3390/electronics15122523 - 8 Jun 2026

Viewed by 216

Abstract

Thickness accuracy is a critical quality indicator in medium and heavy plate production, as it directly affects material utilization, product performance, and manufacturing cost. The rolling process of medium and heavy plates is highly nonlinear. It also involves multivariable coupling and dynamic fluctuations [...] Read more.

Thickness accuracy is a critical quality indicator in medium and heavy plate production, as it directly affects material utilization, product performance, and manufacturing cost. The rolling process of medium and heavy plates is highly nonlinear. It also involves multivariable coupling and dynamic fluctuations in operating conditions. Therefore, achieving highly accurate and reliable thickness prediction in industrial applications remains a major challenge. To address this issue, this paper develops a joint point-interval prediction framework for medium and heavy plate thickness in industrial applications. First, recursive feature elimination with a LinearSVR estimator (LinearSVR-RFE) is employed to eliminate low-contribution features from the original process feature set, retain informative variables, and construct a compact and effective feature subset. Second, Bayesian optimization is employed to tune the hyperparameters of multiple machine learning regression models. A Stacking ensemble strategy is then adopted to improve the accuracy and robustness of point prediction under complex production conditions. Finally, quantile regression is introduced based on the optimal point prediction model to construct prediction intervals at multiple confidence levels. This provides uncertainty-aware results for production decision-making. Experimental results based on real industrial data from a 3500 mm medium and heavy plate production line show that the proposed framework achieves strong point prediction performance on the test set. The optimal Stacking model achieves a coefficient of determination (R²) of 0.9845 with a root mean square error (RMSE) of 0.73 mm on the test set. In addition, the framework produces prediction intervals with a good balance between coverage and compactness at confidence levels from 80% to 95%. For example, at the 90% confidence level, the interval prediction module achieves a PICP of 0.9043 and a PINAW of 0.0711. The results indicate that the proposed framework provides an effective solution for intelligent thickness prediction and quality evaluation in industrial rolling processes. It also shows good potential for engineering applications. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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29 pages, 8196 KB  

Open AccessArticle

Efficient Fault Rupture Simulation with a Dual-Stage Fourier Neural Operator and Physics-Based Sampling

by Ming Yuan, Zhaohui Guo and Qiang Liu

Electronics 2026, 15(11), 2427; https://doi.org/10.3390/electronics15112427 - 2 Jun 2026

Viewed by 191

Abstract

Accurately simulating fault rupture dynamics is critical for aftershock prediction but remains computationally prohibitive due to the multiscale nature of earthquake processes. While Fourier Neural Operators (FNOs) offer a promising framework for seismic simulation, their direct application to rupture dynamics is hindered by [...] Read more.

Accurately simulating fault rupture dynamics is critical for aftershock prediction but remains computationally prohibitive due to the multiscale nature of earthquake processes. While Fourier Neural Operators (FNOs) offer a promising framework for seismic simulation, their direct application to rupture dynamics is hindered by spectral bias from global processing and resolution loss from uniform downsampling. To overcome these limitations, this paper introduces a novel dual-stage FNO architecture explicitly designed for multiscale rupture simulation. The architecture decouples the problem into a first stage for efficient low-frequency wave propagation in the non-fault zone and a second stage for resolving meter-scale nonlinear rupture dynamics within the fault zone. Then, we propose a physics-based sampling strategy that maintains high resolution in the critical fault zone while coarsening the non-fault zone based on wave-propagation criteria, coupled with an interpolation scheme that enforces conservation of mass, momentum, and energy. Evaluated on the SCEC TPV101 benchmark, our integrated framework achieves a 92.4% reduction in model parameters and a 2.34× speedup in training time compared to a baseline FNO approach, while also reducing the NRMSE in fault zones by 80.1%. Furthermore, the model demonstrates robust generalization to unseen geological parameters. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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25 pages, 1848 KB  

Open AccessArticle

Multi-Stage State Assessment of Breakers Based on TCWGAN-GP and XGBoost Under Insufficient Samples

by Lixia Sun, Ling Wang, Jiahao Wang and Zijia Liu

Sensors 2026, 26(10), 3112; https://doi.org/10.3390/s26103112 - 14 May 2026

Viewed by 467

Abstract

The increasing randomness and volatility of renewable energy resources have raised higher demands for circuit breakers. Utilizing monitoring data enables more accurate condition assessment; however, the imbalance between fault and normal samples hampers the performance of machine-learning-based assessment. To address the overfitting and [...] Read more.

