To investigate the changes in hand morphology among young females, researchers employed 3D hand scanning to perform anthropometric measurement of 111 Chinese young women (20-26 years), enabling hand morphology classification for ergonomic applications. A total of 32 hand parts were measured and analyzed based on these models. The findings reveal that variables describing hand morphology are predominantly categorized into four types: finger width, finger circumference, finger length, and hand length. The typical indicators reflecting hand morphological characteristics include hand length, middle finger width, proximal circumference of the index finger, and ring finger length. Results revealed five distinct hand types: short/thin, short/wide, standard, long/thin, and long/wide. Compared to current national standards in China (GB/T 16252 - 1996), modern hand morphology showed significant increases in hand length (+ 3.3%) and metacarpal breadth (+ 8.3%). We propose a novel sizing system (5-size-5-fit) with 180/86 as the predominant type, optimized for ergonomic glove design. This study provides critical data references for the industrial design of hand appliances, while also offering potential implications for ergonomics and hand injury prevention.

Logic rewriting, as a critical and time-consuming task in synthesis, is widely employed in the integrated circuit (IC) design flow because it has the unique advantages of high optimization and independence from technology. However, existing solutions either employ locks to guarantee safe inter‑node parallelism (at the cost of limiting parallelism), or parallelize the sub‑procedures of rewriting for individual nodes without adequately considering logical sharing (at the cost of inevitably decreasing quality). In this paper, we present DACPara 2.0, a fast, enhanced, and easily extensible parallel framework for high-quality logic rewriting. Our key insight is that, due to their characteristics, different large-scale circuits should adopt different parallel mechanisms to process this task enabling significant improvements in parallelism and scalability. In this spirit, for those with complex logic, we propose a divide-and-conquer parallel approach to exploit intra-graph parallelism (i.e., parallelism among nodes and their sub-procedures within the same And-Inverter Graph (AIG)), which separates three substages and redesigns them using dynamic global information. In this process, the nodes in an AIG are executed bottom-up in a level-wise parallel fashion. On the other hand, in the case of heavily pipelined industrial designs where each pipeline stage is represented as different copies of the same design, we propose a conflict-free sub-AIGs parallel approach featuring an ingenious fanout-based partitioning strategy to exploit inter-graph parallelism (i.e., parallelism between independent sub-AIGs). Experiments show that DACPara 2.0 using 40 CPU physical cores achieves 52.86 × /42.25 × speedup in rewriting/total runtime compared to logic rewriting in ABC, and 3.27 × /2.61 × speedup over the state-of-the-art CPU parallel method, with extremely comparable quality of result. Also, for all large-scale circuits with complex logic, DACPara 2.0 can achieve a 0.4% improvement in quality compared to the state-of-the-art GPU accelerated method.

To support the increasing complexity of innovation, design, and performance evaluation in the maritime industry, a ship-specific computational fluid dynamics (CFD) software suite tailored to incompressible viscous flow is required. This study utilizes the MarineFlow marine fluid dynamics code to explore numerical simulation schemes for cylindrical flow problems across a broad range of Reynolds numbers (1–107) that are applicable to self-developed codes. Additionally, an analysis of the flow around a cylinder is conducted from the perspective of code developers. Various grid types and turbulence model schemes are employed to analyze and compare the drag coefficient, separation points, and pressure distribution characteristics of the cylinder. The results obtained from these simulations are then contrasted with those derived from commercial CFD software to assess their accuracy. Despite the presence of certain numerical artifacts, within the Reynolds number range of 1–105, the unstructured grids combined with the laminar flow models effectively capture experimental data. Further exploration of the transitional Reynolds number range (Re = 2×105–6×105) shows a consistent decreasing trend in the mean drag coefficient, although significant deviations from theoretical predictions are evident. From the perspective of code developers, this study aims to reveal the limitations of current computational schemes and code architecture in accurately capturing flow dynamics within the transitional Reynolds number range. This provides a crucial basis for future optimization of turbulence models and algorithmic improvements, which are essential for the continued development of self-developed CFD codes and their engineering applications.

