From Measurements to Knowledge - Online Quality Monitoring and Smart Manufacturing

Tamminen, Satu; Tiensuu, Henna; Ferreira, Eija; Helaakoski, Heli; Kyllönen, Vesa; Jokisaari, Juha; Puukko, Esa

doi:10.1007/978-3-319-95786-9_2

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…For online use, a tool that combines the process data with the models and presents the visualizations is needed. Tamminen et al [10] present a quality monitoring tool (QMT) that enables the online visualization of the production line with information about the predicted quality for each product. The methods presented in this paper can be implemented in the QMT tool.…”

Section: Model Training and Results For Roughness Predictionmentioning

confidence: 99%

“…This includes the handling of process data streams, the integration of prediction models and analytics to the system, and easy access to resulted decision support by end users. In a quality monitoring tool for the steel industry, the system architecture contains four levels: data acquisition, data storage, information analysis, and information delivery [10]. The system enables timely access to the needed data sources, data preprocessing and prediction model integration, and model analytics-based decision support with a webbased user interface.…”

Section: Practical Issues Related To Smart Decision Supportmentioning

confidence: 99%

“…With the help of AI methods, the knowledge behind the large amount of data is possible to determine, and with the extracted knowledge, the process outcomes can be predicted and relationships between process parameters can be utilized [8]. For example, Wang [9] and Tamminen et al [10] have demonstrated how data mining approaches have enabled intelligent tools to automatically extract useful information and knowledge from the industrial data. Hence, the workload and the cognitive load of the workers was decreased, and they were able to concentrate on improving the process when an alarm occurred, for example.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

et al. 2021

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This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement.

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Section: Model Training and Results For Roughness Predictionmentioning

confidence: 99%

Section: Practical Issues Related To Smart Decision Supportmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

et al. 2021

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“…Statistical Process Control is widely used to monitor processes now [13]. Although, developments in Industry 4.0 and improvements of machine tools made it traditional [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematic Model of the General Approach to Tolerance Control in Quality Assessment

Kupriyanov

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Dichev

et al. 2021

Lecture Notes in Mechanical Engineering

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A mathematical model for the numerical determination of the quality indicator value allowing one to obtain non-dimensional values in the range from minus infinity to 1 (the maximum usable value) is proposed. Unlike the traditional system of tolerances, in which the suitability can take two values -0 or 1, the proposed model allows you to obtain intermediate values of the suitability, depending on the proximity of the actual parameter to the desired value. The model uses the suitability function suggested in the paper, and the shape indicator drives it. The characteristic ranges of the shape indicator of the suitability function have been considered. Those of practical importance has been highlighted. In addition to the general dependence, exceptional cases (linear and parabolic suitability functions, which are mathematically simpler) are also given. It is shown how the use of the proposed model allows sorting parts into quality grades and introducing a technological margin of accuracy. The mathematical model is illustrated by the example of the dimensional suitability of an engineering part. The traditional system of dimensional tolerances is a particular case of the proposed generalized system, which is recommended for high-precision manufacturing.

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“…[1-4, 9, 10] The amount of process and quality data sources can be large in steel manufacturing. Often, the insufficient analytic skills or the lack of time prevent the effective use of this data, but intelligently designed presentation of the information derived from this data enables the support for the decision making [11]. The end user should be involved closely in the automated and smart decision support development.…”

Section: Introductionmentioning

confidence: 99%

A tool for finding inclusion clusters in steel SEM specimens

et al. 2020

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AbstractNon-metallic inclusions, especially large or clustered inclusions, in steel are usually harmful. Thus, the microscopic analysis of test specimens is an important part of the quality control. This steel purity analysis produces a large amount of individual inclusion information for each test specimen. The interpretation of the results is laborious and the comparison of larger product groups practically impossible. The purpose of this study was to develop an easy-to-use tool for automatic interpretation of the SEM analysis to differentiate clustered and large inclusions information from the manifold individual inclusion information. Because of the large variety of the potential users, the tool needs to be applicable for any steel grade and application, both for liquid and final product specimen, to analyse automatically steel specimen inclusions, especially inclusion clusters, based on the INCA Feature program produced data from SEM/EDS. The developed tool can be used to improve the controlling of the steel purity or for automatic production of new inclusion cluster features that can be utilised further in quality prediction models, for example.

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From Measurements to Knowledge - Online Quality Monitoring and Smart Manufacturing

Cited by 5 publications

References 14 publications

Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

Mathematic Model of the General Approach to Tolerance Control in Quality Assessment

A tool for finding inclusion clusters in steel SEM specimens

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