In order to obtain real-time insight about the manufacturing capabilities and capacities  of various manufacturing companies, there is a need for adopting advanced data-driven technologies based on Industrial Internet of Things (IIoT) [1][2-4].  IIOT can also be used for real-time monitoring and control of manufacturing assets for diagnostics and prognostics purposes. MTConnect [5] is an ANSI standard and is the leading international standard providing a semantic information model for observing data from manufacturing equipment [6]. The majority of CNC machine tools and controllers support the MTConnect information model and protocol as with most discrete manufacturing applications [7-9]. MTConnect also provides sufficient information to track and trace parts answering the question, where is a part in the process, and what machines are available with sufficient capabilities [10].

The objective if this project is to use MTConnect standard and technology to obtain operational data from the machine’s viewpoint. The collected data is used for data-driven generation of capability models of manufacturing assets and systems. Machine Learning techniques will be used for developing predictive models [31]. Also, ontological approaches will be used for enabling reasoning over the collected data [11, 12]. MTConnect is available for little to no cost on most discrete manufacturing equipment used by manufacturers. In this project, we will use the live data broadcasted by the CNC machines available at NIST and Digital Manufacturing Institute (MxD) machine shops.

Dr. Ameri leads this project.

[1] R. Sabbagh and F. Ameri, “A Framework Based on K-Means Clustering and Topic Modeling for Analyzing Unstructured Manufacturing Capability Data,” Journal of Computing and Information Science in Engineering, vol. 20, 2019.

[2] L. Chao, J. Pingyu, and C. Wei, “Manufacturing capability match and evaluation for outsourcing decision-making in one-of-a-kind production,” Piscataway, NJ, USA, 2014, pp. 575-80.

[3] R. Baker and P. Maropoulos, “Manufacturing capability measurement for cellular manufacturing systems,” International journal of production research, vol. 36, pp. 2511-2527, 1998.

[4] F. Ameri and R. Sabbagh, “Digital factories for capability modeling and visualization,” in IFIP International Conference on Advances in Production Management Systems, 2016, pp. 69-78.

[5] (2020). MTConnect. Available: https://www.mtconnect.org/

[6] H.-Y. Cheng, Y.-C. Su, and J.-P. Tsia, “Design of a machine tool information service system,” in 2010 International Symposium on Computer, Communication, Control and Automation (3CA), 2010, pp. 33-36.

[7] X. Tong, Q. Liu, S. Pi, and Y. Xiao, “Real-time machining data application and service based on IMT digital twin,” Journal of Intelligent Manufacturing, pp. 1-20, 2019.

[8] P. Oborski, “Developments in integration of advanced monitoring systems,” The International Journal of Advanced Manufacturing Technology, vol. 75, pp. 1613-1632, 2014.

[9] S. Chen, C. Yin, and X. Li, “Implementation of mtconnect in machine monitoring system for CNCs,” in 2017 5th International Conference on Enterprise Systems (ES), 2017, pp. 70-75.

[10] A. José Álvares, L. E. S. d. Oliveira, and J. C. E. Ferreira, “Development of a Cyber-Physical framework for monitoring and teleoperation of a CNC lathe based on MTconnect and OPC protocols,” International Journal of Computer Integrated Manufacturing, vol. 31, pp. 1049-1066, 2018.

[11] M. H. Karray, F. Ameri, M. Hodkiewicz, and T. Louge, “ROMAIN: Towards a BFO compliant reference ontology for industrial maintenance,” Journal of Applied Ontology, vol. 14, pp. 155-177, 2019.

[12] F. Ameri and D. Dutta, “An upper ontology for manufacturing service description,” in 2006 ASME International Design Engineering Technical Conferences and Computers and Information In Engineering Conference, DETC2006, September 10, 2006 – September 13, 2006, Philadelphia, PA, United states, 2006