Search Results (340)

This article presents an integrated approach that combines Virtual Reality (VR), Augmented Reality (AR), and an Intelligent Virtual Assistant (IVA) to support training, on-the-job assistance, and maintenance in a textile manufacturing environment. The solution spans three systems: RioRV, a Unity-based VR platform for immersive, step-by-step procedure rehearsal, instructional videos, and simplified 3D animations; RiAR, a mobile AR application for assisted maintenance and access to real-time and historical machine data using marker-based (VuMark) identification; and Ria, a web-based IVA that delivers document-grounded answers, operational queries over a secure plant API, short-horizon forecasting, and a narrow set of guarded remote actions. The architecture prioritizes human-centered Industry 5.0 principles—safety, usability, and resilience—by enabling operators to learn procedures in VR, execute tasks with AR overlays and maintenance media at the workstation, and obtain concise, source-cited guidance via the IVA without leaving immersion. In the case study with a spinning section at RIOPELE, the convergence of VR, AR, and IVA reduced reliance on bulky manuals, shortened time-to-information for machine status, and established a feedback loop in which training and operational experience continuously enrich the knowledge base. Full article

Concrete construction is associated with high environmental impact and geometric limitations imposed by conventional formwork, which has led to growing interest in combining structural optimization with robotic fabrication. In this study, structural optimization refers to computational methods such as topology optimization, shape optimization, and form finding that aim to improve material efficiency and load-bearing performance by modifying the geometry of structural elements. This systematic review investigates how these optimization approaches are translated into fabrication-aware design workflows for robotic concrete construction. Following a PRISMA-based methodology, 90 peer-reviewed studies published between 2015 and 2025 were analyzed. The review focuses on fabrication routes including (i) 3D concrete printing, (ii) 3D-printed formwork, (iii) shotcrete-based additive manufacturing, and (iv) controlled casting systems, and examines how each route constrains geometry representation, design decisions, toolpath generation, and robotic execution. The review analyzes design-to-fabrication workflows that link optimized structural geometry to production logic and process control. Key findings indicate that incorporating fabrication constraints at early design stages can support buildability and potential material efficiency, while reinforcement integration and quality control remain critical challenges for structural reliability. The review also highlights the increasing role of in situ sensing and feedback-driven automation in improving process stability. Overall, the study clarifies current practices, limitations, and emerging directions for integrating structural optimization with robotic concrete fabrication. Full article

The integrity of halal supply chains is increasingly threatened by fragmented paper-based records, certificate fraud, and the absence of real-time traceability. This paper presents HalalChain, a blockchain-based halal product traceability system that enforces role-based access control (RBAC) through three Solidity smart contracts deployed on an Ethereum-compatible blockchain. HalalChain is designed for production deployment on an EVM-compatible Layer-2 or sidechain such as Polygon or BNB Chain, on which the contracts run without code changes. A dual-storage architecture synchronises every supply chain event to both a PostgreSQL relational database and the blockchain, balancing on-chain immutability with off-chain query performance. The system supports five stakeholder roles, namely administrator, supplier, manufacturer, logistics, and retailer, each restricted to specific supply chain event types enforced at the smart contract level. Consumers can verify product halal status and full supply chain history by scanning a QR code linked to a public verification endpoint that cross-checks database records against on-chain event counts, producing a chain-integrity indicator. As the current chain-integrity check is count-base, it can detect missing or extra database rows, but it cannot detect content-level modification if the row count remains unchanged. A total of 107 automated test cases were executed covering functional correctness, edge cases, end-to-end integration, and gas performance benchmarks. Core smart contract operations consume between 25,365 and 213,684 gas units, indicating feasible deployability on Ethereum-compatible networks. An exploratory analysis was carried out with a preliminary survey of 40 respondents (mean = 4.10 on a 5-point Likert scale), suggesting that consumer demand for blockchain-verified halal certification is encouraging. The results demonstrate that HalalChain provides a tamper-evident, role-enforced traceability foundation for the halal food industry. The system secures the digital chain of custody cryptographically and the physical–digital binding between the QR code, and the product remains a separate trust assumption requiring complementary anti-tamper mechanisms. Full article

