Search Results (1,597)

The increasing demand for lightweight and corrosion-resistant structures has accelerated the use of AA5083 aluminium alloy in automotive, aerospace, marine and transportation industries owing to its excellent corrosion resistance, weldability and favourable strength-to-weight ratio. However, the fabrication of complex AA5083 components remains challenging because of limited formability, localised thinning, fracture and springback associated with conventional forming processes. Robotic incremental sheet forming (RISF) has emerged as a promising dieless manufacturing technology capable of producing complex and customised components with reduced tooling requirements. Recent developments in assisted RISF, particularly heating-assisted and hydro-assisted approaches, have further enhanced process capability. The reviewed literature consistently demonstrates that heating-assisted RISF improves formability by reducing flow stress and fracture tendency, whereas hydro-assisted RISF provides superior thickness distribution, deformation stability and dimensional accuracy. Despite these advances, significant challenges remain, including the lack of standardised processing conditions, limited comparative studies between cold and assisted RISF, insufficient understanding of hydro-assisted RISF for AA5083, and the absence of comprehensive process–structure–performance correlations. This review critically summarises the principles of ISF, RISF and assisted RISF technologies, evaluates their technical developments, industrial potential and economic considerations, and identifies the major research gaps limiting industrial implementation. Future research should focus on standardised processing methodologies, predictive modelling, integrated process optimisation and comprehensive material characterisation to facilitate the wider adoption of assisted RISF for manufacturing advanced lightweight AA5083 components. Full article

The Industry 5.0 paradigm reframes sustainable operations management around human-centric, resilient and responsible production, yet the conditions under which digital and AI-enabled manufacturing translate into genuine worker well-being—particularly for women—remain under-investigated. This study analyses the determinants of women’s well-being at work in the Moroccan automotive industry, a sector that has become the country’s largest industrial exporter and a strategic laboratory for Industry 4.0-to-5.0 transitions. A systematic review was first conducted in Scopus (2015–2025) following PRISMA 2020 guidelines, yielding 54 eligible studies, of which 18 explicitly addressed automotive or Industry 4.0–5.0 contexts. Building on Job Demands–Resources theory and the human-centric tenets of Industry 5.0, a conceptual model articulated five antecedents—perceived supervisor support, job autonomy, work–life balance, technology-inclusive AI environment, and organisational justice—and a moderator, Industry 5.0 maturity. The model was tested via PLS-SEM (SmartPLS 4) on survey data from 412 women working in supplier and OEM plants across Tangier, Kénitra and Casablanca. Measurement quality was satisfactory (Cronbach’s α: 0.92–0.94; CR: 0.94–0.96; AVE: 0.81–0.85; HTMT < 0.63). All five antecedents significantly predicted well-being (β = 0.09–0.29; p ≤ 0.01), explaining 62% of its variance (Q² = 0.571). Industry 5.0 maturity amplified the effect of a technology-inclusive AI environment on well-being (β interaction = 0.120; p < 0.001). The findings support a contingent, human-centric view of smart manufacturing and provide actionable levers for sustainable, gender-inclusive operations management. Full article

The advanced manufacturing of metal micro-holes is of great significance in various fields of industrial production, including aerospace, automotive, electronics, and healthcare. New technologies are constantly emerging, including various multi-energy field manufacturing technologies, and the knowledge system is complex and intricate. The present article summarizes recent advancements in metal micro-hole manufacturing technologies, drawing parallels with existing laser processing and electrochemical processing technologies. The present systematic review has been conducted with the objective of providing a comprehensive overview of the latest methodologies. The present review paper is of particular significance in that it encompasses not only the fundamental principles and innovative process methods, but also the most recent research progress and current problems. Furthermore, a synopsis of the developmental trajectory of advanced sustainable manufacturing technology for micro-holes was furnished. Full article

In this study, a double-sheet detection system for automated metal stamping is developed and modeled. This study aims to address the issue of die damage caused by overlapping workpieces (double sheeting), a significant contributor to production line stoppages. The proposed solution involves a control system utilizing a programmable logic controller (PLC), combined with inductive proximity sensors installed on the die, for real-time processing of workpiece status. The system is designed to immediately halt machine operation upon detecting any abnormalities. The experimental results demonstrate that the developed system successfully reduced double-sheet incidents from one occurrence to zero and eliminated production downtime (reducing it from 4 days to zero), resulting in a total of 32,972.26 USD saved in potential damage costs per incident. Furthermore, the system reduced the production cycle time from 11 s to 9.5 s per piece, increasing the production capacity by 1240 pieces per day. The economic assessment indicates that an initial equipment investment of only 483.2 USD yielded a return on investment (ROI) of 6723.73%, a payback period of 5.35 days, a net present value (NPV) of 89,333 USD, and an internal rate of return (IRR) of approximately 6830%. These figures demonstrate a very high level of economic feasibility. Therefore, this system is a cost-effective, reliable, and practical approach to enhance productivity and sustainably support the Smart Factory concept in the metal stamping industry, aligning with the automotive parts manufacturing industry. Full article

