Supervisory System: A Platform for Sustainable Manufacturing

Sustainable manufacturing is defined, by the U.S. Department of Commerce, as the creation of manufactured products that use processes that are non-polluting, conserve energy and natural resources, and are economically sound and safe for employees, communities, and consumers. TechSolve Inc. (Cincinnati, OH) is currently working with the University of Cincinnati to develop a supervisory system (SS) for “next generation smart machine.” The SS will create an optimum manufacturing process plan based on predictive modeling technology. It will also monitor and control the manufacturing process through advanced sensor technology and integrated decision-making. As such, the SS provides an ideal platform for sustainable manufacturing by minimizing the usage of energy (through process optimization technology), materials (through first part correct technology), and other resources (through paperless communication and automation technology).
The SS is an intelligent and self-evolving system that will choose the optimum smart machine, or smart cell, to perform the job regardless of whether the machine is in-house or located at another location or supplier. It will orchestrate the manufacturing processes and equipment by integrating various manufacturing software tools and technologies (in terms of both communication and decision-making). The software and technologies installed, either on the machine tool or in the company’s network, will be “plug and play” utilizing a common language that all computers and machines will recognize regardless of platform. This will enable seamless communication of information and data across various levels in a network. Therefore, the SS will transform the current factory to a paperless factory through the usage of state of the art computer networking, internet security, and e-documentation.
SS for sustainable manufacturing is a high-risk high-reward research endeavor. The research involves large scale multi-objective multi-constraint optimization, real-time sensing and interpretation of sensor data, integration of data and knowledge, and standardized communication interface across a variety of software and hardware platforms. If successful, the research will deliver the following benefits:

- Intelligent systems for manufacturing processes and equipment that delivers to the standard of “First Part Correct”
- New levels of efficiency, reliability, and performance that will result in shorter lead times, reduced maintenance cost, and optimized efficiency in all steps of manufacturing
- Process optimization that will lead to lower input resources
- Low labor content
- Zero waste
- Instant access to the real-time data and tracking of the process which will improve quality.

Reference: A Platform for Sustainable Manufacturing, Technology white paper, Amit Deshpande, Dr. Sam Huang.

Data to Information: Can MTConnect Deliver the Promise?

Manufacturing industry is still lagging behind with respect to integration and standardization of various process monitoring and control systems. The manufacturing community across the globe has realized the vast potential of adopting standard which can facilitate compatibility, interoperability, scalability and plug-and-play functionality between various subsystems. In this paper we describe the protocol details, implementation and case studies for the emerging MTConnect standard. MTConnect is an open non-proprietary extensible XML-based standard which aims at enhancing interoperability between machine tools. MTConnect was implemented in the supervisory system communication level architecture as part of the smart machine platform initiative. The possible implications on manufacturing with its current protocol limitations are described in detail. Finally we conclude with the lessons learned and future direction of MTConnect development.

Reference: Data to Information: Can MTConnect Deliver the Promise?; 37th North American Manufacturing Research Conference (NAMRC 37), May 19-22, 2009, Greensville, SC.
Authors: Sri Atluru, Amit Deshpande