Search Results (63)

Despite the recognition of Blockchain Technology’s disruptive potential, there is ongoing debate about its ontological and axiomatic foundations. This study develops a theoretical framework to explain the underline structural principles of blockchain technology through the lens of Arthur’s theory of technology, and the framework is developed through adopting Narrative Literature Review. By integrating conceptual analysis with a structural examination of Ethereum, this study reveals that blockchain technology is not a single invention but a composite technological system developed through recursive interactions among sub-technologies. The proposed framework identifies three interrelated structural patterns—the Combinatorial Pattern of Components elucidating blockchain technology’s structural ontology, the Capturing Pattern of Algorithms revealing the operational source of its innovation, and the Recursive Pattern of Technologies characterizing its inner logical structure of components—that together explain blockchain technology’s generative and evolving nature. The study extends Arthur’s theory by clarifying the “technology within technology” dynamic that underlies blockchain technology innovation. The Ethereum case confirms the framework’s applicability and generalizability, showing that blockchain systems, despite their diversity, share a consistent structural logic. Beyond its theoretical contribution, the framework offers practical guidance for sustainable technological innovation. It provides analytical support for designing blockchain-based applications’ architectures that enhance transparency, efficiency, and adaptability, contributing to the sustainable evolution of digital technologies. Full article

A bipolar neutrosophic set (BNS) is designed to handle uncertain information by capturing both supportive and opposing aspects of data. In this paper, the pattern classification method is studied based on the proposed Hamming–Chebyshev hybrid distance measure (HCHDM). First, the HCHDM of the bipolar neutrosophic sets is proposed that not only captures discrete differences, but also reflects the maximum dimensional deviation in a more complex environment. Then, the axiomatic definition of the distance measure is proved and some examples are given to show it can better discriminate between the differences of BNSs. Based on the distance measure, an algorithm to solve the pattern classification problem is given. The numerical examples show that the proposed distance measure method is effective in solving pattern classification problems. Full article

Evaluating the human–machine interface (HMI) of service robots remains challenging due to the complex integration of perceptual aesthetics and functional rationality. To address this, we propose a hybrid multidimensional HMI evaluation method that quantifies three key dimensions—layout aesthetics, color aesthetics, and functional layout rationality—by integrating visual cognition theory and axiomatic design (AD). The framework operationalizes five layout principles (balance, proportion, unity, regularity, density) and a four-component color model (color difference, distribution, harmony, and personality), complemented by a biologically grounded metric—visual perceptual intensity (VPI)—derived from cone cell response theory. Subjective weights from expert judgments (via analytic hierarchy process, AHP) and objective weights from the entropy weighting method (EWM) are fused within an AD-based information axiom framework to enable balanced, data-driven assessment. Applied to five candidate HMIs for a medical service robot (N = 15 participants), the method identified the design scheme x₃ as optimal when the balancing coefficient α ≥ 0.5 (reflecting greater emphasis on subjective judgment), whereas design scheme x₂ was preferred when α < 0.5 (prioritizing objective data). Given the modest sample size, correlation analysis revealed moderate-to-large—though not reaching conventional significance—between evaluation indicator scores and eye-tracking behavior: unity correlated with total fixation duration (Pearson_r = 0.682), and color harmony with first fixation duration (Pearson_r = 0.788), suggesting alignment between design attributes and visual attention patterns. These preliminary findings suggest that key design attributes may influence visual attention patterns, supporting the framework’s potential to link aesthetic and visual choices to measurable perceptual outcomes. Full article