With the progression of urban renewal, the functional transformation of numerous old industrial heritage buildings has imposed new demands on their indoor physical environments. This paper focuses on the adaptive renovation of thermal environments in old industrial buildings, using two case studies: Welding Workshop (Before Renovation) and the Cylinder Casting Workshop (After Renovation) of Hefei Motor Factory and Diesel Engine Factory. By integrating on-site thermal environment measurements and subjective thermal sensation questionnaires, we employs statistical regression methods to analyze the relationship between operative temperature and actual thermal sensation (MTS) and subjective thermal discomfort. The study identifies the acceptable temperature range and duration proportion in old industrial buildings, and further compares objective and subjective differences in human thermal comfort between summer and transitional seasons in the same workshop. Based on the acceptable duration proportion, a quantitative relationship between subjective sensations and operative temperature is established. These findings offer theoretical and empirical support for green renovation strategies of existing industrial buildings and design optimization of new constructions. Practical application This study provides empirical, decision-support evidence for the green renovation of industrial heritage. At its core, it establishes operative temperature as a critical design parameter and adopts the acceptable duration proportion of thermal comfort as a quantifiable target—thereby translating comfort needs into actionable design language. The data support a practical approach combining enhanced building envelope performance with flexible indoor environmental adjustments to balance heritage preservation and thermal comfort improvements. This research framework can be integrated into the design justification, scheme comparison, and post-occupancy evaluation processes of similar projects, offering a scientific and operational reference for enhancing environmental performance in the adaptive reuse of industrial heritage.

The role of glass in industrial product design "individually, fundamentally, complementarily".

Arabic calligraphy and its role in enriching the design aspect of industrial design products between development and strengthening identity

The rapid development of artificial intelligence (AI) is driving the transformation of industrial design towards sustainability. However, a systematic integration framework that can effectively clarify how AI promotes sustainable product design across multiple dimensions remains lacking. This systematic review comprises a detailed analysis of 113 core articles from the Scopus and Web of Science databases (covering 2015–2025), following PRISMA guidelines, examining publication trends, key journals, and citation impacts. From the perspectives of technology, systems, and institutions, this study systematically analyzes AI technologies and their possible application in promoting sustainable industrial design. Based on these findings, the challenges in applying AI in industrial design sustainability are discussed, such as technological controllability, system integration barriers, and policy lags. Key directions for future research are also identified. This review constructs a multi-dimensional framework to systematically explain the applications and mechanisms of AI in promoting sustainable industrial design. It also offers clear theoretical foundations and practical guidance for researchers, practitioners, and policymakers, facilitating the advancement of industrial design in a more sustainable and systematic direction.

Who designs when AI-augmented sketching generates form alternatives? The integration of generative AI into design practice raises critical questions about creative processes and skill development in design education. This study investigates how industrial design students experience AI-augmented sketching and form development, how they reconceptualize creative agency and authorship, and what pedagogical considerations emerge from AI integration. A qualitative phenomenological study was conducted with fourteen industrial design students who used outputs of Midjourney for ideation and Viscom for form refinement during a design project. Data were collected through semi-structured interviews and reflective writings, then analyzed using thematic analysis. Six interconnected themes emerged from the analysis. Findings reveal that students valued AI's external perspective for creative exploration and experienced efficiency gains alongside enhanced reflection. However, they encountered challenges when AI failed to interpret contextual meaning in their design intentions. Most significantly, students came to understand AI as a tool requiring human oversight rather than an autonomous designer. Based on these findings, a pedagogical framework is proposed that balances AI's generative capabilities with the preservation of foundational skill development and human judgment. This framework provides guidance for design educators navigating AI integration while supporting students' creative development and skill acquisition.

