Search Results (94)

Industrial robots are undergoing a transformative shift as Artificial Intelligence (AI)-driven and bio-inspired control strategies unlock new levels of precision, adaptability, and multi-dimensional sustainability aligned with Industry 5.0 (energy efficiency, material circularity, and life-cycle emissions). This systematic review analyzes 160 peer-reviewed industrial robotics control studies (2023–2025), including an expanded bio-inspired/human-centric subset, to evaluate: (1) the dominant and emerging control methodologies; (2) the transformative role of digital twins and 5G-enabled connectivity; and (3) the persistent technical, ethical, and environmental challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the study employs a rigorous methodology, focusing on adaptive control, deep reinforcement learning (DRL), human–robot collaboration (HRC), and quantum-inspired algorithms. The key findings highlight up to 30% latency reductions in real-time optimization, up to 22% efficiency gains through digital twins, and up to 25% energy savings from bio-inspired designs (all percentage ranges are reported relative to the comparator baselines specified in the cited sources). However, critical barriers remain, including scalability limitations (with up to 40% higher computational demands) and cybersecurity vulnerabilities (with up to 20% exposure rates). The convergence of AI, bio-inspired systems, and quantum computing is poised to enable sustainable, autonomous, and human-centric robotics, yet requires standardized safety frameworks and hybrid architectures to fully support the transition from Industry 4.0 to Industry 5.0. This review offers a strategic roadmap for future research and industrial adoption, emphasizing human-centric design, ethical frameworks, and circular-economy principles to address global manufacturing challenges. Full article

The integration of Building Information Modeling (BIM) and Digital Twin (DT) technologies offers new opportunities for enhancing reinforcement design and on-site constructability. This study addresses a current gap in DT applications by introducing an intelligent framework that simultaneously automates rebar layout generation and reduces rebar cutting waste (RCW), two challenges often overlooked during the construction execution phase. The system employs heuristic algorithms to generate constructability-aware rebar configurations and leverages Industry Foundation Classes (IFC) schema-based data models for interoperability. The framework is implemented using Autodesk Revit and Dynamo for rebar modeling and layout generation, Microsoft Project for schedule integration, and Autodesk Navisworks for clash detection. Real-time scheduling synchronization is achieved through IFC schema-based BIM models linked to construction timelines, while embedded clash detection and constructability feedback loops allow for iterative refinement and improved installation feasibility. A case study on a high-rise commercial building demonstrates substantial material savings, improved constructability, and reduced layout time, validating the practical advantages of BIM–DT integration for RC construction. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

Spice by-products, often discarded as waste, represent an untapped resource for sustainable packaging solutions due to their unique, multifunctional, and bioactive profiles. Unlike typical plant residues, these materials retain diverse phytochemicals—including phenolics, polysaccharides, and other compounds, such as essential oils and vitamins—that exhibit controlled release antimicrobial and antioxidant effects with environmental responsiveness to pH, humidity, and temperature changes. Their distinctive advantage is in preserving volatile bioactives, demonstrating enzyme-inhibiting properties, and maintaining thermal stability during processing. This review encompasses a comprehensive characterization of phytochemicals, an assessment of the re-utilization pathway from waste to active materials, and an investigation of processing methods for transforming by-products into films, coatings, and nanoemulsions through green extraction and packaging film development technologies. It also involves the evaluation of their mechanical strength, barrier performance, controlled release mechanism behavior, and effectiveness of food preservation. Key findings demonstrate that ginger and onion residues significantly enhance antioxidant and antimicrobial properties due to high phenolic acid and sulfur-containing compound concentrations, while cinnamon and garlic waste effectively improve mechanical strength and barrier attributes owing to their dense fiber matrix and bioactive aldehyde content. However, re-using these residues faces challenges, including the long-term storage stability of certain bioactive compounds, mechanical durability during scale-up, natural variability that affects standardization, and cost competitiveness with conventional packaging. Innovative solutions, including encapsulation, nano-reinforcement strategies, intelligent polymeric systems, and agro-biorefinery approaches, show promise for overcoming these barriers. By utilizing these spice by-products, the packaging industry can advance toward a circular bio-economy, depending less on traditional plastics and promoting environmental sustainability in light of growing global population and urbanization trends. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

