Collaborative Systems, Operation and Task of the Manufacturing Execution Systems in the 21st Century Industry

Rozs, Richard; Andó, Mátyás

doi:10.3311/ppme.14413

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…By using the information of surface normal, we extended the definition of U with a sign: we don't simply take the number of intersections K into account, but separate K+ intersections, where the direction of the projection beam and the intersected surface normal is the same, and K-, where they are opposites. This way, a measure of Ueqv can be defined (3).…”

Section: Methodsmentioning

confidence: 99%

“…The large production volume of this industrial sector drives the conduction of extensive research of cost-effective manufacturing technologies [2]. The manufacturing systems, in general, have undergone significant development in the last decades [3]. Focusing on the automotive industry and the cost-effectiveness, Khan and Mehmood [4] accomplished a comprehensive review of composite manufacturing technology.…”

Section: Introductionmentioning

confidence: 99%

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Automated determination of the optimal manufacturing direction of polymer composite shell structures

Takács

Szabó

2022

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

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There is a promising potential in polymer composites in the automotive industry; therefore, design methods aimed at cost and weight efficiency have become increasingly important. The tool cost is a considerable amount of the total cost, which is affected by the direction of the manufacturing planes. In this paper, a method is introduced to automatically determine the optimal manufacturing direction of an arbitrary shell structure. The method is implemented in Python environment. A meshed surface model is read as input, and the geometrical complexity factors are calculated from all directions in the space in a discretized way. A new 3D branch diagram is shown with which the calculated values can be visualized and evaluated. After that, an undercut factor is introduced and calculated, and it is demonstrated that the minimum of the product of these measures can give the optimal manufacturing direction in a fully automatized way. The paper presents a study about a train seat to show the industrial applicability of the method by evaluating the effect of the manufacturing directions and geometrical complexities at different partitioning of the composite shell on material cost.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated determination of the optimal manufacturing direction of polymer composite shell structures

Takács

Szabó

2022

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

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“…Manufacturing systems, including MES, were reviewed in [ 19 ] and a comprehensive overview of the actual MES solutions with special attention to MES-complementary systems and tools was presented in [ 20 ]. Vertical integration of factory automation systems into MESs was presented in [ 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Personalization of the MES System to the Needs of Highly Variable Production

Zwolińska

Tubis

Chamier-Gliszczyński

et al. 2020

Sensors

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The new generation Manufacturing Executions System (MES) is considered as one of the most important solutions supporting the idea of Industry 4.0. This is confirmed by research conducted among companies interested in the implementation of the Industry 4.0 concept, as well as the publications of researchers who study this issue. However, if MES software is a link that connects the world of machines and business systems, it must take into account the specifics of the supported production systems. This is especially true in case of production systems with a high level of automation, which are characterised by flexibility and agility at the operational level. Therefore, personalization of the MES software is proposed for this class of production systems. The aim of the article is to present the MES system personalization method for a selected production system. The proposed approach uses the rules of Bayesian inference and the area of customisation is the technological structure of production, taking into account the required flexibility of the processes. As part of the developed approach, the variability index was proposed as a parameter evaluating the effectiveness of the production system. Then, the results of evaluation of the current system effectiveness by use of this index are presented. The authors also present the assumptions for the developed MES personalization algorithm. The algorithm uses the rules of Bayesian inference, which enable multiple adjustments of the model to the existing environmental conditions without the need to formulate a new description of reality. The application of the presented solution in a real facility allowed for determining production areas which are the determinants of system instability. The implementation of the developed algorithm enabled control of the generated variability in real time. The proposed approach to personalization of MES software for a selected class of production systems is the main novelty of the presented research and contributes to the development of the described area of research.

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“…However, to stay competitive and adapt to the ever-changing market demands, injection molding companies must move towards smart manufacturing, or Industry 4.0 [ 2 ]. Industry 4.0 involves the digitization of production, which inevitably leads to the generation of Big Data [ 3 ]. The Big Data lifecycle includes the generation, acquisition, storage, processing, and analysis of data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction

Párizs

Török

Ageyeva

et al. 2022

Sensors

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One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8–10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm.

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Collaborative Systems, Operation and Task of the Manufacturing Execution Systems in the 21st Century Industry

Cited by 4 publications

References 15 publications

Automated determination of the optimal manufacturing direction of polymer composite shell structures

Automated determination of the optimal manufacturing direction of polymer composite shell structures

Personalization of the MES System to the Needs of Highly Variable Production

Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction

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