Efficiency and effectivity of high precision grinding manufacturing processes: An approach based on combined DEA and cluster analyses

Bracke, Stefan; Radetzky, Max; Rosebrock, Christoph; Ulutas, Berna

doi:10.1016/j.procir.2019.02.069

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…In contrast to works [3][4][5], the formulated criterion for the efficiency of the production operation has a pronounced technical and economic character, which is associated with the use of value assessments of various types of products to bring comparable units of measurement to the system. This approach allows us to largely abstract from the physical and technical characteristics of the production process.…”

Section: Discussion Of Results Of Studying the Conceptual Base And Ax...mentioning

confidence: 99%

“…Work [5] considers the analysis of the effectiveness of grinding processes using data coverage analysis (Data Envelopment Analysis, DEA). The input and output parameters for the various grinding schemes were processed using cluster analysis techniques.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Evaluation of the effectiveness of the grinding process was carried out by comparing the following indicators: the duration of the process, the average roughness of the surface, the value of the gloss. The methods and research results applied in [5] cannot be directly applied to other production processes.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 2 more Smart Citations

Creating a concept and axiomatization of the applied theory of efficiency of complex technical systems with distributed parameters of input and output products

Lutsenko

Tytiuk

Vikhrova

et al. 2022

EEJET

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The object of this study is the processes of optimal control over complex technical systems with input and output products distributed in time. The automatic formation of optimal control trajectories of technical systems that ensure maximum efficiency of production processes is limited by the insufficient development of the applied theory of the efficiency of complex systems. This work considers the development of the conceptual base and axiomatization of the applied theory of the efficiency of technical systems. A system of definitions and a system of axioms describing the indicators of the functioning of the cybernetic model of a technical system have been developed. The resulting system of axioms formalizes the technique and methods for determining various indicators, as well as obtaining the current value of the performance indicator of the technical system. The obtained expressions make it possible to form a subsystem for assessing the effectiveness of a technical system, invariant with respect to its internal structure and the characteristics of the transformation processes implemented by it. The universality of the proposed indicators and the structural unity of the performance evaluation subsystem make it possible to express an opinion on the cybernetic level of these decisions. An example of the practical application of the proposed system of axioms of the applied theory of efficiency is given. The proposed method for determining the efficiency indicator can be applied to arbitrary technical systems with a distributed nature of the change in input and output products. The cybernetic level of abstractions used to determine the effectiveness of complex systems makes it possible to proceed to solving the problem of formalization and full automation of the processes of optimal control over complex technical systems with distributed input and output products

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Section: Discussion Of Results Of Studying the Conceptual Base And Ax...mentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Creating a concept and axiomatization of the applied theory of efficiency of complex technical systems with distributed parameters of input and output products

Lutsenko

Tytiuk

Vikhrova

et al. 2022

EEJET

View full text Add to dashboard Cite

show abstract

“…Cai et al [6] used cluster analysis to classify and identify the energy efficiency status by applying image processing, and invented a method to locate and trace the source of low energy efficiency problems occurring in cutting and machining systems, providing a theoretical basis and a potential technical solution for online monitoring of the energy efficiency status of cutting and machining systems. Bracke et al [7] outlined an integrated approach to detect grinding parameter settings which ensures repeatability on the one hand and economy on the other hand. Cluster analysis was used to identify groups of grinding parameters and grinding results with similar characteristics.…”

Section: Introductionmentioning

confidence: 99%

Study on the Predictive of Dynamic Milling Force of Milling Process Based on Data Mining

Jin

ZHANG

et al. 2022

Wuhan Univ. J. Nat. Sci.

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To improve surface accuracy of the work-piece and obtain potentially valuable information, a dynamic milling force prediction model was proposed based on data mining. In view of the current dynamic milling force obtained through finite element simulation and analytical calculation, in the finite element modeling, the model built is inevitably different from the actual working conditions, and the analytical calculation is slightly cumbersome and complex, and a dynamic milling force prediction model based on data mining is proposed. The model was established using a combination of regression analysis and Radial Basis Function (RBF) neural network. Using data mining as a means, the internal relationship between milling force, cutting parameters, temperature, vibration and surface quality is deeply analyzed, and the influence of dynamic milling force changes on different situations is extracted and summarized by the methods of cluster analysis and correlation analysis. The results show that the proposed dynamic milling force model has a good prediction effect, ensures the production quality, reduces the occurrence of flutter, improves the surface accuracy of the work-piece, and provides a more accurate basis for the selection of process parameters.

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“…Stefan [7] outlines an integrated approach to detect grinding parameter settings which ensures repeatability on the one hand and economy on the other. Cluster analysis was used to identify groups of grinding parameters and grinding results with similar characteristics.…”

Section: Introductionmentioning

confidence: 99%

The Predictive Research of Dynamic Milling Force Based on Data Mining

Jin

Zeng

et al. 2021

Preprint

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In this paper, surface accuracy of the work-piece was improved by mining large amounts of machining data and obtaining potentially valuable information. By using data mining technology, a dynamic milling force prediction model has been established to keep with its working. The model was developed by a combination of Regression Analysis and RBF Neural Network. The internal relation of the data were analyzed in this study, such as milling force, cutting parameters, temperature, vibration and surface quality et.al, and the methods of Cluster Analysis and Correlation Analysis was used to extract and induct dynamic milling force variations on the effects with different situations. The results suggest that the proposed dynamic milling force model had a better prediction effect, which ensure production quality, reduce the occurrence of chatter and provide a more accurate basis for selecting process parameters.

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Efficiency and effectivity of high precision grinding manufacturing processes: An approach based on combined DEA and cluster analyses

Cited by 9 publications

References 8 publications

Creating a concept and axiomatization of the applied theory of efficiency of complex technical systems with distributed parameters of input and output products

Creating a concept and axiomatization of the applied theory of efficiency of complex technical systems with distributed parameters of input and output products

Study on the Predictive of Dynamic Milling Force of Milling Process Based on Data Mining

The Predictive Research of Dynamic Milling Force Based on Data Mining

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