Artificial intelligence (AI) has transformed orthodontic diagnosis, yet its application to orthodontic materials science remains critically underexplored. This perspective identifies and characterizes the AI–materials integration gap as the central unresolved problem in digital orthodontics: AI-optimized treatment plans are currently executed through empirically selected materials whose mechanical behavior is never modeled by the planning system. We examine four domains where this gap is consequential: thermoplastic aligner polymers (PETG vs. TPU), where supervised ANNs can predict force decay from polymer composition; NiTi archwire alloys, where Bayesian optimization and Gaussian process regression are accelerating alloy design; additive manufacturing of orthodontic devices, where supervised ML reduced print-parameter optimization burden in a 2025 five-variable surface roughness study; and AI-driven biological response prediction, where FEA-surrogate neural networks reduced biomechanical computation from minutes to milliseconds per patient query. A scoping review of clear aligner AI identified 41 studies—none addressing aligner material properties as a primary outcome. We argue that closing the AI–materials gap requires standardized open material-performance datasets; FEA-surrogate models integrating polymer stiffness as a treatment-planning input; patient-specific digital twins with defined material, mechanical, and biological parameter layers; and federated learning infrastructure spanning clinics and manufacturers. Full article

In the digital era, enterprises face increasing pressure to align strategic objectives with operational execution under volatile and data-intensive conditions. Traditional management control systems often rely on lagging indicators and ad hoc interventions, limiting both performance visibility and sustainability outcomes. This study develops MOD-FCA, a prescriptive, multi-layered closed-loop management control framework that links value-centric outcomes to business-centric drivers through vertically aligned metrics, objective tensors, and tiered corrective routines. Using a longitudinal case study in a manufacturing enterprise, we illustrate how MOD-FCA enhances operational traceability, supports systematic deviation identification and response, and institutionalizes organizational knowledge for continuous improvement. Importantly, MOD-FCA is designed to support sustainable industrial practices by embedding sustainability-related metrics, such as resource efficiency, energy intensity, waste reduction, and process compliance, into the same metric deployment, deviation triggering, and corrective-action logic used for operational control. Qualitative feedback from managerial and operational roles indicates that MOD-FCA strengthens accountability, ensures role-aligned responses, and fosters proactive, data-driven decision-making. These findings provide both theoretical contributions to management control system design and practical guidance for enterprises seeking to achieve both operational excellence and long-term sustainability. Full article

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. In the teaching stage, a human operator demonstrates the desired packing layout only once. The system uses reference-prompted SAM-based contour refinement to extract box and in-box object contours, object categories, quantities, and relative position and orientation constraints. These constraints are then converted from pixel-plane measurements into box-local pose constraints, forming a reusable box-centric packing template that preserves both translational and angular layout information. During execution, the recorded template is transferred to detected box instances with different global poses, and executable pick-and-place commands are generated through a task-level perception-to-command pipeline. A mechanism for continuous assignment and state updates is further introduced to maintain residual target slots, update object-to-slot allocation, and report missing or redundant objects across execution rounds. Single-box template transfer experiments achieved mean placement errors of 7.16 mm and 7.57 mm for two recorded templates, while representative post-execution images further showed that the relative object orientations were visually preserved with respect to the taught template footprints. Multi-box experiments demonstrated that unfinished residual slots could be preserved and completed after scene updates without re-teaching. Additional validation with different container types and object shapes showed the feasibility of extending the framework beyond cube-only cases. Ablation tests under nine exposure settings further showed that SAM refinement improved template-acquisition robustness compared with the previous recognition method. These results verify that the proposed framework enables one-shot template acquisition, box-centric layout transfer, relative pose preservation, and persistent task-level execution for constrained robotic packing tasks. Full article