Energy communities enable prosumers to jointly operate distributed energy resources and thereby generate economic benefits that exceed those achievable individually. A central challenge in their implementation is selecting a Cost Allocation Method (CAM) that distributes these benefits fairly among heterogeneous participants. Although numerous CAMs have been proposed, they are often evaluated under different assumptions, making direct comparison difficult. This paper develops a unified axiomatic framework for assessing CAMs in energy communities and applies it to eight representative methods classified in three families: simple rules, savings-based, and price-based. The framework is built around seven desirable properties capturing principles of fairness, environmental friendliness, and continuity. Our main contribution is a comparative table that positions all methods within a single evaluative space and reveals the structural trade-offs that arise across CAMs. The analysis shows that the Average-Price CAM satisfies the same axiomatic properties as the Shapley method while remaining computationally trivial, making it an attractive practical option. We also show that the Extreme-Price CAM is the only price-based method that ensures the property of Beneficial Group Participation (core stability); however, this method violates other properties related to environmental friendliness and continuity—trade-offs we prove to be unavoidable for price-based rules. Finally, we conjecture that the nucleolus satisfies all seven properties, although its computation is rarely feasible in practice. The proposed framework provides researchers and practitioners with a transparent foundation for selecting and designing cost allocation methods in emerging energy communities. Full article

Based on the problem of incomplete mining of Additive Manufacturing (AM) potential caused by the limitations of current Design for Additive Manufacturing (DFAM) methods, this paper proposes to integrate Additive Manufacturing and axiomatic design to obtain the global conceptual design method of products to be manufactured with AM. In response to the lower process dependence of AM technology compared to traditional processes, two integration measures of “influence region division” and “process domain forward” are proposed, and finally, the axiomatic design process for AM is obtained. Taking the assembly-free integrated design of mechanical fingers imitating dexterous hands as an example, the conceptual design method studied was validated. The application of innovative features such as flexible finger joints and lattice-filled finger joints shows that the design method proposed in this paper can deeply tap into the manufacturing potential of AM, achieve lightweight and integrated molding of products, which provides useful references for designers. Full article

High-overload testing equipment is a key platform for evaluating mechanical performance under extreme conditions, requiring specialized functional design to meet stringent operational demands. The current design process faces numerous challenges, including overreliance on designers’ experience, incomplete requirement analysis, and insufficient automation, resulting in suboptimal solutions. To address these issues, in this study, we propose an integrated method based on axiomatic design (AD) and the Theory of Inventive Problem Solving (TRIZ). This method systematically decomposes technical specifications, maps requirements to a structural framework, and resolves design conflicts using inventive principles. The method employs a comprehensive evaluation framework combining the analytic hierarchy process (AHP) and quality function deployment (QFD) to quantitatively assess candidate designs. It facilitates the development of efficient, standardized high-load testing equipment solutions, enhancing design reliability and innovation capabilities. Full article

Modern custom machine construction and automation projects face pressure to shorten innovation cycles, reduce durations, and manage growing system complexity. Traditional methods like Waterfall and V-Model have limits where end-to-end data traceability is vital throughout the product life cycle. This study introduces the implementation of a web application that incorporates a model-based design approach to assess its applicability and effectiveness in conceptual design scenarios. At the heart of this approach is the Case-Based Axiomatic Design Assistant (CADA), which utilizes Axiomatic Design principles to break down complex tasks into structured, analyzable sub-concepts. It also incorporates Case-Based Reasoning (CBR) to systematically store and reuse design knowledge. The effectiveness of the visual assistant was evaluated through expert-led assessments across different fields. The results revealed a significant reduction in design effort when utilising prior knowledge, thus validating both the efficiency of CADA as a model and the effectiveness of its implementation within a user-centric application, highlighting its collaborative features. The findings support this approach as a scalable solution for enhancing conceptual design quality, facilitating knowledge reuse, and promoting agile development. Full article

With the increase in the global aging population, the demand for elderly-friendly game products is growing rapidly. To address existing limitations, particularly in user demand extraction and design parameter setting, this study proposed a design framework integrating the BTM–AHP–AD–TOPSIS methods. The goal was to accurately identify the core needs of elderly users and translate them into effective design solutions. User reviews of elderly-friendly game products were collected from e-commerce platforms using Python 3.8-based web scraping. The Biterm Topic Model (BTM) was employed to extract user needs from review texts. These needs were prioritized using the Analytic Hierarchy Process (AHP) and translated into specific design parameters through Axiomatic Design (AD). Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to comprehensively evaluate multiple design schemes and select the optimal solution. The results demonstrate that the proposed design path offers a holistic method for progressing from need extraction to design evaluation. It effectively overcomes previous limitations, including inefficient need extraction, limited scope, unclear need weighting, and unreasonable design parameters. This method enhances user acceptance and satisfaction while establishing rigorous design processes and scientific evaluation standards, making it well suited for developing elderly-friendly products. Full article

