Search Results (73)

High-voltage wiring harnesses in new energy vehicles (NEVs) are susceptible to thermal aging in high-temperature environments, whereas conventional assessment methods are difficult to deploy rapidly. This study combines laser-induced breakdown spectroscopy (LIBS) with random forest (RF) classification to assess thermal aging levels in cross-linked polyethylene (XLPE) insulation. Thirteen laboratory-aged XLPE wiring harness samples were prepared, and 100 single-shot spectra were acquired at fresh positions for each aging level. The specific methodological contribution is the use of class-wise median absolute deviation (MAD) at each wavelength as a variable-selection criterion before RF training. Three models were compared: RF, principal component analysis (PCA) combined with RF (PCA–RF), and MAD combined with RF (MAD–RF). RF achieved 100% internal hold-out accuracy for the non-aged versus 60-day comparison and 84.23% across all 13 aging levels. PCA–RF increased the multiclass accuracy to 87.31%, whereas MAD–RF reached 95.00% under the original exploratory hold-out workflow. These results indicate that wavelength-wise robust dispersion can provide a compact, discriminative representation of the present LIBS dataset. Full article

Nowadays, modern manufacturing industries still rely on the expertise and manual dexterity of highly skilled operators. In this context, Mixed Reality (MR) technologies are emerging as promising solutions for contextualized task guidance to support operators during complex activities. However, their effective adoption in production environments is still limited by the lack of ergonomic evidence regarding their impact on operators’ well-being, usability, and interaction sustainability. This study investigates whether different interaction modalities with holographic instructional content influence the ergonomic suitability of a MR-based task guidance system for wire harnessing, developed in collaboration with Leonardo S.p.A. A between-subjects experimental design was adopted, involving 16 industrial workers randomly assigned to two groups: gaze and gesture interaction (Group A, n = 8), and gaze and voice interaction (Group B, n = 8). Ergonomic evaluation included both physical and cognitive aspects, assessed respectively through the Simulator Sickness Questionnaire (SSQ) and a composite usability index (UI) based on ISO 9241-11, integrating efficiency, effectiveness, and satisfaction. Results suggest comparable usability levels between the two interaction modalities (UI_A = 0.683; UI_B = 0.667). Gesture interaction was perceived as slightly more supportive of operational efficiency, whereas voice interaction was associated with lower cybersickness severity (mean TS_A = 331.5; TS_B = 196.1). Overall, the findings suggest that no single interaction modality universally outperforms the other, but rather that ergonomic suitability depends on the balance between physical workload, cognitive demands, and task characteristics. These results highlight the importance of human-centered ergonomic evaluations in the design of sustainable MR assistance systems for industrial environments. Full article

Wire harness assembly is a highly manual job performed on formboards. Augmented reality (AR)-assisted wiring operations can improve work efficiency and reduce operator workload. However, investigations into the effects of AR-assisted wiring assembly on operator performance remain in the preliminary stage. To investigate how different AR wire harness modes support novice operators in completing assembly tasks effectively, this exploratory laboratory study examined the impacts of AR instruction modes for single-route conditions on assembly performance (task time and number of assembly errors), gaze behavior using eye-tracking data, and subjective experience measured with the NASA-TLX (Task Load Index) as a post-experiment questionnaire in a controlled laboratory environment. Three wire path visualization modes, i.e., static color mode (SCM), dynamic color mode with flashing display (DCM-FD), and dynamic color mode with segment display (DCM-SD), were implemented for monitor-based, AR-assisted wiring instruction on a formboard. The results reveal a substantial influence of the wire path visualization modes on task time under our controlled experimental conditions: the SCM group achieved an 18% shorter task time than the other two groups, with a statistically significant difference. This finding contradicts the existing observations in the mechanical assembly domain. For gaze behavior, an analysis of the eye-tracking data indicated that the number of switches in the SCM group was the lowest among the three groups, with a marginally significant difference from the DCM-FD group for both low- and high-complexity wiring tasks during the laying phase. Additionally, the total fixation time of the three groups showed a significant difference for low- and high-complexity tasks with a large effect size; the SCM group exhibited the shortest total fixation time across all tasks. No significant differences in the number of assembly errors and users’ perceived workload were observed among the three groups. These findings can serve as a reference for guiding the visual style design in AR-assisted wiring systems for training novice operators in human-centric Industry 5.0 and achieving a decrease in overall workload and improved task performance. Full article