To address the difficulty of directly applying finite element simulation trajectories to actual spinning machines, as well as the discrepancy between simulation data and equipment execution in the spinning process, this paper proposes a trajectory mapping method based on simulation trajectory extraction and data-driven error compensation. First, based on secondary development in ABAQUS, a discrete shell is introduced and combined with coordinate transformation to achieve accurate extraction of the roller center trajectory, and the simulated trajectory is converted into a discrete coordinate sequence. Subsequently, a roller trajectory acquisition and visualization system is developed, and machine motion data are collected and visualized for comparative analysis via the Modbus-RTU protocol. On this basis, to address the systematic deviation between simulated and actual execution trajectories, a trajectory error compensation method based on the projection method and Gaussian Process Regression is proposed. By modeling normal-direction errors and applying normal-direction compensation, smooth and stable optimization of the original simulation trajectory is achieved. Finally, experimental validation is conducted on a single-roller spinning machine, and the variation in trajectory deviation before and after compensation is comparatively analyzed. The results show that the proposed method effectively reduces trajectory execution errors, decreasing the average error from 0.503 mm to 0.229 mm, and significantly improves trajectory matching accuracy. This study provides an effective technical pathway for high-precision transformation of simulation trajectories to actual equipment in spinning processes and offers important support for the transition from experience-driven to model-driven spinning manufacturing. Full article

Dual 6 degrees of freedom (6-DOF) series-parallel cooperative robot actuators are core execution components in modern intelligent manufacturing systems, which are widely used in high-end manufacturing scenarios such as aerospace precision assembly, laser precision machining, and core component assembly of new energy vehicles. However, in actual manufacturing processes, the pose deviation between theoretical model prediction and actual motion execution of the actuator, caused by kinematic model mismatch, unquantified core parameters, incomplete error processing chain, and complex on-site environmental interference, severely restricts the assembly accuracy, product qualification rate and production efficiency of the manufacturing system. To address these critical pain points of robot actuators in precision manufacturing systems, this paper proposes a four-layer hierarchical decoupling multi-modal fusion calibration algorithm for high-precision pose control of dual series-parallel robot actuators. The algorithm integrates singular value decomposition (SVD) for cross-structure coordinate alignment of heterogeneous actuators, chaotic mapping-enhanced particle swarm optimization (PSO) for nonlinear error suppression of the actuator system, attention-enhanced deep residual network (DRN) for unmodeled residual learning of the actuator, and Kalman filter (KF) for dynamic noise reduction in the manufacturing process. Meanwhile, a full-chain error transfer model of the actuator system in the manufacturing process is constructed, and the core parameters of the algorithm are quantified via dimensional sensitivity analysis and orthogonal experiments. Experimental results show that the static position error of the actuator system after calibration reaches 1.4 ± 0.08 mm, and the static pose error reaches 0.0059 ± 0.0003 rad in the laboratory environment; in the engineering application of laser precision machining in an actual manufacturing line, the position error and pose error only increase by 8.6% and 6.8% respectively, maintaining high stability in industrial manufacturing scenarios. Compared with mainstream calibration methods, the proposed algorithm reduces the position error and pose error of the actuator by up to 55.7% and 17.9% respectively, with lower computational complexity and higher engineering reproducibility. This work constructs an end-to-end error suppression chain with quantitative parameter criteria for the series-parallel actuator system in manufacturing systems, which provides a reliable high-precision calibration solution for industrial dual-robot cooperative manufacturing and has important guiding significance for improving the motion accuracy and operation stability of actuators in precision manufacturing systems. Full article

Access to operational information in industrial plants forces operators to interrupt their tasks, walk to the human–machine interface (HMI) terminals, and navigate heterogeneous platforms—namely programmable logic controllers (PLC), supervisory control and data acquisition (SCADA) systems, manufacturing execution systems (MES), and enterprise resource planning (ERP) systems—consuming 15–30 min per query. Previous work integrated local large language models (LLMs) into a five-layer cognitive architecture deployed in a precast concrete plant, reducing that time to 14–23 s through voice-based conversational queries; however, model inference accounted for 55.3% of total latency and the system remained reactive. This work incorporates the event-driven paradigm as a non-intrusive augmentation layer that keeps the operational context permanently updated, continuously monitoring the process and refreshing knowledge only when significant changes occur. The architecture is fully local, cloud-independent, graphics processing unit (GPU)-free, and containerized via Docker Compose. Experimental results demonstrate a 26–31% reduction in response times (means of 9.84 s, 11.23 s, and 16.47 s for simple, moderate, and complex queries), an 8.4 °C reduction in peak hardware temperature (from 79.6 °C to 71.2 °C), a 41.6% decrease in thermal variability, and an expansion of the safety margin before central processing unit (CPU) throttling from 5.4 °C to 13.8 °C. The system achieved 100% success rate and availability over 30 min of autonomous operation, validated in a real industrial environment. Full article