Non-destructive testing (NDT) methods are critical for evaluating the structural integrity of and detecting defects in composite materials across industries such as aerospace and renewable energy. This review examines the recent trends and successful implementations of NDT approaches for composite materials, focusing on articles published between 2015 and 2025. A systematic literature review identified 120 relevant articles, highlighting techniques such as ultrasonic testing (UT), acoustic emission testing (AET), thermography (TR), radiographic testing (RT), eddy current testing (ECT), infrared thermography (IRT), X-ray computed tomography (XCT), and digital radiography testing (DRT). These methods effectively detect defects such as debonding, delamination, and voids in fiber-reinforced polymer (FRP) composites. The selection of NDT approaches depends on the material properties, defect types, and testing conditions. Although each technique has advantages and limitations, combining multiple NDT methods enhances the quality assessment of composite materials. This review provides insights into the capabilities and limitations of various NDT techniques and suggests future research directions for combining NDT methods to improve quality control in composite material manufacturing. Future trends include adopting multimodal NDT systems, integrating digital twin and Industry 4.0 technologies, utilizing embedded and wireless structural health monitoring, and applying artificial intelligence for automated defect interpretation. These advancements are promising for transforming NDT into an intelligent, predictive, and integrated quality assurance system. Full article

Shape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based control methodologies to address these limitations, analyzing their efficacy in enhancing precision, adaptability, and reliability for SMA and Magnetic SMA (MSMA) systems. A PRISMA-guided literature review (2003–2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. Results revealed hybrid AI-linear controllers as the dominant approach (36%), with online-trained neural networks achieving superior accuracy (+2.4%) over offline methods. Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). Sensorless strategies proved advantageous for MSMAs (+5.0%), leveraging intrinsic material properties like electrical resistance for state estimation. The analysis underscores AI’s capacity to mitigate hysteresis and non-linear dynamics, though material-specific optimization is critical: SMA systems favor dynamic control and MPC, whereas MSMAs benefit from sensorless AI and pure neural networks. Challenges persist in computational demands for online training and reinforcement learning’s exploration–exploitation trade-offs. Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. This synthesis establishes AI as a transformative paradigm for SMA actuation, enabling precise control in aerospace, biomedical, and soft robotics applications. Full article

Elevators are a vital part of urban infrastructure, playing a key role in smart cities where increasing population density has driven the rise in taller buildings. As an essential means of vertical transportation, elevators have become an integral part of daily life, making their design, construction, and maintenance crucial to ensuring safety and compliance with evolving industry standards. The safety of elevator systems depends on the continuous monitoring and fault-free operation of Permanent Magnet Synchronous Motor (PMSM) drives, which are critical to their performance. Furthermore, the fault-free operation of PMSM drives reduces operating costs, increases service life, and improves reliability. The PMSM drive components may be susceptible to electrical, mechanical, and thermal faults that, if undetected, can lead to operational disruptions or safety risks. The integration of artificial intelligence and Internet of Things (IoT) technologies can enhance fault prediction, reducing downtime and improving efficiency. Ongoing challenges such as managing machine thermal load and developing more durable materials for PMSMs require the development of suitable models that are adapted to existing drive systems. The proposed framework for fault prediction is validated on a real residential elevator equipped with a PMSM drive. Multimodal signal data is processed through a Generative Adversarial Network (GAN)-enhanced Positive Unlabeled (PU) classifier and a Reinforcement Learning (RL)-based adaptive decision engine, enabling robust and scalable fault prediction in a non-intrusive fashion. Full article

Friction stir spot welding (FSSW) is a solid-state joining technique increasingly favored in industries requiring high-quality, defect-free welds in lightweight and durable structures, such as the automotive, aerospace, and marine industries. This review examines the current advancements in FSSW, focusing on the relationships between microstructure, properties, and performance under load. FSSW offers numerous benefits over traditional welding, particularly for joining both similar and dissimilar materials. Key process parameters, including tool design, rotational speed, axial force, and dwell time, are discussed for their impact on weld quality. Innovations in robotics are enhancing FSSW’s accuracy and efficiency, while numerical simulations aid in optimizing process parameters and predicting material behavior. The addition of nano/microparticles, such as carbon nanotubes and graphene, has further improved weld strength and thermal stability. This review identifies areas for future research, including refining robotic programming, using artificial intelligence for autonomous welding, and exploring nano/microparticle reinforcement in FSSW composites. FSSW continues to advance solid-state joining technologies, providing critical insights for optimizing weld quality in sheet material applications. Full article

