Search results

Researchers heavily investigated the use of industrial robots to enhance the quality of spray-painted surfaces. Despite its advantages, automating process is not always economically feasible. The manual process, on the other hand, is cheaper, but its quality is prone to the mental and physical conditions of the worker making it difficult to operate consistently. This research proposes a mathematical cost model that integrates human factors in determining optimal process settings.

Taguchi's robust design is used to investigate the effect of fatigue, stability of worker's hand and speed on paint consumption, surface quality, and processing time. A crossed array experimental design is deployed. Regression analysis is then used to model response variables and formulate cost model, followed by a multi-response optimization.

Results reveal that noise factors have a significant influence on painting quality, time, and cost of the painted surface. As a result, a noise management strategy should be implemented to reduce their impact and obtain better quality and productivity results. The cost model can be used to determine optimal setting for different applications by product and by industry.

Hardly any research considered the influence of human factors. Most focused on robot trajectory and its effect on paint uniformity. In proposed research, both cost and quality are integrated into a single objective. Quality is measured in terms of uniformity, smoothness, and surface defects. The interaction between trajectory and flow rate is investigated here for the first time. A unique approach integrating quality management, statistical analysis, and optimization is used.

This study aims to examine the research output on climate change, environmental risks and insurance from 1986 to 2020, thereby revealing the development of the literature through collaborative networks. The relationship between insurance, climate change and environmental threats has gained research attention. This study describes the interaction between insurance, climate change and environmental risk.

This study is a bibliometric analysis of the literature and assessed the current state of science. A total of 97 academic papers from top-level journals listed in the Scopus database are shortlisted.

The understanding of climate change, environmental risks and insurance is shaped and enhanced through the collaborative network maps of researchers. Their reach expands across different networks, core themes and streams, as these topics develop.

Data for this study were generated from English-written journal articles listed in the Scopus database only; subsequently, this study was representative of high-quality papers published in the areas of climate change, environmental risks and insurance.

The results of this study can be useful to academic researchers to aid their understanding of climate change, environmental risks and insurance research development, to identify the current context and to develop a future research agenda.

The findings of this study can improve the understanding of industry practitioners about climate change and global warming challenges, and how insurance can be used as a tool to address such challenges.

This study is a novel attempt. To the best of the authors’ knowledge, this is one of the first studies to better understand climate change, environmental risks and insurance as a research topic by examining its evolution in an academic context through visualization, coupling and bibliometric analysis. This bibliometric study is unique in reviewing climate change literature and providing a future research agenda. Using bibliometric data, this study addressed the technical aspects and the value it adds to actual practice. Bibliometric indicators quantitatively and qualitatively evaluate emerging disciplinary progress in this topic.