The testing of System on Chip (SoC) components in Very-Large-Scale Integrated Circuits (VLSI) presents significant challenges due to excessive power dissipation and data volume. The inherent complexity of VLSI circuits, which integrate numerous transistors on a single silicon substrate, increases fault likelihood, leading to large data volumes and longer test times. The conventional scan chain-based testing in Design-for-Testability (DFT), result in high switching activity and power consumption, especially during shift operations. Addressing low power dissipation during testing is essential for chip durability, performance, cost, and reliability. Otherwise, the chip may foresee power surge, voltage drops, hotspots, thermal stress, false timing failures and mismatches due to the reason that test power is always double than the Functional power. Thus, in this paper, a new architecture based on Linear-Feedback Shift-Register (LFSRs) driven architecture that is analogous to that of the traditional scan is proposed, to help diminish transition count in turn decreasing power usage in the testmode. The architecture employs Power gating and Automatic test pattern generation (ATPG) filling methods in the contribution of ensuring the design is in a low power state that provides a high level of test coverage at the lowest switching activity. The experimentation is tested on ISCAS89 benchmarking circuits, and on hyper convoluted industrial designs with Cadence Genus and Modus tools. Scalability between small benchmark circuits and large industrial designs is confirmed by performance analysis of a wide range of circuit complexities. Experimental data demonstrates that LFSR-based method showed a significant 1.34X decrease in the toggling activity, as well as impressive 4-10K decreasing pattern and an overall acceleration of the runtime of approximately 1-1.5 hours over the scan-based designs. Test coverage is enhanced 2.97-3.76 with the case of the static test, 6.03-7.51 with the case of the delay test and 0.41-0.59 with the case of scan tests. Test power recorded a decline of about 40-47 on industrial design exhibiting its substantial findings on intricate designs.

Under the high-mix, low-volume flexible manufacturing paradigm, industrial production design and operations encounter dual challenges. Although current intelligent scheduling methods enhance operational efficiency, their effectiveness remains constrained by inherent design deficiencies. To address these limitations, this paper proposes a causality-driven generative design framework, named CausalGID, which provides a new unified approach to physical layout optimisation and operational strategy generation. Initially, a multi-scale causal knowledge graph is developed, employing large language models to extract expert-rule triplets from domain literature and to perform cross-calibration with data-driven causal relationships, thus establishing a unified knowledge base for hierarchical design. Subsequently, a causality-guided Pareto-front layout generation method is proposed, which formulates production line design as a multi-objective optimisation task and uses structural causal knowledge to guide evolutionary algorithms in automatically generating non-dominated layout solutions, thereby enhancing global flexibility and production capacity limits. Finally, a knowledge-enhanced multi-agent reinforcement learning mechanism is designed, incorporating operational causal constraints and multi-objective reward functions to guide heterogeneous device agents towards efficient, robust, and reliable dynamic coordination. Compared with traditional baseline models such as D-GA, S-MOEA, CausalGID demonstrates an 18.5% increase in average throughput, achieves 89.1% takt matching, and reduces the idle resource rate from 15.3% to 4.2%.

The impact of minor manufacturing deviations in facesheet orifice geometries on the acoustic impedance of liners is studied. Using the lattice-Boltzmann method, simulations of a normal impedance tube (NIT) with plane acoustic waves at sound pressure levels of 130 and 145 dB and frequencies of 800, 1400, and 2000 Hz were performed. Experimental validation was conducted at the Federal University of Santa Catarina using a baseline geometry obtained via 3D scanning and characterized by rounded orifice edges. This geometry was modified to investigate the influence of various edge configurations: sharp edges, double chamfers, and single top chamfers. Results show that sharp-edged orifices increase acoustic resistance and absorption, while geometries with rounded or chamfered edges reduce resistance by up to 28% and lower the absorption coefficient. This is similar to what was found experimentally by performing NIT measurements over different parts of the liner sample. Velocity field analysis reveals that flow separation at the orifice edge is the primary mechanism driving impedance variation, independent of frequency or sound pressure level. These findings underscore the significant influence of small geometric imperfections, often introduced during manufacturing, on liner performance, highlighting the need to consider such variations in industrial design and quality assurance processes.

AI support in CAD is an active research field, generating an increasing number of publications. Recent surveys give a broad overview of this field, each structuring works differently, e.g., by application area. Given this magazine's and special issue's scope, we focus on research relevant to the industrial design process, excluding applications such as avatar or toy generation. Within this scope, we broaden the discussion beyond core computer graphics and artificial intelligence to include perspectives from design theory and human cognition, which clarifies CAD's role in designing and how Generative AI methods relate to designers' intelligence. We discuss CAD as part of the design process, examine what constitutes designers' intelligence, and how this relates to theories of human intelligence. We analyze current works in Generative AI for CAD and how they can be positioned in this landscape. Finally, we discuss limitations and challenges, and identify avenues for future research.

