MTConnect-Based Kaizen for Machine Tool Processes

Lee, Byeong Eon; Michaloski, John L.; Proctor, Frederick M.; Venkatesh, Sid; Bengtsson, Nils

doi:10.1115/detc2010-28763

Cited by 17 publications

(4 citation statements)

References 6 publications

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“…DES systems use statistical distributions as input, so that statistical distribution fitting was applied to the DES event data. In previous work, the statistical distribution fitting was performed automatically on the event data by the DES statistical software [1]. In this implementation, some limited statistical software was embedded to do the statistical distribution fitting, but was aided byà priori understanding of the data and machine behavior.…”

Section: Figure 2: Machining State Model Overviewmentioning

confidence: 99%

“…The open MTConnect standard for shopfloor integration should help end-users collect raw data and assist in understanding the nuances of their process. In this regard, MTConnect allows for raw data acquisition that can be used for continuous process improvements [1]. Formalization of raw machine data acquisition into a DES-ready format is the goal of this paper.…”

Section: Introductionmentioning

confidence: 99%

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Push-Button Discrete Event Simulation for Analysis of Factory Floor Operations

Michaloski

Raverdy

Lee

et al. 2010

Volume 3: Design and Manufacturing, Parts a and B

Self Cite

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In manufacturing, Discrete Event Simulation (DES) can be effectively used to model production and provide sustainability analysis of equipment and system operation by measuring throughput, capacity, and bottlenecks. DES allows analysis under different scenarios and conditions that can then be used to forecast more optimal system performance. For discrete parts production, DES is rarely used because of the difficulty in attaining timely and accurate statistical modeling of the equipment and process operation. In this paper we look at the use of the plant floor interoperability standard, MTConnect, as a means to improve the access to machine tool data. Given data access, we develop a Finite State Model in order to streamline Machine Tool and DES integration. A case study of a prototype Capacity Planning system using DES as a modeling back-end will be described to help in understanding resource allocation of shop floor machines to a batch of aerospace parts. Direct machine tool statistical parameterization into a Web-based DES leads to “Push-button” automated simulations.

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Section: Figure 2: Machining State Model Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Push-Button Discrete Event Simulation for Analysis of Factory Floor Operations

Michaloski

Raverdy

Lee

et al. 2010

Volume 3: Design and Manufacturing, Parts a and B

Self Cite

View full text Add to dashboard Cite

show abstract

“…Apart from that, verifiable data can be collected for further data-mining. The protocol proposed by [10] developed by MTConnect® and Open Platform Communications (OPC) can help users record controller signals. Big data refers to the accumulation of these records, weather be signals, measures, historic data; all that combination is called -Big Data‖ [2].…”

Section: Big Data Helping Mass Customization In Industry 40mentioning

confidence: 99%

Self-organizing tool for smart design with predictive customer needs and wants to realize Industry 4.0

Saldivar

Goh

Chen

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Following the first three industrial revolutions, Industry 4.0 (i4) aims at realizing mass customization at a mass production cost. Currently, however, there is a lack of smart analytics tools for achieving such a goal. This paper investigates this issues and then develops a predictive analytics framework integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a self-organizing map (SOM) is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The selection of patterns from big data with SOM helps with clustering and with the selection of optimal attributes. A car customization case study shows that the SOM is able to assign new clusters when growing knowledge of customer needs and wants. The self-organizing tool offers a number of features suitable to smart design that is required in realizing Industry 4.0.

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“…In this work, it is proposed that intelligent analytics and CPS become one through the use of sensors, virtual platforms, simulation and collection of data for predict future events. A protocol presented in [15] developed by MTConnect® and Open Platform Communications (OPC) can help users record controller signals. This transforming agent consists of many components: visualization tools, predictive analytics, and an integrated platform.…”

Section: B Smart Information Achieving Predictionmentioning

confidence: 99%

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Saldivar

Goh

et al. 2016

2016 22nd International Conference on Automation and Computing (ICAC)

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Abstract-Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms.A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0.

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MTConnect-Based Kaizen for Machine Tool Processes

Cited by 17 publications

References 6 publications

Push-Button Discrete Event Simulation for Analysis of Factory Floor Operations

Push-Button Discrete Event Simulation for Analysis of Factory Floor Operations

Self-organizing tool for smart design with predictive customer needs and wants to realize Industry 4.0

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

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