1,720,960 research outputs found
Experts’ and novices’ views on the use of additive manufacturing for the fabrication of parts based on their geometry
Full text linkAlthough complex designs are acknowledged to be more suitable for fabrication with additive manufacturing, there is no formalized definition of what makes a geometry sufficiently complex and accordingly appropriate for additive manufacturing. This lack of a standardized definition represents a challenge for engineers and designers. In this context. the objective of this study is to evaluate the role of part geometry in manufacturing decisions and to understand the criteria influencing the selection of a manufacturing process. This research used semi-structured interviews with 11 experts and a survey with 37 novices to gather data. Through ten questions, participants were requested to evaluate ten shapes of parts without further information and speculate on their suitability for additive manufacturing. It emerged that some of the experts stressed batch volume, material, part size, mechanical properties, cost, and material waste as fundamental criteria for selecting a manufacturing process, while novices did not consider material waste and cost as critical aspects. Part geometry was overall given secondary importance unless the part included specific features such as thin walls, lattice structures, and optimized topologies, where the selection leant towards additive manufacturing for both experts and novices. The latter preferred additive manufacturing (70% of the answers) more frequently than the former (54%). Overall, this study highlights the differences in decision-making criteria between experience levels and underlines the need for a formalized framework to evaluate geometric suitability for AM
Reasons Behind Selecting a Manufacturing Process: A Semi-structured Interview Study
No full textAdditive Manufacturing (AM) is still considered an emerging technology, and it has proven suitable for the fabrication of complex geometries. Selecting AM to fabricate a part involves investigating the economic, environmental, and mechanical aspects of production processes. However, the role of part geometry is often neglected. Even if it is established that AM can be used to manufacture parts with complex shapes, such AM-oriented complexity has not been formally defined. Hence, this paper investigates the role of part geometry in manufacturing and the reasons behind selecting a manufacturing process. A semi-structured interview study was conducted with ten manufacturing experts. Then, the interview data was evaluated using Latent Semantic Analysis. Six questions were used in the analysis to address the objectives of the paper. Most of the responders tended to describe the manufacturing processes instead of describing the presented geometry when directly asked. The interview revealed that experts consider the material, use, geometry, part size, batch number, machine availability, mechanical properties, production cost, and part quality as fundamental criteria to select a manufacturing process. Based on the interview results, the part geometry remains of secondary importance unless the design includes topology optimization, lattice structures, or organic shapes
HICA: A MATLAB-based hierarchical image clustering algorithm for classifying parts suitable for additive and traditional manufacturing technologies
Full text linkThis paper presents an image clustering algorithm that classifies parts to be fabricated using traditional and additive manufacturing (AM) technologies. The proposed algorithm is a MATLAB-based software tool that clusters 3D CAD models of parts considering their geometry only. The algorithm can classify image datasets, CAD datasets, and combined datasets that contain both images and CAD models. The software tool reduces the time and effort spent during process selection by offering a preselected set of parts that are more suitable for AM. The software tool is aimed at supporting decision-making for traditional manufacturing companies that consider expanding their production capability by introducing AM processes in their production facilities. The HICA software tool expands the scope of scientific applications in manufacturing process selection by providing an unsupervised approach that does not require data labelling. The tool is made available as a MATLAB function through a permanent link
An auto hierarchical clustering algorithm to distinguish geometries suitable for additive and traditional manufacturing technologies: Comparing humans and unsupervised learning
Full text linkThe development of additive manufacturing has made these technologies suitable for fabricating end products. This encourages companies to identify quickly parts in large databases for which switching from traditional manufacturing technologies to additive manufacturing is convenient. Typically, the manufacturing process selection is made by experts who weigh various parameters, but evidence suggests that intelligent systems could beneficially replace or aid this manual selection. One challenge in using manufacturing data for advanced analysis and machine learning is that it is usually unlabeled, and manual data labelling is expensive and time-consuming. This paper deals with the application of an enhanced unsupervised learning algorithm that automatically identifies parts suitable for additive manufacturing based on parts geometry as a preliminary step of process selection. One hundred randomly selected parts were evaluated by manufacturing experts through a survey and then clustered by the proposed algorithm. The comparison of the manual and algorithmic classifications, using unsupervised learning, regarding suitability for additive or traditional manufacturing is the main original contribution of this study. Overall, 78% convergence between most experts’ designations and the unsupervised learning algorithm is achieved. For those parts where expert opinions are substantially aligned, the algorithm showed a 90% convergence rate with human choices. These outcomes support the introduction of an intelligent system to perform a preliminary identification of suitable manufacturing processes based on part geometry, as it can be seen beneficial if compared with the time and cost spent when involving a pool of experts
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
Investigation of failures in rotational moulding using historical production dataset and machine learning
Full text linkRotational moulding (RM) is a versatile manufacturing process widely used for producing lightweight, seamless plastic components, but its potential is often constrained by challenges in optimizing production parameters for diverse product geometries and simultaneous batch production. This study addresses the pressing need for a data-driven approach to enhance RM efficiency and reduce defects under non-optimal process conditions. Leveraging historical production data from a medium-sized RM enterprise, an Ensemble Learning-based machine learning (ML) model was developed to predict failure probabilities across 390 product-process combinations. Input parameters are heating temperature, speed, mould volume, product mass. The model achieved an accuracy of 97.17%, identifying optimal parameter ranges for minimizing defects. The results revealed that deviations between machine and product-specific conditions, particularly in heating temperature and rotational speed, significantly increased failure probabilities. Products with intermediate sizes and masses were most susceptible to failures, while extreme values of mould volume occupancy showed a lower likelihood of failures. Notably, the study highlighted the critical importance of maintaining minimal delta heating temperature and speed ratio disparities to ensure product quality. This approach offers a robust framework for optimizing RM processes without costly sensorization, making it especially beneficial for small- and medium-sized enterprises
Patent-Based Prospective Life Cycle Assessment and Eco-Design of Lithium–Sulfur Batteries
Full text linkLithium–sulfur batteries (LSBs) are a promising emerging technology due to their high energy density, low-cost materials, and safety. However, their environmental sustainability is not yet well understood. This study conducted a prospective life cycle assessment (LCA) on two patented LSB models, using data from patents as the inventory: one with a standard sulfur cathode and another with a graphene–sulfur composite (GSC). The assessment is conducted for a functional unit of 1 Wh of produced electricity, adopting a cradle-to-gate system boundary and a prospective time horizon set to 2035. The LSB GSC model battery showed significantly better performance in terms of climate change and fossil depletion, with a 42% lower impact, mainly due to a reduction in the lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) content from 1205 mg Wh−1 to 250 mg Wh−1. However, the GSC model also had significant drawbacks, showing a 93% higher metal depletion and 49% higher water depletion than the standard sulfur battery. Building on an established patent-based prospective LCA approach, this work applies patent-derived quantitative inventories and patent-informed eco-design analysis to support environmentally informed design decisions for emerging LSB technologies prior to large-scale commercialization
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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