Prediction of Surface Roughness and Power in Turning Process Using Response Surface Method and ANN

doi:10.17559/tv-20190522104029

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…One of the most common ways of doing this is to reduce storage capacity. Therefore, this paper considers three constraints in the model, including the supplier's minimum order quantity setting, inventory constraints and purchasing demand constraints [33]. Finally, an improved genetic algorithm is proposed to find the optimal purchasing volume and allocation scheme.…”

Section: The Framework Of Centralized Procurement Modelmentioning

confidence: 99%

Pareto Optimality of Centralized Procurement Based on Genetic Algorithm

2022

Teh. vjesn.

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In the process of purchasing materials, small enterprises are often unable to meet the minimum availability of suppliers in the process of purchasing due to the lack of economic strength and storage capacity of goods. Therefore, they will encounter difficulties in the process of purchasing.To solve this problem, the group-led centralized procurement strategy for small enterprises has become a new craze. In this paper, we transform the problem of centralized procurement lot into a multi-objective optimization problem by establishing a multi-objective optimization model with cost, quality and logistics as sub-objectives, and use genetic algorithms to solve the multiobjective optimization problem in order to achieve Pareto optimality among each purchaser and supplier. Finally, an example of procurement by the China Energy Investment Corporation is used to verify that the multi-objective optimization model for the collection of lots constructed in this paper can effectively promote the cooperation between purchasers and suppliers, and stimulate the competitive vitality of enterprises in the market.

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Section: The Framework Of Centralized Procurement Modelmentioning

confidence: 99%

Pareto Optimality of Centralized Procurement Based on Genetic Algorithm

2022

Teh. vjesn.

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“…The outcomes revealed that a higher V caused a decreased SR and stable machining. Aljinović et al examined the impacts of the V, f, and D on the SR and power consumed (PC) of the turning aluminium alloy using the RSM and ANN [15]. The authors stated that the ANN provided a better accuracy for the responses, as compared to the RSM one.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Rough Self-Propelled Rotary Turning Parameters in terms of Total Energy Consumption and Surface Roughness

2023

Teh. vjesn.

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The self-propelled rotary tool turning (SPRT) process is an economic and effective solution for machining difficult-to-cut materials. This work optimized SPRT parameters, including the inclination angle (A), depth of cut (D), feed rate (f), and turning speed (V) to decrease the total energy consumption (TE) and surface roughness (SR). The turning experiments of the hardened AISI 4150 steel were executed to obtain the experimental data, while the regression method was applied to develop the TE and SR correlations. The entropy method and quantum-behaved particle swarm optimization (QPSO) were utilized to select the weights and optimal factors. The results indicated that the optimal A, D, f, and V were 34 deg., 0.40 mm, 0.47 mm/rev., and 177 m/min, respectively, while the TE and SR were saved by 9.7% and 35.4%, respectively. The f and V were found to be the most effective parameters, followed by the D and A. The outcomes provide valuable data to determine optimal SPRT factors for minimizing energy consumption and maximizing machining quality.The optimizing technique could be applied to solve other issues for different SPRT operations.

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“…The predictive accuracy of data-driven model is generally better than that of the theoretical model. The data-driven modeling method for surface roughness prediction mainly includes multiple regression modeling method [17,18], response surface methodology (RSM) [19][20][21] and intelligence algorithm modeling method (such as neural network [22][23][24][25] and support vector machine (SVM) [26][27][28]). However, since the data-driven model is based on data, and the more data is, the higher the accuracy of the model is, so it needs to spend the large experimental cost.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction for turning based on the corrected subsection theoretical model

Lü

Wang

Chen

et al. 2022

Int J Adv Manuf Technol

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In order to obtain the accurate prediction model of surface roughness in precision machining processes, this paper researches the subsection theoretical model for surface roughness in turning built by our research group, which considers feed rate, nose radius and tool minor cutting edge angle, and proposes two error correction models of the subsection theoretical model for improving theoretical model accuracy. The prediction performance of two error correction models is evaluated by 25 groups of turning data. The experimental results show that two error correction models have excellent prediction performance and significantly improve the prediction accuracy and stability of the subsection theoretical model. Moreover, the influence of turning parameters and tool geometry on surface roughness based on the three models, and the advantages and disadvantages of three models in prediction performance and test cost are analyzed, which provide an effective guidance for selection of parameters and the prediction model of surface roughness in the actual turning.

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Prediction of Surface Roughness and Power in Turning Process Using Response Surface Method and ANN

Cited by 3 publications

References 18 publications

Pareto Optimality of Centralized Procurement Based on Genetic Algorithm

Pareto Optimality of Centralized Procurement Based on Genetic Algorithm

Optimization of Rough Self-Propelled Rotary Turning Parameters in terms of Total Energy Consumption and Surface Roughness

Surface roughness prediction for turning based on the corrected subsection theoretical model

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