Modelling drawbeads with finite elements and verification

Drawbeads are commonly used in deep drawing processes to control the flow of the blank during the forming operation. In finite element simulations of deep drawing the drawbead geometries are seldom included because of the small radii; because of these small radii a very large number of elements is required in 3-D simulations. To cope with this problem, a 2-D analysis of the drawbead has been performed and the calculated restraining force will be applied in the near future in 3-D simulations with an equivalent drawbead element. Modelling drawbeads by only applying an additional restraining force is not satisfactory. During the flow of the material through a drawbead, the strain distribution changes and the material usually becomes thinner. These effects must be incorporated in the equivalent drawbead element.\ud \ud For the modelling of the drawbead a 2-D plane strain finite element model was developed. Several simulations were carried out to investigate the behaviour of the drawbead. Various geometries were investigated, the friction coefficient was varied and also the frictionless case was taken into account.\ud \ud To verify the model an experimental set-up was built. An extensive set of drawbead geometries was used. The results are compared with the finite element calculations and the similarity is very satisfactory

Introduction to Social and Psychological Perspectices in Collaboration Research Minitrack

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Accepting the Familiar: The Effect of Perceived Similarity with AI Agents on Intention to Use and the Mediating Effect of IT Identity

With the rise and integration of AI technologies within organizations, our understanding of the impact of this technology on individuals remains limited. Although the IS use literature provides important guidance for organization to increase employees’ willingness to work with new technology, the utilitarian view of prior IS use research limits its application considering the new evolving social interaction between humans and AI agents. We contribute to the IS use literature by implementing a social view to understand the impact of AI agents on an individual’s perception and behavior. By focusing on the main design dimensions of AI agents, we propose a framework that utilizes social psychology theories to explain the impact of those design dimensions on individuals. Specifically, we build on Similarity Attraction Theory to propose an AI similarity-continuance model that aims to explain how similarity with AI agents influence individuals’ IT identity and intention to continue working with it. Through an online brainstorming experiment, we found that similarity with AI agents indeed has a positive impact on IT identity and on the intention to continue working with the AI agent

Achieving Success in Community Crowdsourcing: Lessons from the Field

Community crowdsourcing is a relatively new phenomenon where local institutions, such as cities and neighborhoods, invite citizens to engage in a public discussion and solve problems that directly affect them. While community crowdsourcing has been around for over a decade, relatively little is known about what drives the success of these initiatives. In this exploratory study, we analyze field data from over 1,000 community crowdsourcing projects that were hosted on a professional community crowdsourcing platform. Our exploration reveals interesting insights into characteristics of community crowdsourcing projects that are associated with higher levels of user engagement. These insights allow us to speculate on guidelines to organize and execute community crowdsourcing initiatives