Unreal Engine, a real-time rendering platform developed by Epic Games, has expanded beyond gaming and is used in architecture, automotive design, and the movie industry. Due to its ability to generate high-fidelity visualizations, simulate real-world interactions, and support virtual prototyping, it also has the potential to be a valuable tool for industrial designers. This study investigates its application in product design through a self-reflective practice-based approach. As the sole participant, the author conducted iterative design experiments comparing conventional methods (sketching, CAD modeling, physical mock-ups) with Unreal Engine–based workflows in the design of handheld electronic gaming devices. The author’s reflections documented workflow efficiency, visualization quality, cost reduction, and learning experience. Findings suggest that Unreal Engine significantly improves iteration speed, offers highly realistic visualizations, reduces material costs by limiting the need for physical prototypes, and enhances design decision-making. The study demonstrates that Unreal Engine can transform industrial design practice by streamlining workflows, promoting sustainability, and fostering new competencies in digital prototyping.

In the ever-evolving landscape of industrial design, the interplay between symmetry and asymmetry serves as both a philosophical cornerstone and a practical compass [...]

Falls and trips are a leading cause of work-related traumatic brain injuries, yet the protective performance of industrial helmets in such scenarios remains poorly understood. This study assesses the effectiveness of different industrial helmet designs under impact conditions representative of falls and trips. Six industrial helmets with different designs were tested. Four were suspension-based models compliant with EN 397, including two versions of the same model, one with and one without the rotation reduction system, MIPS. Two additional helmets were foam-based, meeting both EN 397 and EN 12492 standards. Helmets were dropped onto angled anvils at different speeds and impact locations to simulate trips and falls. Tests were conducted on two surface types: P80 abrasive papers and roof shingles. The new EN 17950 headform was used. Helmet performance varied by design and impact condition. Foam-based helmets offered better protection against impacts than suspension-based helmets, which showed greater sensitivity to impact location. Front impacts near the rim at 5.5 m/s produced the highest severity, with peak linear accelerations exceeding 700 g for some suspension-based helmets, followed by rear impacts. In the single helmet model evaluated, MIPS reduced peak rotational acceleration. Finally, the influence of the surface type on peak head kinematics was borderline significant, with P80 papers producing larger peak kinematics. Helmet design has a key role in protection against trip and fall impacts, with foam-based helmets providing added benefits. These findings highlight the need for improvements in helmet safety standards and helmet designs to better prevent work-related brain injuries.

Right-to-repair (R2R) regulations effectively dismantle extractive industrial models, fostering a $ 217 B metabolic aftermarket economy that prioritizes sustained utility over new sales. This transformation reveals a significant flaw: traditional linear logistics emphasize depletion rather than recursion, whereas original equipment manufacturer (OEM) monopolies intentionally suppress diagnostic and component autonomy to limit product metabolism. Analysis of institutional morphologies and metabolic logistics across 12 countries indicates that leaders implementing Circular Sovereignty frameworks—redistributing control among diagnostic, component, and logistical domains—achieve 30–68% higher Total Value Retained (TVR) per asset. Mature recursive logistics networks significantly decreased virgin material inputs by 45% and converted waste liabilities into valuable streams. This research presents a comprehensive theory of Metabolic Aftermarkets and TVR calculus, offering policymakers and industrial designers practical frameworks for creating anti-fragile, value-retentive ecosystems based on open accessibility and endogenous resource flows. Keywords: Right-to-Repair, Circular Sovereignty, Metabolic Aftermarket, Value Retention, Recursive Logistics.

Sustainability in interior design is fast gaining importance in India as a consequence of environmental concerns, changing consumer values and motivating incentives imposed by various regulatory bodies. This research examines consumers' perceptions and industry practices of sustainable materials adoption in Indian interior design through a study of the Indian masses in a fast urbanising, environmentally stressed country. Sustainable materials: renewable, low-emission, recycled, non-toxic, local, etc., are fostering savings in embodied carbon, the quality of indoor air, and lifecycle-based design decisions. Despite the rise of environmental awareness among Indian consumers, the adoption of sustainable interior materials is inconsistent and fragmented. However, based on the present data there is limited verified research that systematically examines the bridge between sustainable material manufacturers, sustainable products and acceptance of these products by consumers in the Indian market. This study aims to evaluate willingness of the Indian consumer to adopt sustainable materials over the other alternatives available. A mixed- method approach is applied to the research, combining quantitative survey data from 450 urban consumers with qualitative information from 40 industry professionals, designers, suppliers and contractors. Findings reveal the high conceptual awareness of sustainability (86%) but low ability to identify certified sustainable materials (54%). Consumer preference is notably strong for low- VOC paints (83% due to lower VOCs); bamboo (78% due to low carbon emissions and high water absorbency); natural fiber textiles (70% due to durability, recyclability and biodegradability); however, industry application remains significantly below for bamboo (55%) and natural fibers (48% due to cost barriers and instability of supply chain, undefined certification). Low-VOC paints have the highest correlation between consumer demand and industry pickup for their availability because of clear regulations and availability in the market. Willingness-to-pay analysis indicates that 56% of consumers are willing to invest in premium sustainable products; education, environmental concern and experience were found to be important predictors. According to industry stakeholders, cost, inconsistent supply and insufficient client awareness continue to be barriers. The author of the paper concludes by stating that to address the perception-practice gap, it is important to have stronger certification frameworks, improved supply networks, policy incentives, and targeted educational interventions in order to mainstream sustainable materials in interior design in India.

