Design and Development of Semantic Ontology for Large Scale Manufacturing Industry with Help of Expert Miner
How to Cite?
Soumitra Singh, Partha Sarathi Chakraborty, S. Nallusamy, K. Balakannan, "Design and Development of Semantic Ontology for Large Scale Manufacturing Industry with Help of Expert Miner," International Journal of Engineering Trends and Technology, vol. 69, no. 5, pp. 186-189, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I5P225
Abstract
Process mining in industry encourages the professional and invention in industry raw data by machine learning and semantic technologies. Key problematic in trade businesses is drifting between professional and difficult to automate. In this situation, ontologies develop as a substantial technique for characterize manufacturing facts in an engine-understandable method. This information can then be applied by computerized problem resolving techniques to configure the regulator package that synchronizes and controls manufacturing schemes. Also, ontology shows a vital role in development of generating and handling the knowledge. This research illustrates the design and development of semantic knowledge in manufacturing industry using protege tool. This resource description framework is plug-in with java and python software. Integration of the manufacturing ontology produce more effective performance in car manufacturing company to find the car buyer patters effectively using data mining techniques.
Keywords
Semantic Mining, Ontology, Manufacturing Industry, Knowledge Base, Buyer Patterns, Clustering
Reference
[1] Zahid Usman, Robert Ian Marr Young, Nitishal Chungoora, Claire Palmer, Keith Case and Jenny Harding, A manufacturing core concepts ontology for product lifecycle interoperability, International IFIP Working Conference on Enterprise Interoperability. 76 (2011) 5-18.
[2] Usman, Z., A manufacturing foundation ontology for product life cycle interoperability, Enterprise Interoperability IV. (2010) 147-155.
[3] Viktor Zaletelj, Rok Vrabic, Elvis Hozdic, Peter Butala, A foundational ontology for the modelling of manufacturing systems, Advanced Engineering Informatics. 38 (2018) 129-141.
[4] Gunji Venkata Punna Rao, Nallusamy, S. and Rajaram Narayanan, M., Augmentation of production level using different lean approaches in medium scale manufacturing industries, International Journal of Mechanical Engineering and Technology. 8(12) (2017) 360-372.
[5] Wan, J., Chen, B., Imran, M., Tao, F., Li, D., Liu, C. and Ahmad, S., Towards dynamic resources management for IoT based manufacturing, IEEE Communications Magazine. 56(2) (2018) 52-59.
[6] Nilsson, J. and Sandin, F. Semantic interoperability in industry 4.0: Survey of recent developments and outlook, IEEE International Conference on Industrial Informatics. (2018) 127-132.
[7] Sanya, O.I. and Shehab, M.E., A framework for developing engineering design ontologies within the aerospace industry, International Journal of Production Research. 53(8) (2015) 2383-2409.
[8] Allen, R.H. and Sriram, R.D. The role of standards in innovation, Technological Forecasting and Social Change. 64(2-3) (2000) 171-181
[9] Inden, U., Mehandjiev, N., Monch, L. and Vrba, P., Towards an ontology for small series production, Proceedings of the 6th International Conference on HoloMAS. (2013) 128-139.
[10] Zhang, T., Gu, X. and He, E. Heterogeneous problems and elimination methods for modular ontology of product knowledge engineering, Proceedings of the 9th International Symposium on Linear Drives for Industry Applications. (2014).
[11] Peter Chhim, Ratna Babu Chinnam and Noureddin Sadawi, Product design and manufacturing process based ontology for manufacturing knowledge reuse, Journal of Intelligent Manufacturing. 30 (2019) 905-916.
[12] Soumitra Singh, Chakraborty, P.S., Nallusamy, S. and Balakannan, K. Process analysis on large scale manufacturing industry for performance and sustainable development, Journal of Green Engineering. 10(12) (2020) 12737-12752.
[13] Thangamani. M. and Thangaraj, P. Fuzzy ontology for distributed document clustering based on genetic algorithm, Applied Mathematics and Information Sciences. 7(4) (2013) 1563-1574.
[14] He, Y.H., Wang, L.B., He, Z.Z. and Xie, M., A fuzzy TOPSIS and rough set based approach for mechanism analysis of product infant failure, Engineering Applications of Artificial Intelligence. 47 (2016) 25-37.
[15] Suresh Kumar, N. and Thangamani, M., Multi-ontology based points of interests (MO-POIS) and parallel fuzzy clustering (PFC) algorithm for travel sequence recommendation with mobile communication on big social media, Wireless Personal Communications. 103(1) (2018) 1-8.
[16] Mohammed Alkahtani, Arijit De, Alok Choudhary and Jenny Harding, A decision support system based on ontology and data mining to improve design using warranty data, Computers and Industrial Engineering. 128 (2019) 1027-1039.
[17] Chang, W. L., Pang, L.M. and Tay, K.M., Application of self-organizing map to failure modes and effects analysis methodology, Neuro Computing. 249 (2017) 314-320.
[18] Yap Bee Wah, Nor Huwaina Ismail and Simon Fong, Predicting car purchase intent using data mining approach, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery, (2011) 1994-1998.
[19] Wollschlaeger, M., Sauter, T. and Jasperneite, J., The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0, IEEE Industrial Electronics Magazine. 11(1) (2017) 17-27.
[20] Wan, J., Tang, S., Li, D., Imran, M., Zhang, C., Liu, C. and Pang, Z., Reconfigurable smart factory for drug packing in healthcare industry 4.0, IEEE Transactions on Industrial Informatics. 15(1) (2019) 507-516.
[21] Eeva Jarvenpaa, Niko Siltala, Otto Hylli and Minna Lanz, The development of an ontology for describing the capabilities of manufacturing resources, Journal of Intelligent Manufacturing. 30 (2019) 959-978.