Automation, Volume 6, Issue 2 (June 2025) – 11 articles

Corrosion, a naturally occurring process leading to the deterioration of metallic materials, demands diligent detection for quality control and the preservation of metal-based objects, especially within industrial contexts. Traditional techniques for corrosion identification, including ultrasonic testing, radiographic testing, and magnetic flux leakage, necessitate the deployment of expensive and bulky equipment on-site for effective data acquisition. An unexplored alternative involves employing lightweight, conventional camera systems and state-of-the-art computer vision methods for its identification. In this work, we propose a complete system for semi-automated corrosion identification and mapping in industrial environments. We leverage recent advances in three-dimensional (3D) point-cloud-based methods for localization and mapping, with vision-based semantic segmentation deep learning techniques, in order to build semantic–geometric maps of industrial environments. Unlike the previous corrosion identification systems available in the literature, which are either intrusive (e.g., electrochemical testing) or based on costly equipment (e.g., ultrasonic sensors), our designed multi-modal vision-based system is low cost, portable, and semi-autonomous and allows the collection of large datasets by untrained personnel. A set of experiments performed in relevant test environments demonstrated quantitatively the high accuracy of the employed 3D mapping and localization system, using a light detection and ranging (LiDAR) device, with less than $0.05$ m and $0.02$ m average absolute and relative pose errors. Also, our data-driven semantic segmentation model was shown to achieve $70\%$ precision in corrosion detection when trained with our pixel-wise manually annotated dataset. Full article

Concrete-printing robots have become an advanced technology in the construction industry that allows the creation of complex structures, while saving materials and shortening construction time compared to traditional methods. With the structure of a concrete 3D printing robot using a concrete extruder with a screw, this mechanism provides stable flow of concrete, and less pressure fluctuation. However, using a large mass extruder changes the inertia of the joint and the mass coefficient of the arm when the mass changes, leading to a position error. With the high demands for precision and stability in the operation of 3D concrete printing robots, advanced control methods have become essential to ensure trajectory tracking and robustness in complex real-world environments. This study provides a sliding mode controller with an error and integral, and derivatives are introduced into the sliding surface to improve the stability of the robot without chattering. The controller exhibits fast convergence times and small trajectory tracking errors, at less than 0.1 mm. Simulation results show that this controller is suitable for concrete 3D printing applications, and the controller exhibits fast and good responses to continuously changing extruder mass. This enables the robot to track the expected trajectory with high accuracy. Full article

The integration of 3D printing technologies in automated manufacturing systems marks a significant progression in the manufacturing industry, enabling elevated degrees of customization, efficiency, and sustainability. This paper explores the synergy between 3D printing and automation by conducting a critical literature review combined with case study analysis, focusing on their roles in enhancing production lines within the framework of Industry 4.0 and smart factories. Key opportunities presented by this integration include mass customization at scale, reduced material waste, and improved just-in-time manufacturing processes. However, challenges related to quality control, scalability, and workforce adaptation remain critical issues that require careful consideration. The study also examines the emerging role of hybrid manufacturing systems that combine additive and subtractive processes, alongside the growing need for standardized regulations and frameworks to ensure consistency and safety. Case studies are highlighted, showcasing real-world applications of automated 3D printing technologies and AI-driven print optimization techniques. In conclusion, this paper contributes to advancing the scholarly understanding of automated 3D printing by synthesizing technical, organizational, and regulatory insights and outlining future trajectories for sustainable and agile production ecosystems. Full article

Static Timing Analysis (STA) plays a crucial role in realizing timing convergence of integrated circuits. In recent years, there has been growing research on pre-routing timing prediction using Graph Neural Networks (GNNs). However, existing approaches struggle with scalability on large graphs and lack generalizability to new designs, limiting their applicability to large-scale, complex circuit problems. To address this issue, this paper proposes a timing engine based on Graph Attention Network (GAT) to predict the slack of timing endpoints. Firstly, our model computes net embeddings for each node prior to training using a gated self-attention module. Subsequently, inspired by the Nonlinear Delay Model (NLDM), the node embeddings are propagated through multiple levels by alternately applying net propagation layers and cell propagation layers. Evaluated on 21 real circuits, the framework achieved a 16.62% improvement in average $R^2$ score for slack prediction and a 15.55% reduction in runtime compared to the state-of-the-art (SOTA) method. Full article

The energy consumption of rotary-wing unmanned aerial vehicles has become an important factor restricting their long-term application. This article focuses on decoupling the motion channel and reducing control energy consumption, and proposes a decoupling controller based on dynamic inversion for the complete dynamics of quadcopter unmanned aerial vehicles. Firstly, we design a direct closed-loop feedback controller for the z-channel to exhibit second-order linear dynamic characteristics with adjustable parameters. Then, the specific functions of pitch angle and yaw angle are combined as virtual control variables for the comprehensive decoupling design of the x-direction and y-direction, so that the x-channel and y-channel also exhibit independent parameter-adjustable second-order linear dynamic characteristics. Next, by solving the actual control variables, a fast convergence system is dynamically formed by the deviation between the virtual control variables and their actual values, ensuring that the specific function combination of pitch angle and yaw angle quickly converges to the expected value. Finally, the effectiveness and low energy consumption control characteristics of the decoupling control scheme were demonstrated through simulation comparison with other control methods (such as classical PID) in terms of energy consumption. Full article