Background: Reducing production time while making efficient use of resources is a key challenge in modern manufacturing, particularly in environments with high variability in specific components for the automotive industry. Methods: This paper presents a hybrid approach to production scheduling that combines linear programming (LP) principles with heuristic decision-making. A structured literature review is conducted to compare exact methods, heuristics, and metaheuristics in terms of their applicability and limitations. Based on this analysis, a hybrid scheduling method is proposed, where LP defines the objective function and constraints, while heuristic rules enable efficient assignment of operations to workstations under capacity limitations. The approach is validated through a case study involving over 900 product variants in an automotive part production system characterized by interchangeable workstations. The proposed heuristic algorithm was tested in terms of real company daily scheduling performance and compared with former scheduling performance. Results: The results show that the proposed approach achieves better solution quality with significantly lower computational effort, while also improving time utilization and production efficiency. Conclusions: The hybrid LP-heuristic approach provides a computationally efficient and practical tool for real-time production scheduling in high-variability manufacturing environments, effectively balancing solution quality and sub-minute execution speed under strict capacity constraints. Full article

The use of post-consumer scrap (PCS) in aluminium structural components is a challenging but important strategy for reducing carbon footprint in the automotive industry. However, introducing PCS increases uncertainty and compositional variation, which complicates property assurance in serial production. This paper evaluates a parallel hybrid modeling framework that combines a physics-based Kampmann–Wagner precipitation model (NaMo) with a machine learning (ML) model, using a distance-based, adaptive weighting coefficient pre-trained on earlier non-PCS observations. The hybrid is tested on a separate dataset from alloys that emulate plausible compositional deviations introduced by PCS. To probe robustness, training is repeated with randomized initialization to evaluate distributions of RMSE and $R^2$ for yield strength ($R_{p0.2}$) and ultimate tensile strength ($R_m$). The results show that the hybrid improves average performance relative to both constituent models and also reduces variation in accuracy compared to the ML model. The results support the role of adaptive weighting as a pragmatic safeguard against machine learning extrapolation under domain shift, while also exposing limitations related to coefficient calibration and feature choice in the distance metric. The findings are discussed in the context of decision support tools for recycling-oriented manufacturing and gradual onboarding of PCS chemistries into data-driven property models. Full article

Synthetic data generation is a potential solution for addressing limited data in manufacturing defect detection. In injection molding of automotive tubes, surface defects such as splay present as white or silver streaks in the tube’s texture, that are difficult to capture in sufficient quantity for training robust object detection models. This study proposes Fast-DG2GAN: a DG2GAN based, computationally optimized, generative style defect generator for manufacturing defect images. Fast-DG2GAN achieves a 56% reduction in training time relative to the original DG2GAN, completing training in 334.9 min compared to 768.0 min, while maintaining comparable image quality metrics with a best FID score of 132.83 and IS 1.45 ± 0.08. Key contributions to this DG2GAN variant include depth-wise separable convolutions, reduced residual blocks, and automatic mixed precision (AMP) training, to improve training time. Training stabilization techniques include perceptual loss, feature matching, and exponential moving average of weights (EMA). Legacy Generative Adversarial Network (GAN) architectures are benchmarked for feasibility and include WGAN, DCGAN, and FastGAN, for fine-grained defect image generation essential to downstream object detection. Metrics, such as Inception Score (IS) and Fréchet Inception Distance (FID) are used for quantitative performance evaluation. This study highlights the potential of GAN-generated datasets to augment real-world training for defect detection models. Full article

Background: Manufacturing supply chains must increasingly coordinate cost, environmental impact, and service continuity under demand uncertainty and limited capacity. Methods: This study develops a stochastic mixed-integer linear programming framework for carbon-aware production–distribution planning in an automotive supply chain. The model jointly optimizes production quantities, inventory levels, shipments, truck usage, and lost sales over a multi-period horizon. Demand uncertainty is represented through scenarios, while production- and transportation-related emissions are monetized using an internal carbon price. Lost-sales penalties capture service degradation when demand cannot be fulfilled by the focal plant, and a rolling-horizon analysis evaluates planning responsiveness as demand information is updated. The framework is applied to an industrially inspired, capacity-constrained automotive case with multiple products, production lines, destinations, and demand scenarios. Computational experiments assess carbon pricing, lost-sales penalties, demand volatility, deterministic versus stochastic planning, and rolling-horizon replanning. Results: Results show that carbon pricing mainly acts as an economic valuation mechanism under the studied fixed-structure configuration, whereas lost-sales penalties strongly influence service performance. Demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand rather than improved efficiency. Conclusions: The study provides a decision-support framework for evaluating cost–carbon–service trade-offs under stochastic demand while acknowledging single-plant and fixed-routing limitations. Full article