This study aims to explore the impact of artificial intelligence adoption in accounting systems (AIAS) on organizational performance (OP). Further, the study explores the mediating role of financial decision-making quality (FDMQ) on the AIAS-OP relationship. The sample comprises 583 accountants, finance managers, CFOs, and auditors in all firms listed on the Egyptian Stock Exchange (EGX), covering banking, IT, manufacturing, and service sectors. Data were analyzed using Smart-PLS 4 software. The results revealed a positive and significant impact of AIAS on both FDMQ and OP. Further, the results revealed a positive and significant impact of FDMQ on OP. Finally, FDMQ showed a significant mediating role between AIAS and OP. These results have significant implications for policymakers, investors, regulators, and corporate executives, emphasizing the crucial role played by AIAS and FDMQ in shaping OP, particularly within emerging markets such as Egypt. This study provides a valuable contribution to the accounting literature by highlighting the impactful consequences of AIAS and FDMQ on OP in a unique and unexplored context. Furthermore, this research underscores the vital role that FDMQ assumes in mediating the relationship between AIAS and OP, contrasting with earlier studies in the literature which primarily examined the direct impact of AIAS or FDMQ on OP. Full article

The tube-notching process is widely used to manufacture structural joints and ducting systems for fluid transport. In these applications, accurate intersection angles and proper fit-up geometry are essential to ensure reliable assembly and system performance. Consequently, CNC-based automation is increasingly adopted to improve productivity in operations where precision and cycle time are critical. The main problem, however, lies in the complexity of generating accurate cutting trajectories for tube–tube intersections and converting them into machine-executable commands. This study addresses this gap by proposing a simple, novel mathematical model for toolpath generation capable of producing intersection profiles at arbitrary joint angles, including lateral offset (non-coaxial) configurations. A systematic procedure was developed to convert the resulting trajectories into G-code, which was processed in a low-cost CNC plasma cutter designed to experimentally validate the toolpaths. The machine incorporates a fourth axis to enable bevel cutting during tube processing. Experimental results demonstrate stable operation, high dimensional accuracy (error ±0.1°), and consistent cut quality for trajectories generated by the proposed model, confirming the feasibility of the low-cost CNC plasma system and its scalability to diverse fabrication requirements. Full article

Critical infrastructures are increasingly Internet-connected cyber–physical systems whose recovery after cyber incidents must satisfy safety, timing, regulatory, and interdependency constraints. Yet, the use of large language models (LLMs) for generating recovery plans remains fragmented across cybersecurity, industrial control, digital twins, and AI assurance research. This review synthesizes that emerging field through a structured critical survey of studies on LLMs in incident response, OT/ICS resilience, and cyber–physical recovery, with a focused perspective on grounding, trust, and assurance mechanisms relevant to recovery-plan generation. It develops an architecture-centric taxonomy spanning prompt-only assistants, retrieval-augmented copilots, graph-aware planners, multi-agent systems, and hybrid verification/simulation pipelines; maps realistic applications across energy, water, manufacturing, transportation, healthcare, and telecommunications; and organizes limitations into technical, security, governance, and human-factor categories. Based on this synthesis, the paper proposes the Grounded Recovery Planning Stack as a reference architecture and outlines a staged roadmap from human-in-the-loop copilots to bounded orchestration. The main conclusion is that near-term value lies in grounded, auditable, compliance-aware copilots, whereas autonomous recovery execution remains premature without stronger validation, state-aware grounding, sector-specific benchmarks, and formal safeguards. Full article