Non-destructive testing (NDT) and non-destructive evaluation (NDE) are essential tools for ensuring the structural integrity, safety, and reliability of critical systems across the aerospace, civil infrastructure, energy, and advanced manufacturing sectors. As engineered materials evolve into increasingly complex architectures such as fiber-reinforced polymers, fiber–metal laminates, sandwich composites, and functionally graded materials, traditional NDT techniques face growing limitations in sensitivity, adaptability, and diagnostic reliability. This comprehensive review presents a multi-dimensional classification of NDT/NDE methods, structured by physical principles, functional objectives, and application domains. Special attention is given to hybrid and multi-material systems, which exhibit anisotropic behavior, interfacial complexity, and heterogeneous defect mechanisms that challenge conventional inspection. Alongside established techniques like ultrasonic testing, radiography, infrared thermography, and acoustic emission, the review explores emerging modalities such as capacitive sensing, electromechanical impedance, and AI-enhanced platforms that are driving the future of intelligent diagnostics. By synthesizing insights from the recent literature, the paper evaluates comparative performance metrics (e.g., sensitivity, resolution, adaptability); highlights integration strategies for embedded monitoring and multimodal sensing systems; and addresses challenges related to environmental sensitivity, data interpretation, and standardization. The transformative role of NDE 4.0 in enabling automated, real-time, and predictive structural assessment is also discussed. This review serves as a valuable reference for researchers and practitioners developing next-generation NDT/NDE solutions for hybrid and high-performance structures. Full article

With the growing integration of electronic systems into modern infrastructure, the need for effective electromagnetic interference (EMI) shielding materials has intensified. This study explores the development of electroless Ni-plated fiber composites and systematically investigates the effects of post-heat treatment on their electrical, structural, and interfacial performance. Both carbon fibers (CFs) and glass fibers (GFs) were employed as reinforcing substrates, chosen for their distinct mechanical and thermal characteristics. Ni plating enhanced the electrical conductivity of both fibers, and heat treatment facilitated phase transformations from amorphous to crystalline Ni₃P and Ni₂P, leading to improved EMI shielding effectiveness (EMI-SE). NGF-based composites achieved up to a 169% increase in conductivity and a 116% enhancement in EMI-SE after treatment at 400 °C, while NCF-based composites treated at 800 °C attained superior conductivity and shielding performance. However, thermal degradation and reduced interfacial shear strength (IFSS) were observed, particularly in GF-based systems. The findings highlight the importance of material-specific thermal processing to balance functional performance and structural reliability. This study provides critical insights for designing fiber-reinforced composites with optimized EMI shielding properties for application-driven use in next-generation construction materials and intelligent infrastructure. Full article

In recent years, the rapid development of three-dimensional (3D)-printed continuous fiber-reinforced polymer (CFRP) technology has provided novel strategies for customized manufacturing of high-performance composites. This review systematically summarizes research advancements in material systems, processing methods, mechanical performance regulation, and functional applications of this technology. Material-wise, the analysis focuses on the performance characteristics and application scenarios of carbon fibers, glass fibers, and natural fibers, alongside discussions on the processing behaviors of thermoplastic matrices such as polyetheretherketone (PEEK). At the process level, the advantages and limitations of fused deposition modeling (FDM) and photopolymerization techniques are compared, with emphasis on their impact on fiber–matrix interfaces. The review further examines the regulatory mechanisms of fiber orientation, volume fraction, and other parameters on mechanical properties, as well as implementation pathways for functional designs, such as electrical conductivity and self-sensing capabilities. Application case studies in aerospace lightweight structures and automotive energy-absorbing components are comprehensively analyzed. Current challenges are highlighted, and future directions proposed, including artificial intelligence (AI)-driven process optimization and multi-material hybrid manufacturing. This review aims to provide a comprehensive assessment of the current achievements in 3D printing CFRP technology and a forward-looking analysis of existing challenges, offering a systematic reference for accelerating the transformation of 3D printing CFRP technology from laboratory research to industrial-scale implementation. Full article