The transition from internal combustion engine vehicles to battery electric vehicles has changed the acoustic design problem in automotive engineering. The absence of combustion-related masking increases the perceptibility of tonal and narrowband sources, including gear whine, electric motor orders, inverter-related components, tire cavity resonances, auxiliary system noise, and lightweight-panel radiation. At the same time, mass-based acoustic treatments conflict with electric vehicle lightweighting, range, cost, and sustainability targets. Vibroacoustic metamaterials offer an alternative route by manipulating elastic and acoustic wave propagation through architected geometries, local resonances, periodicity, membranes, lattice architectures, and adaptive or topological wave-control mechanisms. This review examines vibroacoustic metamaterials for low-frequency electric vehicle noise, vibration, and harshness (EV NVH) from an engineering perspective. It covers mechanisms, EV-specific NVH problems, component applications, materials, manufacturing, modeling, validation, AI-assisted design, sustainability, and technology readiness. Particular emphasis is placed on order-targeted, path-oriented, manufacturable, and experimentally validated solutions for electric-drive (e-drive) housings, wheel arches, battery enclosures, body panels, covers, and auxiliary systems. The review concludes that vibroacoustic metamaterials are most promising when integrated into conventional NVH workflows through order analysis, transfer path ranking, robust resonator tuning, durability validation, and multi-objective design optimization. Full article

Integration of composites into the fabrication process of structural assemblies within the aerospace, automotive, marine or civil engineering industries represents a rational solution adopted by leading companies which are guided by the necessity for novel low-weight, high-strength, and high-stiffness materials. During the manufacturing process and throughout the service life, fiber-reinforced polymer structures are subjected to impact loading, either accidentally or as an inherent requirement of the operational cycle. Firstly, general aspects regarding impact loading and some parameters used for its characterization are briefly described. Recent progress regarding the influence of the stacking sequence, fiber type, and impactor geometry on the impact performance of Kevlar and glass fiber reinforced composite materials is emphasized. Additionally, the effects of environmental factors (such as temperature, UV radiation, or humidity) on the impact energy absorbed by polymers reinforced with each of the two types of synthetic fibers are presented. Finally, the importance of directing the researcher’s judgment towards improving the characteristics of materials subjected to impact, from a sustainable perspective, is motivated through the presentation of the impact behavior of polymer composites reinforced with Kevlar fibers or glass fibers hybridized with vegetable fibers. Full article

Designing products that feel simple and intuitive while remaining efficient to manufacture and meaningful across cultures remains a key challenge in contemporary product development. Although minimalist design has achieved commercial success, most methodologies fail to systematically connect aesthetic intentions with engineering, usability, and production realities. This study proposes a four-phase iterative framework—Framing, Translation, Materialisation, and Experience that integrates human-centred design, simplicity heuristics—including ‘SHE’ (Shrink, Hide, Embody) tactics, quantitative aesthetic measurement, Design for X methods, and cross-cultural considerations to operationalise minimalist principles such as formal reduction, seriality, and industrial materiality. An empirical case study applied the framework to three competing automotive interior concepts through image-based evaluation by 113 respondents. Results provided preliminary evidence that a balanced hybrid approach consistently outperformed both traditional button-heavy and extreme single-screen minimalist designs across measures of usability, sense of order, trust, and user recommendation. Findings confirm that effective minimalism does not merely remove elements but strategically redistributes complexity into interface logic, production systems, and material quality. The framework offers designers a structured yet flexible path to create manufacturable, user-validated, and culturally sensitive minimalist products. Full article

