Tristan Schlotthauer, Jan Nitsche and Peter Middendorf
During post-processing of stereolithography photopolymers, the limited penetration depth of ultraviolet (UV) light can lead to inhomogeneous cross-linking. This is a major problem…
Abstract
Purpose
During post-processing of stereolithography photopolymers, the limited penetration depth of ultraviolet (UV) light can lead to inhomogeneous cross-linking. This is a major problem in part design for industrial applications as this creates uncertainty regarding the mechanical load capacity. Therefore, this paper aims to present an experimental method to measure the post-curing depth in stereolithography photopolymers.
Design/methodology/approach
Printed specimens made from urethane acrylate photopolymers are placed in a protective housing and are exposed on one side to UV light during post-processing. A depth profile of the hardness according to ASTM D2240 Shore D is determined alongside the specimens. UVA,-B and -C spectra are investigated and the dependence on exposure dose and pigmentation is studied. The results are directly linked to the mechanical properties via tensile tests and validated on an automotive trim part.
Findings
Exposure with a 405 nm light-emitting diode provides the deepest homogenous post-curing depth of 10.5 mm, which depends on the overall exposure dose and pigmentation. If the initially transparent photopolymer is colored with black pigments, post-curing depth is significantly reduced and no homogenous post-curing can be achieved. To obtain comparable mechanical properties by tensile tests, complete cross-linking of the specimen cross-section has to be ensured.
Research limitations/implications
The spatial resolution of the presented measurement method depends on the indenter size and sample hardness. As a result, the resolution of the used setup is limited in the area close to the edges of the specimen.
Originality/value
This paper shows that the spatially resolved hardness measurement provides more information on the post-curing influence than the evaluation of global mechanical properties. The presented method can be used to ensure homogenous cross-linking of stereolithography parts.
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Jan Svanberg, Tohid Ardeshiri, Isak Samsten, Peter Öhman, Presha E. Neidermeyer, Tarek Rana, Frank Maisano and Mats Danielson
The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted…
Abstract
Purpose
The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.
Design/methodology/approach
This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.
Findings
The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.
Practical implications
This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.
Social implications
The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.
Originality/value
To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.
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Michał R. Nowicki, Dominik Belter, Aleksander Kostusiak, Petr Cížek, Jan Faigl and Piotr Skrzypczyński
This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact…
Abstract
Purpose
This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics.
Design/methodology/approach
Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process.
Findings
The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory.
Research limitations/implications
The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research.
Practical implications
The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining.
Originality/value
The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.
Details
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This study aims to examine the volume of ehealth literacy documents during 2006–2022, and the nature of citation of ehealth documents by country, organizations, sources and…
Abstract
Purpose
This study aims to examine the volume of ehealth literacy documents during 2006–2022, and the nature of citation of ehealth documents by country, organizations, sources and authors.
Design/methodology/approach
The study adopted a bibliometric approach. Bibliographic data was collected on citation of ehealth documents by country, organizations, sources and authors from Scopus and mapped and visualized the citations using VosViewer.
Findings
A total of 1,176 documents were produced during 2006–2022, indicating a high rate of document production in this sub-discipline. Among the 102 countries that contributed documents on the subject, 58 qualified for the analysis. The USA had the highest number of cited documents on eHealth literacy, followed by Canada and Australia. The average publication year for the USA was 2018, with 348 publications and an average of 24.12 citations. Canada had a high average citation count of 44.69. Furthermore, the document examined citations by organizations.
Research limitations/implications
The research implications of the study suggest that eHealth literacy is an actively growing field of research, with a substantial impact on the academic community, and researchers should focus on collaboration with high-impact institutions and journals to increase the visibility and recognition of their work, while also paying attention to the need for more research representation from African countries.
Practical implications
The study’s findings indicate a high rate of document production and growing interest in eHealth literacy research, with the USA leading in the number of cited documents followed by Canada, while Canadian eHealth literacy research receives relatively higher citation rates on average than the USA.
Originality/value
The study’s originality lies in its examination of citation patterns and global contributions to eHealth literacy literature, offering valuable insights for researchers. It identifies key authors, high-impact journals and institutions, providing valuable guidance for collaboration. The research highlights a growing interest in eHealth literacy, underscoring its potential impact on public health and digital health interventions.