The growing need for sustainable packaging materials with enhanced functionality has prompted our investigation into biodegradable polymers reinforced with nanostructures. In this work, we began by extracting anthocyanins from pigmented native potatoes (Solanum tuberosum) and confirming their concentration via UV–Visible spectroscopy. The corresponding potato starch was then characterized according to its amylose and amylopectin contents. The natural pigments subsequently served as reducing and stabilizing agents in a green synthesis of silver nanoparticles (AgNPs), which were subsequently incorporated into starch matrices derived from the same tuber. To evaluate the performance of the resulting composite films, we examined their pH-responsive color behavior—demonstrating their potential as visual indicators—their molecular structure through FTIR analysis—to verify the successful integration of AgNPs—and their moisture content as a measure of barrier properties. The AgNP-containing films exhibited markedly improved color stability across varying pH levels and superior moisture retention compared to pure starch films. These results illustrate the promise of combining underutilized Andean crops with eco-friendly nanotechnology to produce advanced, biodegradable materials suitable for intelligent food-packaging applications. Full article

The growing energy demands and increasing environmental concerns in industrial manufacturing necessitate innovative solutions to reduce fuel consumption and lower carbon emissions. This paper presents Sustain AI, a multi-modal deep learning framework that integrates Convolutional Neural Networks (CNNs) for defect detection, Recurrent Neural Networks (RNNs) for predictive energy consumption modeling, and Reinforcement Learning (RL) for dynamic energy optimization to enhance industrial sustainability. The framework employs IoT-based real-time monitoring and AI-driven supply chain optimization to optimize energy use. Experimental results demonstrate that Sustain AI achieves an 18.75% reduction in industrial energy consumption and a 20% decrease in CO₂ emissions through AI-driven processes and scheduling optimizations. Additionally, waste heat recovery efficiency improved by 25%, and smart HVAC systems reduced energy waste by 18%. The CNN-based defect detection model enhanced material efficiency by increasing defect identification accuracy by 42.8%, leading to lower material waste and improved production efficiency. The proposed framework also ensures economic feasibility, with a 17.2% reduction in operational costs. Sustain AI is scalable, adaptable, and fully compatible with Industry 4.0 requirements, making it a viable solution for sustainable industrial practices. Future extensions include enhancing adaptive decision-making with deep RL techniques and incorporating blockchain-based traceability for secure and transparent energy management. These findings indicate that AI-powered industrial ecosystems can achieve carbon neutrality and enhanced energy efficiency through intelligent optimization strategies. Full article

Hydraulic sandblasting perforation guns play a critical role in well completion and productivity enhancement operations in oil and gas wells, as their performance and service life directly affect perforation efficiency, reservoir integrity, and downhole operational safety. Drawing on a comprehensive review of the existing literature, this paper systematically summarizes recent research progress on surface erosion, high-pressure leakage, and vibration-induced fatigue in perforation guns. Regarding erosion wear, we discuss the mechanisms and preventive strategies influenced by abrasive particle flow characteristics, material selection, and coating applications. In the field of high-pressure leakage, we analyze the key factors of seal failure, structural deformation, and material degradation that contribute to leakage formation, and we provide improvement measures involving seal structure optimization, enhanced material properties, and real-time monitoring technologies. Concerning vibration and fatigue, we elucidate the multi-factor coupling mechanisms of failure—encompassing fluid–solid interactions, cavitation impacts, and stress concentration—and outline mitigation strategies through structural redesign, material reinforcement, and fluid dynamic control. Furthermore, the paper anticipates the future trends of intelligent fault diagnosis and predictive maintenance, including multi-sensor data fusion, AI-driven predictive models, and digital twin technologies. Overall, the integrated application of precision design, dynamic optimization, and intelligent control across the entire service life of perforation guns is poised to guide forthcoming research and engineering practices, driving hydraulic sandblasting perforation technology toward greater efficiency, reliability, and intelligence. Full article