Organizations worldwide are moving towards sustainability in the supply chains (SCs). Ergonomics management (EM) in SCs can contribute to their social sustainability (SS) by providing a fair, safe, and healthy environment. The literature recognizes the lack of an ergonomics management evaluation model (EMEM) for SCs contributing to SS. This research aims to propose an EMEM applicable to SCs. A continuous improvement approach with five constructs: Plan, Do, Check, Act, and Leadership and Worker participation (L&WP) was conducted, including nineteen domains, and the axiomatic design methodology was deployed. Design ranges (DRs) were defined by 34 experts from Latin America. System ranges (SRs) were assessed by self-assessments of EM practices to obtain the information content axiom in one case study of the Mexican salt industry. A new ergonomics management index for the supply chain (EMISC) and a corresponding scale were implemented. According to this scale, the index was found to be low, indicating a poor ergonomics management index (EMI) for the supplier link across the nineteen domains. The proposed EMEM effectively obtains an EMI of the supply chain (SC) by link and entirely. The model identifies opportunities to improve ergonomics practices for companies participating in sustainable supply chains (SSC). Full article

Pharmaceutical production typically focuses on individual drug types for each production line, which limits flexibility. However, the emergence of Industry 4.0 technologies presents new opportunities for more adaptable and customized manufacturing processes. Despite this promise, the development of innovative design techniques for pharmaceutical production equipment remains incomplete. Manufacturers encounter challenges due to rapid innovation cycles while adhering to stringent Good Manufacturing Practice (GMP) standards. Our research addresses this issue by introducing an information model that organizes the design, development, and testing of pharmaceutical manufacturing equipment. This model is based on an exploratory review of 176 articles concerning design principles in regulated industries and integrates concepts from Axiomatic Design, Quality by Design, Model-Based Systems Engineering, and the V-Model framework. Further refinement was achieved through insights from 10 industry experts. The resultant workflow-based information model can be implemented as software to enhance engineering and project management. This research offers a structured framework that enables pharmaceutical equipment manufacturers and users to collaboratively develop solutions in an iterative manner, effectively closing the gap between industry needs and systematic design methodologies. Full article

To enhance the adaptability and disassemblability of winter ice fishing safety auxiliary products, a modular design approach was introduced during the design process. Axiomatic design (AD) and design structure matrix (DSM) were employed as the theoretical guidance and methodological framework. In the design process, the “Z-mapping” method was used to reanalyze the product’s requirements, functions, and structure, progressively decomposing the overall function and constructing a corresponding design matrix. This approach converted initial user requirements into detailed functional specifications and design parameters. Geometric correlation was used as the evaluation criterion, with values assigned to the design matrix, leading to the development of a correlation matrix for the design parameters of the winter ice fishing safety auxiliary product. System clustering techniques were then applied to optimize the distribution of matrix values, allowing for the identification of functional module areas. Based on these results, a modular design scheme was proposed. The findings indicate that the Kano-AD-DSM-based design strategy significantly improved the disassemblability of the winter ice fishing safety auxiliary product, which is crucial for protecting the safety of ice fishers, reducing physical exertion, and enhancing the ice fishing experience. Moreover, the multi-module design allows the product to be flexibly configured and upgraded based on varying operational needs and personalized user requirements, significantly improving its adaptability and practicality. This research not only provides new theoretical insights for the innovative design of winter ice fishing safety auxiliary products but also offers valuable references for the modular design of similar products. Full article

