Journal Published Online: 09 December 2021
Volume 5, Issue 1

In-Process Dimension Monitoring System for Integration of Legacy Machine Tools into the Industry 4.0 Framework

CODEN: SSMSCY

Abstract

Manufacturers are implementing sensors, Internet of Things (IoT)-based automation, and communication devices on shop floors for connecting machine tools to a network-connected system for achieving “smart” functionality. The existing installation of legacy machines that offer either no or limited adaptability to these changes is a big obstacle to realizing the potential benefits of smart manufacturing. This research paper presents a sensor-based dimension monitoring system to capture and digitize component dimensions during machining operations. The capabilities are attained by developing an integrated framework consisting of sensors, data acquisition systems, feature extraction modules, and digital interfaces. The framework is implemented on legacy equipment such as lathes, milling, and drilling machines for component dimension monitoring while performing common operations. The proposed system functions at the edge level to improve man (operator)–machine–material interactions by displaying component dimensions and graphical visualization of the operations. The system also helps the operator recognize the resulting cutting forces and thereby achieve guided process control. The data generated at an edge level can be transmitted to the enterprise layer for performing tasks such as machine performance evaluation, man–machine utilization, process optimization, operator feedback, etc. The proposed framework provides a potential solution for integrating a vast base of the existing legacy machines into the Industry 4.0 framework.

Author Information

Dayam, Sunidhi
Department of Mechanical Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India
Desai, K. A.
Department of Mechanical Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India
Kuttolamadom, Mathew
Department of Engineering Technology and Industrial Distribution, 3367 Texas A&M University, College Station, TX, USA
Pages: 22
Price: Free
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Details
Stock #: SSMS20210021
ISSN: 2520-6478
DOI: 10.1520/SSMS20210021