The increasing randomness and volatility of renewable energy resources have raised higher demands for circuit breakers. Utilizing monitoring data enables more accurate condition assessment; however, the imbalance between fault and normal samples hampers the performance of machine-learning-based assessment. To address the overfitting and limited diversity of traditional oversampling methods, this paper proposes a Transformer-conditioned CWGAN-GP (TCWGAN-GP) model to generate multi-class fault samples for data augmentation. The generator of the proposed model takes random noise and class labels as input to capture the distribution characteristics of real fault samples. By combining a transformer-based generator to model inter-feature dependencies among 14 monitoring indicators and a WGAN-GP training objective with gradient penalty, the proposed approach improves training stability and synthetic sample quality. Moreover, a three-stage state assessment method based on XGBoost is developed to sequentially assess health status, fault category, and fault severity. Results demonstrate that the proposed method in the paper outperforms conventional data augmentation methods and single-stage classifiers in terms of accuracy, recall, F1-score, and online prediction efficiency. Specifically, the proposed three-stage model achieves an overall assessment accuracy of 93.10%, outperforming the single-stage XGBoost framework. In terms of online efficiency, the initial anomaly detection stage requires only 0.0041 s per sample, which is a substantial reduction compared to the 0.0241 s required by the single-stage model. Furthermore, compared to traditional Random Oversampling (ROS) and SMOTE, the TCWGAN-GP augmentation yields superior evaluation performance on fully balanced datasets, achieving a recall rate of 91.26% and an F1-score of 92.61%. Overall, the proposed TCWGAN-GP and three-stage XGBoost method contributes to addressing data imbalance challenges and improving the accuracy of circuit breaker state assessment. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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24 pages, 10505 KB  

Open AccessArticle

Design and De-Icing Performance Evaluation of a Stay-Cable De-Icing Robot

by Yaoyao Pei, Xinyan Yu, Lei Xi, Yuzhen Zhao and Feng Gao

Appl. Sci. 2026, 16(10), 4605; https://doi.org/10.3390/app16104605 - 7 May 2026

Viewed by 401

Abstract

In winter, ice readily accretes on the HDPE sheath of stay cables, creating shedding hazards and exacerbating wind-induced vibrations, thereby threatening bridge and traffic safety. Cable-climbing de-icing devices have been proposed to replace manual operations, yet their performance is often limited by climbing [...] Read more.

In winter, ice readily accretes on the HDPE sheath of stay cables, creating shedding hazards and exacerbating wind-induced vibrations, thereby threatening bridge and traffic safety. Cable-climbing de-icing devices have been proposed to replace manual operations, yet their performance is often limited by climbing instability caused by abrupt changes in cable-surface friction. This study develops a quadrotor-driven stay-cable de-icing device that integrates an arc-shaped milling wheel with an embedded heating module to realize thermo-mechanically coupled de-icing. The device climbs via rotor-generated aerodynamic lift and performs continuous top-down de-icing using gravity-assisted motion together with rotor thrust. Laboratory tests and ANSYS LS-DYNA explicit dynamic simulations are conducted to quantify the effects of clamping force and axial thrust on the ice removal ratio in a purely mechanical mode. In addition, a three-stage experimental campaign—temperature-rise, thermo-mechanical de-icing, and thermal-balance tests—is carried out to verify heating feasibility and to examine the roles of heating power and initial wheel temperature. The results indicate that, under purely mechanical de-icing, the ice removal ratio increases monotonically with clamping force and thrust but gradually approaches saturation. Under thermo-mechanical de-icing, higher heating power and initial temperature improve removal performance. Notably, thermo-mechanical de-icing under low thrust achieves a higher removal level than purely mechanical de-icing under high loads, demonstrating improved effectiveness and engineering practicality. An initial equivalence relationship between mechanical parameters and temperature is established to support further optimization. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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34 pages, 7482 KB  

Open AccessReview

Machine Learning for Leakage Diagnosis in Building Pipe Networks: A Review

by Mingyu Chang, Haosen Qin and Zhengwei Li

Buildings 2026, 16(10), 1855; https://doi.org/10.3390/buildings16101855 - 7 May 2026

Viewed by 502

Abstract

Pipe networks are essential components of modern building infrastructure, including heating, ventilation, and air conditioning (HVAC) water systems, water distribution networks (WDNs), and district heating and cooling (DHC) systems. Leakage in these systems can lead to increased energy consumption, loss of thermal efficiency, [...] Read more.