This research project was developed in collaboration with the interactive center Mundo Fútbol, a company dedicated to educational entertainment through interactive sports exhibits. The main objective was the redesign of three of its most representative interactive exhibits: Compare Your Jump, Test Your Power, and Measure Your Speed. These exhibits, which are central to the visitor experience, showed significant deficiencies in material durability, ease of use, maintenance, and visual appeal due to prolonged use and a lack of technical updates for over a decade. The research was structured based on the Design Thinking methodology, which allowed for a deep understanding of the needs of both the users and the Mundo Fútbol staff responsible for the transportation, installation, and operation of the exhibitions. The process was carried out in five stages: empathize, define, ideate, prototype, and test, supported by complementary tools such as the Ishikawa diagram, SCAMPER, and brainstorming. These tools helped facilitate the identification of root problems and the generation of innovative solutions. Each exhibit was addressed individually, with a detailed analysis of its operation, structure, components, user interaction, and usage conditions. As part of the redesign, structural, functional, and aesthetic improvements were proposed and modeled using SolidWorks software, producing technical drawings, 3D models, and photorealistic renders of the new proposals. Additionally, structural simulations with static load analysis were conducted using materials such as ABS plastic and ASTM A-36 steel, in order to validate the strength, safety, and efficiency of each design. The results demonstrate significant improvements in ergonomics, structural resistance, ease of maintenance, and user experience, while also reducing setup times and transportation requirements. This research not only provides technical solutions tailored to Mundo Fútbol’s needs but also establishes a replicable methodology for the development of modern, sustainable, and user-centered interactive exhibits within the field of industrial design and engineering.

This review synthesizes research on industrial design education, highlighting technology integration and sustainability as key drivers. Studies explore the adoption of digital tools like VR and 3D modeling, balancing their potential for enhancing creativity with concerns over preserving traditional skills. Sustainability education research emphasizes practical, project-based learning to embed environmental principles, though implementation faces contextual challenges. Investigations into teaching methods reveal insights into student cognitive processes and the effectiveness of technological interventions. Furthermore, the field examines how policy and cultural factors shape curricula, alongside evolving career trends toward UX roles and interdisciplinary competencies. Methodologically diverse, the literature provides practical frameworks for educators. However, it remains fragmented, relying heavily on context-specific, small-scale studies, which limits generalization. Critical gaps include a lack of longitudinal data on skill retention, insufficient integration of emerging technologies like AI, and limited cross-cultural comparisons. Future research should prioritize longitudinal and comparative studies, deeper exploration of advanced technologies aligned with sustainability goals, and stronger industry collaboration to ensure curricula meet evolving professional demands.

This study introduces the Global Design Capabilities Ranking (GDCR), a novel index developed to benchmark national design capabilities across 153 countries. Responding to the limitations of earlier frameworks, such as limited coverage and reliance on non-replicable indicators, the GDCR leverages internationally available data on industrial design registrations, trademarks, and design awards. These outputs were aggregated using Principal Component Analysis and linked through regression to a replicated Design Competitiveness Ranking. The resulting model identifies two main dimensions: design production and recognition. Results validate the use of intellectual property as proxies of design capabilities, revealing regional patterns and providing actionable insights. Compared to previous benchmarks, this framework advances design policy research by offering a cost-effective, scalable tool to assess national design ecosystems. It supports international comparability and enables policymakers, especially in developing contexts, to better understand and promote design-driven innovation.