Multivariate SPC methods for controlling manufacturing processes using predictive models – A case study in the automotive sector

Sanchez-Marquez, Rafael; Vivas, José Manuel Jabaloyes

doi:10.1016/j.compind.2020.103307

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…The research on the SPC algorithm has a long history and will not be repeated in this article. Drawing on Sanchez-Marquez, R's introduction of control charts and process capabilities in Statistical Process Control, this section implements a sample application at the platform application level [39].…”

Section: ) Data Visualization Analysis Interfacementioning

confidence: 99%

Blockchain-Based Process Quality Data Sharing Platform for Aviation Suppliers

Cao

Duan

et al. 2023

IEEE Access

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Given the challenge of manufacturing data silos and information credibility issues in traditional aviation suppliers' result-oriented management approach, this paper proposes a blockchain-based aviation supplier manufacturing process quality data-sharing platform. Firstly, the paper introduces the possibility of integrating manufacturing supply chain quality management with blockchain technology. Secondly, the quality and data sharing platform architecture of the production process of new aviation suppliers is presented based on quality state and island kinds of aviation suppliers. Then, a detailed method for the implementation of quality and data security sharing is proposed to support the sharing platform's real-time and orderly operation. Build critical technologies such as manufacturing quality data block packaging models, data storage security sharing, and supplier assessment models on this foundation. Finally, depending on data collection of supplier product production processes to shared application practices based on a specific aircraft industrial park under the supervision of the platform architecture and technology. The application platform integrates the data supply and request components, providing practical and intelligent sharing solutions for airlines' product quality data.

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Section: ) Data Visualization Analysis Interfacementioning

confidence: 99%

Blockchain-Based Process Quality Data Sharing Platform for Aviation Suppliers

Cao

Duan

et al. 2023

IEEE Access

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“…Ali et al [2] gave an overview of control charts to detect signi cant variations in the process to deliver high quality products. Each type of control chart has its strength and limitation in monitoring process target and process dispersion [3,4].…”

Section: Introductionmentioning

confidence: 99%

Geometric Brownian Motion-Based Time Series Modeling Methodology for Statistical Autocorrelated Process Control: Logarithmic Return Model

Lee

Liew

Chen

et al. 2022

International Journal of Mathematics and Mathematical Sciences

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Fitting a time series model to the process data before applying a control chart to the residuals is essential to fulfill the basic assumptions of statistical process control (SPC). Autoregressive integrated moving average (ARIMA) model has been one of the well-established time series modeling approaches that is extensively used for this purpose and is widely recognized for its accuracy and efficiency. Nevertheless, the research community commented that its iterative stages are laborious and time-consuming. In addressing this gap, a novel time series modeling technique with its conceptual assumptions of attributes that was derived from the geometric Brownian motion (GBM) law was developed in this study. It was termed as the logarithmic return (LR) model. Then, the model was employed and tested on a real-world autocorrelated data, whereby the results were assessed and benchmarked with the ARIMA model. The findings for LR model reported a mean average percentage error that ranged between 1.5851% and 3.3793% (less than 10%), which were as accurate as the ARIMA model. The running time (in second of CPU time) taken by the LR model was at least 96.2% faster than the ARIMA model. Interestingly, the corresponding multivariate control chart constructed from the LR model also portrayed a similar general conclusion as that of its counterpart. The LR model was obviously parsimonious and easier to compute and took a shorter running time than the ARIMA model. Therefore, it possessed the potential as an alternative time series modeling methodology for the ARIMA model in the procedures of SPC.

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“…Only compliance with all system principles in the process will allow to obtain products with appropriate functional properties. Qualitative supervision is particularly difficult for such processes, as it is based not on 100% control, but on complex analyses of statistical production control, as in other processes in the field of materials science [1][2][3][4][5][6][7][8][9]. Large steel products produced by complex open-die forging processes are widely used in industries with high quality demands on structural and mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of Rare Earth Metals Alloying on the Internal Quality of Industrially Produced Heavy Steel Forgings

Jonšta

Brožová

et al. 2021

Materials

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The paper presented the findings obtained by industrial research and experimental development on the use of rare earth metals (REMs) in the production of heavy steel ingots and their impact on the internal quality of the 42CrMo4 grade steel forging. REMs alloying was carried out after vacuuming the steel. A relatively large melting loss of cerium (about 50%) and its further decrease in casting due to reoxidation were observed. Refinement of structure and better mechanical properties of forged bar containing about 0.02 wt.% of Ce compared to that of the standard production were not achieved. The wind power shaft with content of about 0.06 wt.% of Ce showed high amount of REM inclusions, which were locally chained, and in some cases, initiated cracks. Four stoichiometrically different types of REM inclusions were detected in forgings, namely (La-Ce)2O2S + (La-Ce)O2 + SiO2 (minority); oxygen, phosphorus, arsenic, and antimony bound to lanthanum and cerium probably bonded with iron oxides La + Ce, MgO, Al2O3 a SiO2; (La-Ce)2O2S, FeO, SiO2, and CaO or CaS.

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Multivariate SPC methods for controlling manufacturing processes using predictive models – A case study in the automotive sector

Cited by 11 publications

References 24 publications

Blockchain-Based Process Quality Data Sharing Platform for Aviation Suppliers

Blockchain-Based Process Quality Data Sharing Platform for Aviation Suppliers

Geometric Brownian Motion-Based Time Series Modeling Methodology for Statistical Autocorrelated Process Control: Logarithmic Return Model

The Effect of Rare Earth Metals Alloying on the Internal Quality of Industrially Produced Heavy Steel Forgings

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