Waste wire harnesses composed of thin copper wires coated with polyvinyl chloride (PVC) are difficult to recycle due to hydrogen chloride (HCl) emission during conventional thermal treatment. In this study, copper recovery from waste wire harnesses was investigated using alkali hydroxide-assisted pyrolysis with sodium hydroxide (NaOH) or potassium hydroxide (KOH) under an inert atmosphere. The coexistent heating with alkali hydroxides enabled the decomposition and carbonization of PVC while effectively capturing chlorine species, thereby suppressing HCl gas release. As a result, thin copper wires were successfully separated and recovered. The addition of alkali hydroxides significantly improved PVC gasification efficiency and copper–PVC separation compared with pyrolysis without alkali hydroxides. No notable differences were observed between NaOH and KOH in terms of chlorine capture or gaseous byproduct formation. These findings demonstrate a simple and effective method for recovering copper from waste wire harnesses without HCl emission. Full article

Designing low-voltage (LV) power distribution systems for mass-sensitive electric vehicles involves several unresolved technical challenges, including parasitic $I^{2}R$ losses, excessive mass of commercial off-the-shelf distribution units, and difficulties in isolating thermal phenomena during vehicle operation. In dynamic racing conditions, temperature measurements of LV components are strongly influenced by external heat sources such as traction batteries, motors, and inverters, complicating accurate assessment of conductor self-heating and distribution losses. This work presents a load-driven methodology for the specification, implementation, and validation of LV architectures, demonstrated using a Formula Student electric race car. The proposed approach combines harness current mapping, resistive loss modeling, and component-level topology optimization to support the development of lightweight and electrically robust systems. Within this framework, a mass-optimized programmable solid-state power distribution unit (PDU), an auxiliary battery system with a battery management system (BMS), and an optimized LV wiring harness were developed and experimentally validated through controlled subsystem tests and in-vehicle operation. The proposed methodology enabled reduction in PDU mass by 40–80% relative to commercially available solutions while maintaining programmable protection, integrated current sensing, and stable thermal operation under representative racing loads. This reduction was achieved through load-driven conductor sizing, application-specific protection threshold optimization, and elimination of redundant protection and interconnection hardware. The developed PDU achieved a mass of 155 g with measured channel resistances of 40–70 m$\mathsf{\Omega }$. The auxiliary battery pack exhibited an average internal resistance of 64.2 m$\mathsf{\Omega }$ at a total mass of 2190 g, while the optimized harness demonstrated resistivity in the range of 14.72–33.98 m$\mathsf{\Omega }$/m. Experimental validation confirmed stable operation below critical thermal limits under both nominal and off-nominal load conditions. The obtained results demonstrate that the proposed methodology enables measurable reductions in both system mass and resistive power losses through application-specific optimization of the LV architecture. However, the presented approach is primarily suited for motorsport and other highly mass-constrained applications, where reduced packaging volume, efficiency, and weight justify the increased design complexity and lower universality compared to commercial off-the-shelf solutions. Full article

Hand-drawn circuit diagrams remain common in education, maintenance, and early-stage design and are often photographed for storage, sharing, and reuse. Recovering electrically meaningful structure from such camera-acquired images is difficult because irregular strokes, wire discontinuities, crossings, symbol–text interference, and imaging artifacts can disrupt valid circuit topology. We therefore formulate the task as topology recovery with semantic completion rather than symbol recognition alone. To solve it, we propose a topology-consistent structural parsing framework that integrates multi-source visual perception, wire connected-component-guided connectivity reasoning, and explicit endpoint semantic recovery for direction-sensitive and multi-terminal components. On an independent benchmark of 1317 hand-drawn circuit diagrams, the proposed method achieves a 95.14% strict image-level end-to-end success rate. The recovered structures are further exported as Simulation Program with Integrated Circuit Emphasis (SPICE)-compatible netlists for downstream simulation and verification. These results support a practical vision-based image acquisition and processing workflow for converting camera-acquired hand-drawn circuit images into machine-readable and simulation-ready circuit representations. Full article

