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MS-MECH-84 MSTHESIS ABSTRACT. Reconfigurable Manufacturing Systems (RMS) effectively respond to fluctuating market needs and customer demands for finished product. Diagnosability is a supporting characteristic of RMS that has a say in the quality of finished product. Cost and time taken for manufacturing are also considerably affected if proper diagnosability measures are not taken. Previous studies on Diagnosability of RMS have been studied from Axiomatic System Theory as such Design For Diagnosability (DFD). Nevertheless Diagnosability remains to be the least studied characteristic of RMS. With the availability of digitized data, Machine Learning approaches to advance manufacturing have proven to be considerably effective. A research gap existed for the application of Machine Learning techniques in improving the Diagnosability of RMS. A framework of Machine Learning has been proposed to address this gap. The working of the framework has been illustrated by two demonstrations from the available datasets, one in identifying proper signals in semi-conductor manufacturing to predict excursions, and the second in predicting machine failures due to a variety of factors. The framework is rendered in a concurrent-engineering fashion. The framework is tested against two available manufacturing datasets. Increase in Diagnosability will decrease the cost and time taken to production. Key Words: Reconfigurable Manufacturing Systems, Machine Learning, Artificial Intelligence, Preventive Maintenance, Intelligent Manufacturing
MS-CSE-15 MSTHESIS ABSTRACT. The use of web and mobile applications is growing very rapidly in the modern era. Due to high end demand of such applications, the software stakeholders want the applications to be available on both mobile and web. In software industry this requires more efforts to develop applications for both mobile and web. Consequently, more resources with different technology experts are needed for the development of multiplatform applications. The software engineers always look for time saving and robust methodology for good, quick and qualitative software development. Web and mobile applications usually composed of three layers i.e. application, business and data. Application layer deals with the UI related concepts that run on browser. On the other hand, business layer deals with the business logic that is usually implemented on server side. Finally, data layer deals the data access from database. We have performed the literature review in which we found that a methodology is needed where the software engineers can generate scaffolding code for the data and presentation layers considering the modern development technologies of hybrid (ionic) and web apps (angular). Normally in software industry, the system analysts design the class diagram and is handled over to the software developers. The developers start writing code in client side technology, server side technology and also generate database according to the class diagram. We have proposed the model-based methodology for the development of applications for both mobile and web applications, because, Model Driven Architecture (MDA) is renowned software design approach in software industry that make the software development very rapid and consistent. MDA facilitate the development of multiplatform applications from one UML diagram. Specifically, by applying the principle of "Run everywhere after develop once", we have designed a profile which have data types and stereotypes of model, class and property meta-class. We have generated the code from class diagram by Acceleo. Our methodology is validated by two case studies demonstrating that the idea is workable. Moreover, one empirical case study was given to 12 industry professional, for evaluating the saving of development effort using the proposed methodology. We found that the proposed approach reduced the amount of effort significantly. Key Words: Hybrid App, Web App, Model-Based Scaffolding, CRUD, MDA, Web service