Implementing the Transformation of Discrete Part Manufacturing Systems Into Smart Manufacturing Platforms

Author:

Botcha Bhaskar1,Wang Zimo1,Rajan Sudarshan1,Gautam Natarajan1,Bukkapatnam Satish T. S.1,Manthanwar Amit2,Scott Miller2,Schneider Dean2,Korambath Prakashan3

Affiliation:

1. Texas A&M University, College Station, TX

2. Texas A&M Energy Institute, College Station, TX

3. University of California Los Angeles, Los Angeles, CA

Abstract

Prior R&D efforts point to substantial performance enhancements and energy savings from adopting the Smart Manufacturing (SM) paradigm for process optimization and real-time quality assurance. Significant barriers and risks disincentivize the industry from investing in the adoption and training of SM component suites for discrete manufacturing applications. A diverse discrete part manufacturing enterprises, SM tools and platform vendors are yearning for a testbed reconfigurable to achieve three objectives of performance benchmarking, demonstration, and workforce training for a spectrum of their industrial scenarios and workflows. This paper presents the key ingredients towards the successful transformation of present machine tool and manufacturing environments into SM platform-integrated environments. The present implementation focuses on demonstration of the use of the Smart Manufacturing (SM) platform towards qualification of advanced materials and manufacturing technologies to meet an industry-specified functionality. This initial implementation uses Kepler workflow system residing as part of an Amazon Web Services environment to allow flexible workflows on multiple machines, each of which is integrated with an innovative sensor wrapper that integrates Commercial Off The Shelf (COTS) components from National Instruments (NI) to connect a legacy equipment to the SM platform. Here, an advanced analytics engine with modules customizable for both high-performance computing and shop floor environments was integrated into the commercial web service (from Amazon) to provide real-time monitoring and anomaly detection capability. This implementation indicates the potential of SM platform to achieve drastic reductions in the time and effort taken towards qualification of advanced materials and manufacturing technologies.

Publisher

American Society of Mechanical Engineers

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