Manual assembly of multi-pin cable harnesses remains vulnerable to miswiring issues when conductors are visually indistinguishable. This paper presents an industrial case study of a quick-connect harness composed of two connectors (receptacle-type and pin-type) linked by 16 black conductors (2.5 mm² and 200 mm length), where the dominant failure mode is a two-wire swap that breaks correct pin-to-pin mapping and may cause downstream equipment damage. In the baseline state, end-of-line verification relies on visual inspection only (1 min/unit), resulting in an internal nonconformity rate of 4% (repairable). To achieve the operational goal of zero defects (zero escapes), we proposed and integrated an electronic pin-to-pin continuity and mapping fixture as a deterministic End-of-Line (EOL) quality-gate implementing poka-yoke logic (“no PASS—no shipment”) and enabling structured traceability records. Using a before–after workload model that includes a mandatory retest after rework, the fixture reduces test time to 0.33 min/unit. For a monthly volume of 1500 units, total quality workload (test + rework + retest) decreases from 31 h/month to 13.58 h/month, releasing 17.42 h/month. Global quality productivity increases from 48.39 units/h to 110.46 units/h (+128%). The proposed architecture couples deterministic electrical verification with data logging aligned to digital-thread and data-driven quality-management concepts to sustain continuous improvement and prevent customer escapes. Full article

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic inventory validation is still required to verify and correct records in the warehouse management system. Methods: This study examines the feasibility of passive ultra-high-frequency (UHF) RFID technology for automatic inventory management in a components warehouse. It also reviews relevant scientific literature on autonomous RFID signal measurement and Synthetic Aperture Radar (SAR)-based localization methods, which are subsequently adapted for inventory applications. An experimental setup is developed to characterize the reading field, hysteresis effects, and the influence of distance and tag orientation on detection performance. Results: The findings indicate that RFID-based automatic inventory is achievable with high accuracy and stability, especially when tag trajectories correspond to areas of high detection probability and antenna polarization is optimally configured. Conclusions: The proposed RFID-based system can be implemented with minimal hardware changes and low investment, thereby improving stock accuracy, traceability, and operational efficiency in automotive component logistics. Full article

This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in automotive power systems and vehicle wiring—such as conductor eccentricity and magnetic interference from adjacent cables—two key techniques are proposed. First, an eccentricity error compensation algorithm is developed, achieving a measurement accuracy of 97.07% under specific misalignment conditions. Second, an equivalent modeling method based on eccentricity principles is introduced to characterize interference fields in complex wiring environments, maintaining 94.31% accuracy in the presence of external disturbances. When the conductor is centered within the array, the average measurement accuracy reaches 99.05%. Experimental results demonstrate that the proposed sensor can reliably measure large currents from 0 to 210 A, making it highly suitable for applications in electric vehicles, high-voltage harness monitoring, power electronics, and intelligent transportation systems. Full article

Accurate collision detection in off-line robot simulation is essential for ensuring safety in modern manufacturing. However, current simulation environments often neglect flexible components such as wire harnesses, which are attached to articulated robots with irregular slack to accommodate motion. Because these components are rarely modeled in CAD, the absence of accurate 3D harness models leads to discrepancies between simulated and actual robot behavior, which sometimes result in physical interference or damage. This paper addresses this limitation by introducing a fully automated framework for extracting, segmenting, and reconstructing 3D wire-harness models directly from dense, partially occluded point clouds captured by terrestrial laser scanners. The key contribution lies in a motion-aware segmentation strategy that classifies harnesses into static and dynamic parts based on their physical attachment to robot links, enabling realistic motion simulation. To reconstruct complex geometries from incomplete data, we further propose a dual reconstruction scheme: an OBB-tree-based method for robust centerline recovery of unbranched cables and a Reeb-graph-based method for preserving topological consistency in branched structures. The experimental results on multiple industrial robots demonstrate that the proposed approach can generate high-fidelity 3D harness models suitable for collision detection and digital-twin simulation, even under severe data occlusions. These findings close a long-standing gap between geometric sensing and physics-based robot simulation in real factory environments. Full article

With the rapid development of the automotive industry, terminals—as critical components of wiring harnesses—play a pivotal role in ensuring the reliability and stability of signal transmission. At present, terminal crimping quality inspection (TCQI) primarily relies on manual visual examination, which suffers from low efficiency, high labor intensity, and susceptibility to missed detections. To address these challenges, this study proposes an improved YOLOv5-based model, TCQI-YOLOv5, designed to achieve efficient and accurate automatic detection of terminal crimping quality. In the feature extraction module, the model integrates the C2f structure, FasterNet module, and Efficient Multi-scale Attention (EMA) attention mechanism, enhancing its capability to identify small targets and subtle defects. Moreover, the SIOU loss function is employed to replace the traditional IOU, thereby improving the localization accuracy of predicted bounding boxes. Experimental results demonstrate that TCQI-YOLOv5 significantly improves recognition ccuracy for difficult-to-detect defects such as shallow insulation crimps, achieving a mean average precision (mAP) of 98.3%, outperforming comparative models. Furthermore, the detection speed meets the requirements of real-time industrial applications, indicating strong potential for practical deployment. Full article