China is a major producer, consumer, and exporter of automobiles. As an important component of the national industrial system, the automotive industry is associated with strong economic support functions, notable industrial spillover effects, and significant technological externalities, and its core values constitute a powerful driving force in achieving the global Sustainable Development Goals. Accordingly, this paper establishes a complex network evolutionary game model that involves two types of automobile manufacturers (established and latecomer automakers) in a strategic interaction within an exogenous environment jointly shaped by the government and the consumer community. We conduct a numerical simulation analysis to explore the organic relationships between the core elements within the system and the long-term performance of the automotive industry. The main findings are as follows. First, in adopting sustainable business models (SBMs), latecomer automakers exhibit a “high-start, low-end” evolutionary trajectory, whereas established automakers follow a “low-start, high-end” convergence path. Second, regarding the characteristics of game rules, the proportion of automakers that adopt SBMs is positively correlated with a larger proportion of ESG consumer groups, stronger comprehensive production and consumption subsidy standards, a more favorable expected payoff, and stronger market advantages on the part of established automakers. Finally, regarding network-structure characteristics, the proportion of automakers that adopt SBMs is positively correlated with a moderate total number of automakers, a reasonable proportion of established automakers, and a higher edge-addition probability. Moreover, this proportion is nearly independent of the noise interference coefficient, thus indicating that the mathematical model constructed as part of this study exhibits strong anti-interference capability. Full article

Gigacasting is emerging as a disruptive manufacturing route for automotive body-in-white structures, especially for electric vehicles, by enabling large aluminium alloy components to replace assemblies traditionally produced from stamped and joined sheet-metal parts. This paper presents a technological assessment of aluminium gigacasting against conventional multi-material mix sheet-metal manufacturing. The comparison addresses product architecture, structural performance, manufacturability, factory organisation, cost, repairability, supply chain implications, and sustainability. Gigacasting offers benefits in part consolidation, reduced joining operations, shorter process chains, and potentially lower non-material manufacturing costs, making it attractive for high-volume, low-variant EV platforms and greenfield production. However, these advantages are counterbalanced by challenges, including high capital investment, limited die life, defect sensitivity, dimensional distortion, mechanical-property variation, and reduced repairability. Recent benchmark data also indicate that total part cost and production-phase CO₂ emissions may remain higher than conventional solutions when aluminium material cost, component mass, and aluminium carbon intensity are considered. Conventional sheet-metal architectures retain advantages in modularity, repairability, quality control, tooling flexibility, and lower-risk implementation in brownfield plants. The analysis concludes that gigacasting should not be regarded as a universal replacement for sheet-metal multi-material Body-in-White (BIW) manufacturing but as a platform-dependent technology whose success requires defect control, low-carbon aluminium supply, process-aware simulation and validation, and high and stable production volumes. Full article

In the complex landscape of Industry 5.0, traditional management systems for smart manufacturing struggle to harmonize high-speed production with the rapid integration of AI and digital technologies. Crucially, these legacy frameworks often fail to capture tacit human knowledge or ensure trustworthy AI and trusted sharing of sensitive industrial data. This paper proposes a novel Decision Support Framework (DSF) that addresses these challenges through a multi-layered approach. At its core, the framework utilizes Data Spaces to enable secure, sovereign data sharing, ensuring that organizations maintain control over their assets. To handle the inherent ambiguity of industrial data, the system employs fuzzy logic and DAG-based root-cause-oriented investigation to provide robust recommendations, helping users distinguish descriptive correlations from plausible structural dependencies that require expert validation. Furthermore, the framework integrates eXplainable AI (XAI) services and AI-driven visual analytics, transforming complex algorithmic outputs into transparent, intuitive insights. By synthesizing data sovereignty with interpretable machine intelligence, this framework empowers trusted data sharing and human-centric decision-making, providing an advanced platform for achieving operational excellence within the Industry 5.0 vision. The proposed DSF is validated in three different pilot cases with end-users to be a milk industry, an automotive supplier and a machine manufacturer. Full article

In response to rapid shifts toward electrification, digitalization, and sustainability in the automotive industry, this study developed a sustainability-oriented, evidence-based competency framework for automotive maintenance technicians. Traditional competency frameworks, often derived from manufacturer manuals or curricula, overlook tacit knowledge from real-world maintenance practices, leading to gaps in diagnostic effectiveness, service quality, and resource efficiency. To address this limitation, 8500 maintenance records from 67 service centers (2022–2025) were subjected to quantitative content analysis to identify preliminary competency indicators across five vehicle systems. A three-round Delphi survey involving 24 senior automotive experts was subsequently conducted to validate and prioritize these indicators on the basis of mean importance scores and coefficients of variation (≤0.20). The final framework comprised 39 competencies, such as diagnostic proficiency, electronic system integration, system-level troubleshooting, and technical documentation application. Beyond traditional mechanical skills, cross-system diagnostic capability and digital tool proficiency have become essential competencies for modern electric vehicles. By transforming tacit maintenance knowledge into measurable indicators, the developed framework can contribute to supporting workforce sustainability, enhancing repair accuracy, reducing unnecessary part replacement, and improving resource efficiency. It can also inform vocational education, industry certification, and human capital development aligned with Sustainable Development Goals 8, 9, and 12. Full article