Embedded fog computing for high-frequency MTConnect data analytics

Lynn, Roby; Wescoat, Ethan; Han, Dongmin

doi:10.1016/j.mfglet.2017.11.002

Cited by 21 publications

(5 citation statements)

References 15 publications

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“…IoT devices were used for monitoring status, detecting problematic issues, and controlling their environments. Some researchers also reported monitoring systems for machine tools using IoT devices [26,54]. Moreover, the average cost of IoT devices is falling, and the recent trend towards free or open-source software allows for the development of a system that does not rely on proprietary and expensive commercial software [55,56].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs)

Kim

Jung

Choi

et al. 2019

Sensors

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In the new era of manufacturing with the Fourth Industrial Revolution, the smart factory is getting much attention as a solution for the factory of the future. Despite challenges in small and medium-sized enterprises (SMEs), such as short-term strategies and labor-intensive with limited resources, they have to improve productivity and stay competitive by adopting smart factory technologies. This study presents a novel monitoring approach for SMEs, KEM (keep an eye on your machine), and using a low-cost vision, such as a webcam and open-source technologies. Mainly, this idea focuses on collecting and processing operational data using cheaper and easy-to-use components. A prototype was tested with the typical 3-axis computer numerical control (CNC) milling machine. From the evaluation, availability of using a low-cost webcam and open-source technologies for monitoring of machine tools was confirmed. The results revealed that the proposed system is easy to integrate and can be conveniently applied to legacy machine tools on the shop floor without a significant change of equipment and cost barrier, which is less than $500 USD. These benefits could lead to a change of monitoring operations to reduce time in operation, energy consumption, and environmental impact for the sustainable production of SMEs.

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Section: Introductionmentioning

confidence: 99%

A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs)

Kim

Jung

Choi

et al. 2019

Sensors

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“…The MQTT protocol (developed by IBM) [65] facilitates the transfer of data to multiple clients. The MTConnect is an XML-based, read-only standard protocol that facilitates the gathering of manufacturing data [66]. The Representational State Transfer (RST) architecture with Hypertext Transfer Protocol (HTTP) comprise the system through which most applications that connect to the internet exchange data [67], such as smartphone apps and web browsers.…”

Section: Connectivity In Retrofittingmentioning

confidence: 99%

“…For manufacturing SMEs, the cost of implementing an Industry 4.0 project remains one of the major obstacles to adoption [62]. This is due to the lack of clear mechanisms and awareness of funding schemes [66], the limited availability of funds to support investments and the limited amount of data available to demonstrate the return on investment [69]. Financial constraints include the cost of new equipment and training staff and lack of demonstration of potential benefits and opportunities [70][71][72].…”

Section: Challenges In Retrofittingmentioning

confidence: 99%

“…The intrinsic disadvantages of SMEs, such as resource scarcity, skill limitation, competency of employees, and managing change, have long been recognised as barriers to adopting I 4.0. It is possible to alleviate some of these obstacles by addressing the internal knowledge gap, developing absorptive capacity, collaborating with academia and other partners, and preparing employees for digitalisation [66,72].…”

Section: Challenges In Retrofittingmentioning

confidence: 99%

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Industry 4.0: a systematic review of legacy manufacturing system digital retrofitting

2022

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Industry 4.0 technologies and digitalised processes are essential for implementing smart manufacturing within vertically and horizontally integrated production environments. These technologies offer new ways to generate revenue from data-driven services and enable predictive maintenance based on real-time data analytics. They also provide autonomous manufacturing scheduling and resource allocation facilitated by cloud computing technologies and the industrial Internet of Things (IoT). Although the fourth industrial revolution has been underway for more than a decade, the manufacturing sector is still grappling with the process of upgrading manufacturing systems and processes to Industry 4.0-conforming technologies and standards. Small and medium enterprises (SMEs) in particular, cannot always afford to replace their legacy systems with state-of-the-art machines but must look for financially viable alternatives. One such alternative is retrofitting, whereby old manufacturing systems are upgraded with sensors and IoT components to integrate them into a digital workflows across an enterprise. Unfortunately, to date, the scope and systematic process of legacy system retrofitting, and integration are not well understood and currently represent a large gap in the literature. In this article, the authors present an in-depth systematic review of case studies and available literature on legacy system retrofitting. A total of 32 papers met the selection criteria and were particularly relevant to the topic. Three digital retrofitting approaches are identified and compared. The results include insights common technologies used in retrofitting, hardware and software components typically required, and suitable communication protocols for establishing interoperability across the enterprise. These form an initial basis for a theoretical decision-making framework and associated retrofitting guide tool to be developed.

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“…For an open integration and interoperability, the computing system requires a predictive model associated with the manufacturing process that is being monitored, and real-time process sensor data. With the availability of Predictive Model Markup Language (PMML) technical standard (Grossman et al, 1999) and MTConnect (Vijayaraghavan et al, 2008), there exists standardized approaches for the representation of predictive models (Nannapaneni et al, 2018;Park et al, 2017) and transmission of real-time sensor data from the production floor equipment to the computing systems (Lynn et al, 2018). The sensor data will be used to update the process parameters, and the predictive model will be used to calculate the optimal control action.…”

Section: Monitoring and Control In A Cyber-physical Turning Processmentioning

confidence: 99%

Online monitoring and control of a cyber-physical manufacturing process under uncertainty

et al. 2020

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Recent technological advancements in computing, sensing and communication have led to the development of cyber-physical manufacturing processes, where a computing subsystem monitors the manufacturing process performance in real-time by analyzing sensor data and implements the necessary control to improve the product quality. This paper develops a predictive control framework where control actions are implemented after predicting the state of the manufacturing process or product quality at a future time using process models. In a cyber-physical manufacturing process, the product quality predictions may be affected by uncertainty sources from the computing subsystem (resource and communication uncertainty), manufacturing process (input uncertainty, process variability and modeling errors), and sensors (measurement uncertainty). In addition, due to the continuous interactions between the computing subsystem and the manufacturing process, these uncertainty sources may aggregate and compound over time. In some cases, some process parameters needed for model predictions may not be precisely known and may need to be derived from real time sensor data. This paper develops a dynamic Bayesian network approach, which enables the aggregation of multiple uncertainty sources, parameter estimation and robust prediction for online control. As the number of process parameters increase, their estimation using sensor data in real-time can be computationally expensive. To facilitate realtime analysis, variance-based global sensitivity analysis is used for dimension reduction. The proposed methodology of online monitoring and control under uncertainty, and dimension reduction, are illustrated for a cyber-physical turning process.

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Embedded fog computing for high-frequency MTConnect data analytics

Cited by 21 publications

References 15 publications

A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs)

A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs)

Industry 4.0: a systematic review of legacy manufacturing system digital retrofitting

Online monitoring and control of a cyber-physical manufacturing process under uncertainty

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