In response to the growing demand for automation in aerospace harness manufacturing, this study proposes a digital twin-based intelligent monitoring system for robotic wiring operations. The system integrates a seven-degree-of-freedom robotic platform with an adaptive servo gripper and employs a five-dimensional digital twin framework to synchronize physical and virtual entities. Key innovations include a coordinated motion model for minimizing joint displacement, a particle-swarm-optimized backpropagation neural network (PSO-BPNN) for adaptive gripping based on wire characteristics, and a virtual–physical closed-loop interaction strategy covering the entire wiring process. Methodologically, the system enables motion planning, quality prediction, and remote monitoring through Unity3D visualization, SQL-driven data processing, and real-time mapping. The experimental results demonstrate that the system can stably and efficiently complete complex wiring tasks with 1:1 trajectory reproduction. Moreover, the PSO-BPNN model significantly reduces prediction error compared to standard BPNN methods. The results confirm the system’s capability to ensure precise wire placement, enhance operational efficiency, and reduce error risks. This work offers a practical and intelligent solution for aerospace harness production and shows strong potential for extension to multi-robot collaboration and full production line scheduling. Full article

Mass customisation in the automotive industry has exploded the number of wiring harness variants that must be assembled, tested and repaired on the shop floor. Existing CAD or schematic formats are too heavy and too coarse-grained to drive in-line, per-VIN validation, while supplier documentation is heterogeneous and often incomplete. This paper presents a pin-centric, two-tier graph model that converts raw harness tables into a machine-readable, wiring-aware digital twin suitable for real-time use in manufacturing plants. All physical and logical artefacts—pins, wires, connections, paths and circuits—are represented as nodes, and a dual-store persistence strategy separates attribute-rich JSON documents from a lightweight NetworkX property graph. The architecture supports dozens of vehicle models and engineering releases without duplicating data, and a decentralised validation pipeline enforces both object-level and contextual rules, reducing initial domain violations from eight to zero and eliminating fifty-two circuit errors in three iterations. The resulting platform graph is generated in 0.7 s and delivers 100% path-finding accuracy. Deployed at Ford’s Almussafes plant, the model already underpins launch-phase workload mitigation, interactive visualisation and early design error detection. Although currently implemented in Python 3.11 and lacking quantified production KPIs, the approach establishes a vendor-agnostic data standard and lays the groundwork for self-aware manufacturing: future work will embed real-time validators on the line, stream defect events back into the graph and couple the wiring layer with IoT frameworks for autonomous repair and optimisation. Full article

The design of aircraft electrical wiring interconnection systems (EWISs) is central to ensuring the safe and reliable operation of aircraft. The calculation of the temperature rise characteristics of aircraft wire harnesses is one of the key technologies in EWIS design, directly affecting the safety margin of the system. However, existing calculation methods generally face a bottleneck in the balance between speed and accuracy, failing to meet the requirements of actual engineering applications. In this paper, we conduct an in-depth study on this issue. Firstly, a finite element harness model is established to accurately obtain the convective heat transfer coefficients of wires and harnesses. Based on the analysis of the influencing factors of the thermal network model for a single wire, an improved thermal resistance hierarchical wire thermal network model is proposed. A structure consisting of series thermal resistance within layers and iterative parallel algorithms between layers is proposed to equivalently integrate and iteratively calculate the mutual thermal influence relationship between each layer of the harness, thereby constructing a hierarchical harness thermal network model. This model successfully achieves a significant improvement in calculation speed while effectively ensuring useable temperature rise results, providing an effective method for EWIS design. Full article

Under the transition towards electrification and intelligence in modern automotive industry, the health status of low-voltage wiring harnesses directly affects vehicle performance and safety. To address the challenge of predicting performance degradation caused by multi-physics coupling effects during wiring harness aging, this study proposes a CNN-BiLSTM-Attention hybrid neural network model. By capturing voltage, current, and temperature parameters during low-voltage system operation, the model combines CNN’s local feature extraction, BiLSTM’s temporal sequence analysis, and attention mechanisms to predict aging levels. Accelerated aging experiments were conducted to obtain wiring harnesses with different degradation levels from new to 720 h aged states, and a dedicated experimental platform was built for data collection and verification. The results show the system achieves a mean absolute error (MAE) of 0.02806, with 32.50% and 62.06% error reduction compared to LSTM and Random Forest models, respectively, demonstrating effective prediction performance. Full article