Method for Holistic Optimization of the Manufacturing Process

Afteni, Cezarina; Frumuşanu, Gabriel; Epureanu, Alexandru

doi:10.7763/ijmo.2019.v9.721

Cited by 5 publications

(10 citation statements)

References 12 publications

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“… The modelling power MP representing the 'sensitivity' of E-variable relative to the considered C-variable;  The modelling capacity MC referring to the measure in which C-variable is able to describe E-variable by itself only;  The modelling unevenness MU reflecting the variability of the relation between Cand E-variables. The algorithm, designed in order to assess the criteria from above in the case of Cvariable p i , belonging to the cluster (p 1 , p 2 ... p n ), when aiming to model E-variable q, consists of the following steps, [23].…”

Section: Algorithm For Assessing the Modelling Potential Of Variablesmentioning

confidence: 99%

“…The selection of such most suitable model assumes that multiple causal models concerning q are identified. This can be realized by successively and repetitively applying a couple of algorithms [23], namely i) the algorithm for generating smaller clusters, and ii) the algorithm for assessing the modelling potential of a cluster.…”

Section: Identification Of Casual Modelsmentioning

confidence: 99%

“…The exceptions (A, M and R) shall be expressed by conventional dimensionless values, from 1 to 10, assigned after synthesizing some features. The relations describing the real turning process express v, f, t, F, P and V depending on L, D, A, M and R [23], as follows:…”

Section: Case Studiesmentioning

confidence: 99%

“…MS causal identification refers to the finding of causal models for all nominal jobs performed by the addressed MS, as well as the assessment of the obtained causal models. The here proposed MS causal identification consists of applying an algorithm that supposes the following steps: data concatenation, comparison of instances, assessment of variables, and identification of models, [23].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Causal Modeling of the Manufacturing System

Frumusanu

Afteni

Epureanu

2021

TFAMENA

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In manufacturing system management, the decisions are currently made on the base of 'what if' analysis. Here, the suitability of the model structure based on which a model of the activity will be built is crucial and it refers to multiple conditionality imposed in practice. Starting from this, finding the most suitable model structure is critical and represents a notable challenge. The paper deals with the building of suitable structures for a manufacturing system model by data-driven causal modelling. For this purpose, the manufacturing system is described by nominal jobs that it could involve and is identified by an original algorithm for processing the dataset of previous instances. The proposed causal modelling is applied in two case studies, whereby the first case study uses a dataset of artificial instances and the second case study uses a dataset of industrial instances. The causal modelling results prove its good potential for implementation in the industrial environment, with a very wide range of possible applications, while the obtained performance has been found to be good.

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Section: Algorithm For Assessing the Modelling Potential Of Variablesmentioning

confidence: 99%

Section: Identification Of Casual Modelsmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Causal Modeling of the Manufacturing System

Frumusanu

Afteni

Epureanu

2021

TFAMENA

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“…The authors used a new methodology for analyzing a set of cost data available in the literature, and compared the new cost model to results from a neural network based analysis and to a cost regression model. The paper [8] concerns a new approach of the finding optimal decisions at all stages of the manufacturing process. This approach involves estimating the results (for example the cost of a product) based on the values of a descriptive parameters by comparative assessment.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Roller Bearings Manufacturing Cost by Causal Identification and Comparative Assessment – Case Study Performed on Industrial Data

Frumuşanu¹,

Afteni²,

Păunoiu³

2020

IJMO

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The conventional approach is not the best choice for optimizing the manufacturing process, because of its specific structure, and of the specific definition of the optimization problem in its case. During last years, the authors of this paper have developed the concept of holistic optimization and a method for its application in manufacturing process optimization. The method works on the base of two procedures, aiming the causal identification and the comparative assessment of the manufacturing jobs. This paper presents a validation of these procedures by applying them in the case of estimating the manufacturing cost of roller bearings. The case study used a database extracted from the industrial environment. Clusters of condition-variables being the most appropriate for evaluating the cost are determined, at first. Then, neighborhoods of the interrogated cases are extracted from the database and the proximity function is identified. The estimated ranking (hence the manufacturing cost) of the interrogated case is found on this base, eventually.

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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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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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Method for Holistic Optimization of the Manufacturing Process

Cited by 5 publications

References 12 publications

Data-Driven Causal Modeling of the Manufacturing System

Data-Driven Causal Modeling of the Manufacturing System

Estimation of Roller Bearings Manufacturing Cost by Causal Identification and Comparative Assessment – Case Study Performed on Industrial Data

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

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