Data-Driven Causal Modeling of the Manufacturing System

Frumusanu, Gabriel-Radu; Afteni, Cezarina; Epureanu, Alexandru

doi:10.21278/tof.451020920

Cited by 5 publications

(17 citation statements)

References 18 publications

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“…1). In what concerns the choice of the most suitable arguments (job effect-variables), this can be done by instance based causal identification of the manufacturing system, [14], while the comparative assessment between two or more typical jobs can be realized after the values of their effect-variables, according to the method presented in [16].…”

Section: Holistic Optimization Methods -Hommentioning

confidence: 99%

“…Three actions are necessary in order to do data concatening, namely clustering, updating and homogenization, [14].…”

Section:  Data Concateningmentioning

confidence: 99%

“…The core idea in finding causal models is to look for relations between the variations of cause-and effect-variables (denote by p i respective q i ), instead to see each instance as event that illustrates the causal relation between these variables, [14]. Variables variations can be revealed by instances comparing.…”

Section:  Instances Comparingmentioning

confidence: 99%

“…The results of applying the first procedure is the cause-variables maximal cluster. Starting of this, based on the values of the specific characteristics that characterize the cause-variables in terms of their modeling capacity, a sub-cluster of the maximal cluster can be generated (simply called clusters), [14].…”

Section:  Variables Assessingmentioning

confidence: 99%

“…The optimal management of the manufacturing process and, implicitly, the making of optimal decisions can be done by applying a new optimization method, developed exclusively for application in the case of this type of process, namely the holistic optimization, [14]. Holistic optimization generally involves using as process model the history of its operation under similar conditions, in the form of a database.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Study on the Application of the Holistic Optimization Method of the Manufacturing Process in the Case of a Reduced Instances Database

Afteni

Frumusanu

2022

MATEC Web Conf.

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The optimal management of the manufacturing processes is achieved through a set of optimal decisions, which must be made for choosing the best way to follow, every time we find ourselves in a point from which several potential manufacturing paths start. A dedicated method, namely the Holistic Optimization Method has been already developed in this purpose, and validated in a number of studies based on artificial and real instances databases. In the current papers that approach the optimal management of the manufacturing processes, in order to estimate the consequences of a decision, are used known methods, such as: NN modeling, big data analysis, statistics, etc. In all these cases, the database size plays an essential role in terms of estimation quality. The present study aims to prove the feasibility of applying the Holistic Optimization Method when the decision-maker does not dispose of a consistent database. This can be a significant advantage relative to the other methods. The study is performed using an artificially generated instances database in the case of a turning process, and the results obtained are promising.

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Section: Holistic Optimization Methods -Hommentioning

confidence: 99%

“…Three actions are necessary in order to do data concatening, namely clustering, updating and homogenization, [14].…”

Section:  Data Concateningmentioning

confidence: 99%

Section:  Instances Comparingmentioning

confidence: 99%

Section:  Variables Assessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study on the Application of the Holistic Optimization Method of the Manufacturing Process in the Case of a Reduced Instances Database

Afteni

Frumusanu

2022

MATEC Web Conf.

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Method for holistic optimization of the manufacturing process numerically described as low-dimensional database

Chivu,

Afteni,

Frumusanu

2024

Mechanics & Industry

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The management of the production processes in an optimal manner involves the usage of knowledge about past jobs as reference for current decisions. During a manufacturing flow in every process step the process engineers could be in situations that request quick decisions based on comparison of different potential manufacturing paths. The Method for Holistic Optimization was developed in order to be used as support for decisions. The method was validated thru different studies. For the mentioned studies there were used artificial and real instances databases. The approach of the optimal management of the manufacturing processes was developed in the current study in order to estimate the consequences of a decision, are used known methods, such as: NN modeling, big data analysis, statistics, etc. In all these cases, the database size plays an essential role in terms of estimation quality. The main purpose of the study is to analyze and validate that the Method for Holistic Optimization is feasible to be used in case a decision-maker uses a reduced database. This can be a significant advantage compared with other methods. The study it is performed using an instance database which was artificially generated in case of a turning process. The obtained results are consistent and promising.

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Holistic management of next generation manufacturing system

Frumuşanu¹,

Afteni²,

Epureanu³

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The manufacturing system is a widely addressed subject. Many researchers have worked since a significant number of years in this field. Still, manufacturing system performance is capped by the insufficient level of accordance between manufacturing system and product. At the same time, global energy and global information networks have been created during recent years. Besides other applications, these networks can be turned in hard and soft infrastructure supporting the manufacturing system. For this reason, many specialists consider that the conceptual rebuilding of the manufacturing system is a notable challenge. This paper answers to this challenge by suggesting a holonic technique for manufacturing system management at basic, operational level. The proposed technique lays on the following hypothesis: i) next generation manufacturing system will be a modular cyber-physical system, ii) the typical management task is decision making, and iii) the procedure for decision making consists in options identification, followed by their comparative assessment. The approach is to manage the manufacturing system activity by holonic modelling of the system functioning, followed by the identification of a dedicated holarchy. The proposed technique aims to mitigate system performance in what concerns responsiveness capacity, investment efficiency and production costs. The technique is presented in architectural conceptual manner.

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Data-Driven Causal Modeling of the Manufacturing System

Cited by 5 publications

References 18 publications

Study on the Application of the Holistic Optimization Method of the Manufacturing Process in the Case of a Reduced Instances Database

Study on the Application of the Holistic Optimization Method of the Manufacturing Process in the Case of a Reduced Instances Database

Method for holistic optimization of the manufacturing process numerically described as low-dimensional database

Holistic management of next generation manufacturing system

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