Building of Internet of Things Model for Cyber-Physical Manufacturing Metrology Model (CPM3)

Majstorovic, Vidosav D.; Živković, Sanja; Djurdjanović, Dragan; Sabbagh, Ramin; Kvrgić, V.; Gligorijevic, Nemanja

doi:10.1016/j.procir.2019.03.215

Cited by 13 publications

(2 citation statements)

References 22 publications

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“…Automated quality control is very important aspect of a manufacturing process (Milo et al, 2015), and the measurement accuracy is particularly important in the new manufacturing era called Smart Manufacturing, Intelligent Factory or Industry 4.0 (Moroni & Petro, 2018). Metrology becomes part of the Cyber-Physical Manufacturing Systems (CPMS) and involves big data analytics (Majstorovic et al, 2018) as well as Internet of Things (Majstorovic et al, 2019). To handle metrological data in multistage manufacturing processes, intelligent systems based on neural networks are applied (Papananias et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of in-process control with non-contact air gauges

Rucki

2020

Rep. Mech. Eng.

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Dynamic properties of the air gauges performing in-process measurement are of the great importance because of dynamic error affecting the measurement results. The paper presents the analysis of the air gauges dynamics and some practical recommendation. The investigations proved the dependence of the time constants on the actually measured back-pressure. In practical solutions of in-process control, the air gauge must work in conditions of falling back-pressure since with the material removal dimensions of the machined workpiece go down. Thus, in the area of the smallest values of back-pressure within the measuring range, the time constant value is the largest. Worsening of the air gauge dynamic properties at the end stage of the machining must be considered when the dynamic characteristics of the projected air gauge are calculated.

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Section: Introductionmentioning

confidence: 99%

Dynamics of in-process control with non-contact air gauges

Rucki

2020

Rep. Mech. Eng.

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“…In order to remain competitive in today's volatile economy, manufacturing companies need to be provided with smart supply chain management system that enables them to manufacture products more efficiently, less expensively, and more quickly. Recent applications of Machine learning and Artificial Intelligence provided us with powerful tools and methods to make smarter decisions [1,2,3,4]. To react quickly to the sudden changes and be aware of the future demands and situations, companies need to focus more on previous information and trends.…”

Section: Introductionmentioning

confidence: 99%

Developing Smart Supply Chain Management Systems Using Google Trend’s Search Data: A Case Study

Sabbagh

Djurdjanović

2019

IFIP Advances in Information and Communication Technology

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Future manufacturing companies require smarter solutions to compete in the economy. Smart supply chain management systems are one of the most effective solutions. Use of previous information can help companies to predict the demands of the market and react in an agile manner to sudden changes. Google receives over 63,000 searches per second on any given day. This huge amount of data provides us with the opportunities to investigate researches in multiple subjects and extract useful information from the raw data that is available through Google Trend. In this research, we investigate the possible relationships between searches that are made in Google for two manufacturing capability terms, namely, Precision Machining (PM) and Electric Discharge Machining (EDM). Time-series oriented research is conducted on these two datasets in order to find the dynamics characteristics as well as interesting hidden relationships between these two search items to help us build a smarter supply chain management system. Two different methods namely ARMA and ARMAV models are be applied to fit a representative model to these datasets. The order of the both models are evaluated based on AIC statistic. In addition, multiple seasonal trends are detected in the datasets. Finally, Using ARMA model, we predict the datasets for one-step ahead in order to validate our models. Recognition of seasonalities and correlations between two datasets could lead to better prediction and smarter supply chain creation and management.

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