The use of prosthetic devices can significantly improve the quality of life of individuals with limb amputations. However, existing prosthetic hands face multiple engineering and manufacturing challenges, making them economically inaccessible to a large portion of the population. This study focuses on the design and analysis of a cost-effective prosthetic hand capable of performing five fundamental grasp types: tripod, cylindrical, spherical, lateral, and pinch. The development process began with a biomechanical analysis of the human hand, followed by the derivation of a kinematic model. To ensure anthropomorphic fidelity, finger trajectories were synthesized using a computer vision-based algorithm that captured natural human motion. These trajectories were then mapped to the prosthetic control system. Experimental validation was conducted through rigorous goniometric analysis of the prototype’s execution. The results demonstrated the system’s effectiveness in replicating functional grasps, with a Root Mean Square Error (RMSE) within acceptable thresholds for assistive tasks. While the prototype achieved high motion correspondence, higher deviations were observed in distal joints due to mechanical transmission resistance and spring-return torque requirements. This work provides a scalable framework for tendon-driven prostheses, balancing advanced trajectory synthesis with a robust and accessible mechanical architecture. Full article

Centralised architectures in contemporary manufacturing systems impose structural constraints on resilience, scalability, and operational transparency that existing approaches have failed to resolve. This work reports the development and empirical validation of AgentBlock, a framework integrating blockchain technology with multi-agent robotic systems to enable decentralised autonomous manufacturing. The architecture operates across three functionally decoupled layers: a React-based decentralised application interface, an Ethereum Sepolia blockchain interaction layer with Solidity 0.8.18 smart contracts following an upgradeable proxy architecture (EIP–1967) coordinated through an Industrial PoA consensus mechanism, and a physical execution layer comprising two heterogeneous robotic agents (KUKA youBot and UFactory Lite 6) and one edge validation agent on an NVIDIA Orin platform that also hosts Q-Learning optimisation, with inter-agent coordination provided by ROS Noetic Ninjemys under Ubuntu 20.04 LTS. Experimental validation conducted over 15 days across 1500 training episodes in a controlled 5 m × 3 m industrial laboratory reveals a task success rate of 95.58%, sustained throughput of 49.0 tasks per hour, average cycle time of 1.224 min, blockchain transaction latency below 15 s (mean: 11.4 s), and gas costs averaging US $0.000669 per operation. These findings establish that blockchain-enabled autonomous manufacturing is not merely theoretically sound but operationally viable, delivering immutable traceability, decentralised coordination, and transparent verification at performance levels compatible with Industry 4.0 and 5.0 production demands. Full article

Overall equipment effectiveness (OEE) is a consolidated metric for manufacturing performance, but the exponential growth of data generated by manufacturing execution systems (MES) in Industry 4.0 environments renders manual analysis impractical. This study investigates whether three freely available generative artificial intelligence (AI) tools—ChatGPT, Gemini, and Copilot—can support the diagnosis of OEE losses and the formulation of improvement actions in a real CNC machining cell. A single-case, mixed-method study was conducted in a Brazilian metalworking company from January 2023 to July 2025, using 31 months of MES-derived OEE data from a two-lathe U-shaped cell. The three large language models were queried under an identical prompt protocol, and their outputs were benchmarked against operationalized criteria of consistency, depth, and sensitivity, then validated by a cross-functional team of operations, maintenance, quality, and tooling experts. All three tools converged in identifying availability as the main loss driver (with setup-time growth as a secondary bottleneck), while differing in diagnostic depth and sensitivity. The validated diagnostics were consolidated into a five-phase strategic roadmap structured by the 5W2H method, producing expected ranges, derived from AI-projected scenarios and cross-functional team consensus, of approximately +18 percentage points in availability and +22 percentage points in cell OEE over a 90-day horizon. These ranges represent projections to be empirically validated in a planned longitudinal follow-up study, not post-intervention measurements. The study contributes a replicable low-cost framework for integrating free generative AI into OEE management and explicitly documents the limitations of non-deterministic, uncurated LLMs in industrial decision support. Full article