The development of personalized drugs introduces new uncertainties and risks in production machinery design, which can be mitigated through structured workflows. As the commonly used V-Model approach has limitations in dealing with complex multi-domain problems, it is essential to address traceability and relationships between requirements and solutions in a regulated environment to ensure product quality. This study focuses on the conceptual design phase and develops a design methodology called the Case-based Axiomatic Design Assistant (CADA) to address this type of problem. It takes, as a starting point, Axiomatic Design (AD), due to its simplicity and graphical tools for quality evaluation, and Case-Based Reasoning (CBR), due to its capacity to integrate data structures and continuously improve. This combination is put into practice through a visual assistant that utilizes a knowledge graph to represent design elements comprehensively. This article describes the development, implementation, and testing process of CADA, which includes examples of the conceptual design for pharmaceutical manufacturing. The proposed CADA method facilitates systematic requirements analysis, structured reasoning, and solution evaluation, and overcomes the limitations of previous methodologies. It represents a novel approach with an intuitive workflow and advanced graphical capabilities, exemplified in the context of a conceptual design for pharmaceutical manufacturing. The inclusion of intrinsic data labeling capabilities and inference visualization enhances its relevance. Full article

Industry 4.0 has introduced a data-driven model of production and management of goods and services. This manufacturing paradigm leverages the potential of the Internet of Things (IoT), but finding the information necessary to drive manufacturing processes can be challenging. In this context, the authors propose an innovative approach based on axiomatic design to design RDF knowledge graphs from which to extract the information needed by decision makers. This approach derives from the possibility of providing RDF knowledge graphs with an equivalent matrix representation based on axiomatic design. It allows the selection of the most reliable data sources, thereby optimizing the knowledge graph construction process using matrix algebra, minimizing redundancy and improving the efficiency of query response. The goal of the presented methodology is to address the five critical aspects of Big Data (volume, velocity, variety, value, and veracity) by preordering the knowledge graph according to the information needs of business decision makers, thereby optimizing the use of the immense wealth of information made available by the Web in design. Full article

Manufacturing environments, characterized by dynamic changes and uncertainties, demand effective strategies to minimize disruptions. This study introduces an innovative approach that integrates engineering management principles with modular design to prioritize risk mitigation and enhance robustness in manufacturing processes. From a systems engineering perspective, all manufacturing activities are perceived as interconnected components within a unified system. Leveraging the Axiomatic Design (AD) theory and the Design Structure Matrix (DSM) method, the study modularizes manufacturing process architecture to effectively curb risk propagation and manage system complexity. This study identifies the most optimal design as a pivotal architectural configuration, significantly improving the structural robustness and stability of the System of Interest (SOI). Empirical evidence supports this design’s capability to reduce complexities, thereby enhancing robustness within the broader system architecture. Notably, the proposed approach results in a substantial reduction in complexity, with the most optimal design exhibiting an approximately 82.79 percent reduction in work volume compared to the original design. Our research underscores the critical relationship between manufacturing and engineering management. Effective collaboration between these domains optimizes resource allocation, decision-making processes, and overall organizational strategy, leading to improved production processes and increased efficiency. Importantly, the study demonstrates a significant enhancement in modularization, resulting in elevated overall robustness in manufacturing processes. This highlights the proactive involvement of engineering management in the design phase to address production challenges, ultimately optimizing system performance. Thus, this research contributes to both practical applications and academic discourse by offering a novel approach to enhancing the robustness in manufacturing processes. By integrating engineering management principles and modular design strategies, organizations can fortify their processes against disruptions and effectively adapt to evolving circumstances. Full article

The design of an Active Reflective Surface Control System (ARCS) is a complex engineering task involving multidimensional and multi-criteria constraints. This paper proposes a novel methodological approach for ARCS design and optimization by integrating Axiomatic Design (AD) and Multi-Criteria Decision Making (MCDM) techniques. Initially, a structured design plan is formulated within the axiomatic design framework. Subsequently, four MCDM methods—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Entropy Weight Method (EWM), Multi-Criteria Optimization and Compromise Solution (VIKOR), and the integrated TOPSIS–Grey Relational Analysis (GRA) approach—are used to evaluate and compare the alternative solutions. Additionally, fuzzy information axioms are used to calculate the total information content for each alternative to identify the optimal design. A case study is conducted, selecting the optimal actuator for a 5 m diameter scaled model of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), followed by digital control experiments on the chosen actuator. Based on the optimal design scheme, an ARCS prototype is constructed, which accelerates project completion and substantially reduces trial-and-error costs. Full article