Given the growing need to enhance the accuracy of exploration robots, this study focuses on designing a teleoperated navigation system for a robot equipped with a continuous-track traction system. The goal was to improve navigation performance by developing mathematical models that describe the robot’s behavior, which were validated through experimental measurements. The system incorporates a digital twin based on ROS (Robot Operating System) to configure the nodes responsible for teleoperated navigation. A PID controller is implemented for each motor, with zero-pole cancellation to achieve first-order dynamics, and anti-windup to prevent integral error accumulation when the reference is not met. Finally, a physical implementation was carried out to validate the functionality of the proposed navigation system. The results demonstrated that the system ensured precise and stable navigation, highlighting the effectiveness of the proposed approach in dynamic environments. This work contributes to advancing robotic navigation in controlled environments and offers potential for improving teleoperation systems in more complex scenarios. Full article

Unmanned Surface Vehicles (USVs) have emerged as key enablers of autonomous maritime operations, offering innovative solutions across multiple industries, including defense, oceanography, offshore energy, and logistics. This review examines the current state of operative USVs, analyzing their technological evolution, design characteristics, and applications. The study highlights trends in autonomy, propulsion, endurance, and communication technologies, providing insights based on market-ready platforms. While USVs present significant advantages in terms of efficiency and operational safety, challenges such as regulatory constraints, cybersecurity risks, and limitations in autonomous decision-making persist. This paper aims to update researchers, policymakers, and industry stakeholders on the technological advancements and emerging trends shaping the future of unmanned vehicles. Full article

This paper compares the performance of Deep Learning (DL) and multi-template matching (MTM) models for detecting small defects. DL models extract distinguishing features of objects but require a large dataset of images. In contrast, alternative computer vision techniques like MTM need a relatively small dataset. The lack of large datasets for small metal-surface defects has inhibited the adoption of automation in small-defect detection in remanufacturing settings. This motivated this preliminary study to compare template-based approaches, like MTM, with feature-based approaches, such as DL models, for small-defect detection on an initial laboratory and remanufacturing industry dataset. This study used You Only Look Once v5 (YOLOv5) as the DL model and compared its performance against the MTM model for small-crack detection. The findings of our preliminary investigation are as follows: (i) YOLOv5 demonstrated higher performance than MTM in detecting small cracks; (ii) an extra-large variant of YOLOv5 outperformed a small-size variant; (iii) the size and object variety of the data are crucial in achieving robust pre-trained weights for use in transfer learning; and (iv) enhanced image resolution contributes to precise object detection. Full article

A suspended cable-driven parallel robot is a type of lightweight large-span parallel robot. The stability and control of this multi-input multi-output robot are studied in this work to overcome its inherited vulnerability to disturbance. An adaptive fuzzy synergetic controller is proposed to overcome these issues, combining synergetic control theory with adaptive fuzzy logic to ensure robust trajectory tracking. The parameters of the controller are optimized using the Dragonfly Algorithm, a metaheuristic technique known for its simplicity and fast convergence. The adaptive fuzzy synergetic controller is tested on a suspended cable-driven parallel robot model under both disturbance-free and disturbed conditions, demonstrating global asymptotic stability and superior tracking accuracy compared to existing controllers. Simulation results show the proposed controller achieves minimal tracking error and improved robustness in the presence of dynamic uncertainties, validating its practical applicability in industrial scenarios. The findings highlight the effectiveness of integrating synergetic control, fuzzy logic adaptation, and optimization for enhancing the performance and reliability of suspended cable-driven parallel robots. Full article

Rotating machines predominantly operate under healthy conditions, leading to a limited availability of fault data and a significant class imbalance in diagnostic datasets. These challenges hinder the development and deployment of fault diagnosis methods based on deep learning in practice. Considering these issues, a novel hierarchical adaptive wavelet-guided adversarial network with physics-informed regularization (HAWAN-PIR) is proposed. First, a hierarchical wavelet-based imbalance severity score is used to quantify the data imbalance within the datasets. Second, HAWAN-PIR generates synthetic fault data in the time domain via multiscale wavelet decomposition and represents the first attempt to embed physics-informed regularization to incorporate relevant fault knowledge. The quality of the synthetic fault data is then evaluated via a comprehensive multiscale synthesis quality index. Furthermore, a scale-aware dynamic mixing algorithm is proposed to optimally integrate synthetic data with real data. Finally, a one-dimensional convolutional neural network (1-D CNN) is employed for extracting features and classifying faults. The effectiveness of the proposed method is validated through two case studies: motor bearings and planetary gearboxes. The results show that HAWAN-PIR can synthesize high-quality fake data and improve the diagnostic accuracy of the 1-D CNN by 17% for the bearing case and 15% for the gearbox case. Full article

This paper presents a novel Hybrid Artificial Protozoa Optimizer with Differential Evolution (HPDE), combining the biologically inspired principles of the Artificial Protozoa Optimizer (APO) with the powerful optimization strategies of Differential Evolution (DE) to address complex and engineering design challenges. The HPDE algorithm is designed to balance exploration and exploitation features, utilizing innovative features such as autotrophic and heterotrophic foraging behaviors, dormancy, and reproduction processes alongside the DE strategy. The performance of HPDE was evaluated on the CEC2014 benchmark functions, and it was compared against two sets of state-of-the-art optimizers comprising 23 different algorithms. The results demonstrate HPDE’s good performance, outperforming competitors in 24 functions out of 30 from the first set and 23 functions from the second set. Additionally, HPDE has been successfully applied to a range of complex engineering design problems, including robot gripper optimization, welded beam design optimization, pressure vessel design optimization, spring design optimization, speed reducer design optimization, cantilever beam design optimization, and three-bar truss design optimization. The results consistently showcase HPDE’s good performance in solving these engineering problems when compared with the competing algorithms. Full article