https://www.syncsci.com/journal/RIMA/issue/feed Research on Intelligent Manufacturing and Assembly 2022-07-11T17:06:54+08:00 Snowy Wang snowy.wang@syncsci.com Open Journal Systems <p><strong>Research on Intelligent Manufacturing and Assembly</strong> (RIMA) (ISSN: pending) is an open access, continuously published, international, refereed journal publishing original peer-reviewed scholarly articles that are of general significance to technologies developed and studied for design, analysis, manufacturing and operation of Intelligent Manufacturing and Intelligent Equipment to provide a vital link between the research community and practitioners in industry.</p> <p>Topics of interest include, but are not limited to the following:<br>• Digital design and manufacturing<br>• Theory, method and system of intelligent design<br>• Intelligent processing<br>• Intelligent monitoring and control<br>• Modelling, operation, control, optimization and scheduling of manufacturing system<br>• Manufacturing system simulation and digital twin<br>• Industrial control and industrial internet of things<br>• Safety critical equipment and reliability assessment<br>• Intelligent equipment<br>• Intelligent robot</p> https://www.syncsci.com/journal/RIMA/article/view/RIMA.2022.01.002 Application of probabilistic methods to turbine engine disk life prediction and risk assessment 2022-07-11T17:06:54+08:00 Paul Copp editor@syncsci.com Ashley Whitney-Rawls editor@syncsci.com Jace Carter editor@syncsci.com Tarun Goswami tarun.goswami@wright.edu <p>Turbine engine disk life prediction and understanding the associated risk remains a significant challenge for today’s designer. Despite advances made in materials testing and characterization, as well as, the application of damage tolerance and linear elastic fracture mechanics modeling, there remains a void in properly assessing loading, geometry, and material design property variability. Add to this the application of advanced hybrid and composite material systems and the need to accurately deal with material variability is even greater. There still remain incidents of failure of critical components which were not properly accounted for by the existing analytical methods, testing, and inspections employed today. Application of probabilistic methods offers an effective and useful approach to modeling this variability while also providing a means by which to assess random variable sensitivity and risk assessment. Current research, as well as, applicable industry and government regulatory guidelines and publications were examined and will be presented. An assessment of the most effective tools, modeling methods, and predictive risk of failure assessments together with recommendations for future work will be discussed. The potential for probabilistic methods to provide a cost-effective way to manage fleet engine and component usage is presented, as well as, its ability to enhance the safe implementation of Retirement for Cause concepts to fleet management.</p> 2022-07-11T17:06:53+08:00 ##submission.copyrightStatement## https://www.syncsci.com/journal/RIMA/article/view/RIMA.2022.01.001 From the Editor-in-Chief of RIMA 2022-02-24T12:22:23+08:00 Matthew Chin Heng Chua mattchua@nus.edu.sg <p>The journal RIMA would be the bridge between researchers and industry practitioners. The key theme on smart factories encompasses many topics of interests; for example, digital design, novel control algorithms, digital twins, cobots (collaborative robots) and more. Furthermore, in today’s pandemic world which has significantly transformed the way traditional manufacturing industries operate, there is an even greater drive for a change in the manufacturing paradigm. Scientists and engineers of today should take bold steps in proposing and validating new workspace architecture that is reflective of the future. For instance, the development of digital twins or even virtual collaborative manufacturing are key drivers as we move into a future where both the virtual world and reality become seamless.</p> 2022-02-24T12:11:37+08:00 ##submission.copyrightStatement##