Pipe networks are essential components of modern building infrastructure, including heating, ventilation, and air conditioning (HVAC) water systems, water distribution networks (WDNs), and district heating and cooling (DHC) systems. Leakage in these systems can lead to increased energy consumption, loss of thermal efficiency, and unstable system operation, thereby affecting indoor environmental quality and overall building performance. Despite differences in scale and application, similar leakage mechanisms are also observed in other pipe network systems, such as oil and gas pipelines and liquid cooling networks. These shared characteristics motivate a unified analytical perspective across different applications. This review provides a systematic analysis of leakage diagnosis methods, with a focus on machine learning (ML) approaches. The results indicate that ML methods have become a dominant research direction due to their ability to capture nonlinear relationships and process high-dimensional data. However, their effectiveness is often constrained by the limited availability of labeled leakage data, sensitivity to dynamic operating conditions, and insufficient physical interpretability. This review provides a structured framework for understanding ML-based leakage diagnosis and offers insights into the integration of data-driven and physics-based approaches for pipe network systems. In addition, the potential role of reinforcement learning (RL) is briefly discussed as an emerging direction for handling dynamic and adaptive scenarios. Compared with ML-based methods, RL has not yet been systematically explored in leakage diagnosis and remains at an early stage of development. This review synthesizes current methodologies, identifies key challenges, and outlines future research directions. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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36 pages, 3661 KB  

Open AccessArticle

Intelligent Temperature Control Using Artificial Neural Networks in an IoT-Enabled Cyber-Physical Hot-Air Drying System: Analysis of Drying Kinetics and Thermal Efficiency

by Juan Manuel Tabares-Martinez, Adriana Guzmán-López, Micael Gerardo Bravo-Sánchez, Francisco Villaseñor-Ortega, Juan José Martínez-Nolasco and Alejandro Israel Barranco-Gutierrez

AI 2026, 7(5), 157; https://doi.org/10.3390/ai7050157 - 30 Apr 2026

Cited by 1 | Viewed by 1148

Abstract

This study aims to develop and experimentally evaluate an artificial neural network-based temperature control strategy for hot-air carrot drying within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped with AM2302 (air temperature sensor), MLX90614 (infrared surface temperature sensor), [...] Read more.

This study aims to develop and experimentally evaluate an artificial neural network-based temperature control strategy for hot-air carrot drying within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped with AM2302 (air temperature sensor), MLX90614 (infrared surface temperature sensor), and SHT35 (relative humidity sensor), an HX711 load cell, and a WS68 anemometer, with cloud communication provided by an ESP8266 module for remote monitoring via Wi-Fi. The neural controller, implemented using the Arduino Neurona library, regulates the dryer temperature in real time, enabling drying kinetics analysis under ANN-based thermal control to investigate its capability to maintain thermal stability. Three initial loads (2, 4, and 6 kg) were analyzed to determine the thermal efficiency. In the dehydration experiments, the 2 kg load reached a final moisture content of 10% in 4.4 h, consuming 1390 kJ with a thermal efficiency of 83%. The 4 kg load exhibited the best time–energy balance (6.6 h, 1850.0 kJ, 88%), while the 6 kg load achieved the highest efficiency (8.1 h, 2250.0 kJ, 91%). These results demonstrate the effectiveness of neural-network-based control implemented on low-cost microcontrollers to enhance thermal efficiency in food dehydration processes. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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22 pages, 1687 KB  

Open AccessArticle

Data-Driven Offline Compensation of Robotic Welding Trajectories Using 3D Optical Metrology in Industrial Manufacturing

by Alexandru Costinel Filip, Dorian Cojocaru and Ionel Cristian Vladu

Appl. Sci. 2026, 16(5), 2510; https://doi.org/10.3390/app16052510 - 5 Mar 2026

Viewed by 666

Abstract

The geometric variability of industrial components represents a persistent challenge in robotic arc welding, particularly in high-volume manufacturing environments where parts are positioned in fixtures based on nominal CAD assumptions. Even moderate deviations in dimensions or seating conditions can lead to weld defects, [...] Read more.

The geometric variability of industrial components represents a persistent challenge in robotic arc welding, particularly in high-volume manufacturing environments where parts are positioned in fixtures based on nominal CAD assumptions. Even moderate deviations in dimensions or seating conditions can lead to weld defects, rework, and reduced process capability when conventional offline programming is employed. This paper presents an applied industrial workflow for adaptive robotic welding trajectory correction that integrates full-field 3D optical metrology with a data-driven deep reinforcement learning (DRL) model. Prior to welding, each component is scanned using a structured-light 3D system, and critical geometric deviations are extracted relative to the nominal CAD model. These deviations define a compact state representation that is mapped, via a trained DRL agent, to corrective translational and rotational adjustments of the welding trajectory. Importantly, all trajectory corrections are computed offline, ensuring compatibility with standard industrial robot controllers and avoiding real-time computational overheads. The proposed approach is validated using real production data from an industrial batch of 5000 components characterized by significant dimensional variability and limited process capability. Experimental results demonstrate a reduction in welding defects exceeding 90%, elimination of rework associated with improper part positioning, and an improvement of the overall process performance to a sigma level of 5.219. The results show that combining 3D optical metrology with learning-based trajectory adaptation enables robust compensation of part-level geometric deviations without mechanical fixture modifications. The proposed method provides a practical and scalable solution for improving welding quality in manufacturing environments affected by upstream variability and imperfect part positioning. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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