1,720,991 research outputs found
Special issue on ‘new transdisciplinary practices for intelligent manufacturing for industry 4.0’
Full text linkTransdisciplinary Engineering (TE) is an emerging area of research able to evolve traditional engineering approaches by transcending the technical disciplines. It can be successfully applied in different fields, by combining natural sciences, applied sciences, social sciences, and humanities to achieve a higher level of comprehension and awareness of the context in which industrial products, processes, systems, and services will be implemented and experienced by users (Borsato et al. Citation2016). Research in TE also incorporates social science methodologies to acquire knowledge about users and context, and solve ill-defined, socially relevant problems. Based on recent evidence, it can be stated that numerous engineering problems can be characterised as ill-defined and socially relevant, too (Wognum et al. Citation2019)
Special issue on ‘transdisciplinary approaches to digital manufacturing for industry 4.0’
Full text linkThe concept of Industry 4.0 (I4.0) outlines the vision of a smart factory characterised by the complete networking of all production parts and processes, consisting of real-time control via cyber-physical systems, increased use of robots, intelligent and adaptable production systems, which should contribute to greater productivity through resource efficiency. The convergence of production and interaction, work and communication requires increasingly transdisciplinary competencies for creating a smart factory, which is economically successful and competitive. These competencies consist, among others, of divers expert knowledge, flexibility, and creativity for moving toward I4.0
Human-driven design-to-cost methodology for industrial cost optimization
Full text linkOver the years cost optimization has gained a strategic importance to realize competitive products. However, traditional approaches are no longer efficient in modern highly competitive industrial scenarios, where numerous factors have to be contemporarily considered and optimized. In order to be effective, design has to care about cost along all its phases. This paper presents a methodology that integrates Design-To-Cost (DTC), Design for Manufacturing and Assembly (DFMA), Human Factors (HF) and Feature-Based Costing (FBC) to include costs from the early conceptual design stages and properly drive the product design. Thanks to a structured knowledge base and a FBC approach, it predicts both manufacturing and assembly processes from the 3D geometrical models and estimate the global costs, more accurately than existing tools. The research demonstrates the method validity by an industrial case study focusing on cost optimization of packaging machines. Thanks to the proposed method, the main design inefficiencies are easily identified from the early design stages and optimization actions are taken in advanced, in respect to traditional design process. Such actions allowed reducing total industrial costs of 20%, improving machine assemblability and human ergonomics due to structure simplification, part number reduction, and production processes modification, and reducing the time spent for cost estimation (until -60%)
Models of impact for sustainable manufacturing
Full text linkDesign for Sustainability (D4S) and LifeCycle Assessment (LCA) methods usually focus on one single aspect of sustainability at a time (e.g., environmental issues, ergonomics or costs) and are usually applied when the industrial system is already created, so that only corrective actions can be taken. In this context, the present research highlights the need of predictive methods to design sustainable system, able to provide an early holistic assessment from the early conceptual stages, and defines a set of models of impact able to assess all aspects of sustainability (i.e., environmental, economic and social) by proper key performance indicators (KPIs) from the early design stages. An industrial case study is presented to show the application of the proposed models on industrial manufacturing systems and demonstrate their validity in estimating the global impact on sustainability, including also human factors
Design for sustainability in PSS: evidences of QFD-based method application
Full text linkNowadays companies are pushed to offer solutions with new functionalities, higher performances, lower environmental impact, lower cost, and high usability for final users. In this context, the concept of Product-Service System (PSS) represents a valid way from manufacturing firms to evolve their market proposition, reduce impacts of their processes, and satisfy the customers’ needs. However, the design of PSS is still difficult, due to the lack of structured methodologies and evidences of the benefits connected with their adoption. The research adopts a systematic QFD-based methodology and demonstrates its validity to develop high sustainability PSS solutions. The case study focuses on the definition of a new PSS for green roofs: two groups of students, using respectively traditional methods and the proposed QFD-based methodology, were involved. The two PSSs conceived were evaluated in terms of outputs supporting the design phases and sustainability impacts. The case study results demonstrated how the adoption of a systematic method allows developing more business-oriented and more sustainable PSS in respect to traditional methods
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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