Piezoelectric-based non-destructive monitoring of contact pressures in fine-blanking process

Ding, Rui; Guo, Cheng; Zhu, Zicai; Zhang, Ming-kai; Gao, Rong

doi:10.1177/0954405413514955

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…In this article, the Kriging models of Δ R a , Δ R y , and Δ SH were developed in terms of machining parameters using the experimental data. The Kriging model is an integrative function of a known polynomial function p ( x ) and local deviations Z ( x ) 27…”

Section: Methodsmentioning

confidence: 99%

Optimization of roller burnishing process using Kriging model to improve surface properties

Nguyen

2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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The objective of this work is to investigate the influences of three machining factors (burnishing speed V, feed rate f, and depth of penetration a) on the improved rate of arithmetic average roughness Δ Ra, improved rate of maximum height roughness Δ Ry, and improved rates of surface hardness Δ SH. The internal roller burnishing experiments were conducted with the aid of the computer numerical control machining center and Box–Behnken experimental design. The Kriging models were used to render the highly nonlinear relationships between inputs and outputs. An integrative approach combining a Non-dominated Sorting Genetic Algorithm II and Technique for Order Preference by Similarity to Ideal Solution was adopted to generate a set of feasible optimal solutions and determine the best machining conditions. The scanning electron microscopy images were depicted to investigate the surface morphology at the different conditions. The X-ray diffraction was applied to measure the compressive stresses at the external surface. The results showed that the predicted values of the objectives have good agreement with the experimental ones. High surface quality is characterized by an improved average roughness of 95.80%, an enhancement in the maximum roughness of 91.98%, and an improvement in surface hardness of 45.44%, compared to pre-machined surfaces. The selection of optimum process parameters could help the burnishing operators to save the machining costs and time. The combination of Kriging model, Non-dominated Sorting Genetic Algorithm II, and Technique for Order Preference by Similarity to Ideal Solution is considered as an intelligent approach for modeling and optimization of burnishing processes.

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Section: Methodsmentioning

confidence: 99%

Optimization of roller burnishing process using Kriging model to improve surface properties

Nguyen

2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Depending on the correlation function, the model can either ''honor the data'' providing an exact interpolation of the data, or ''smooth the data'' providing an inexact interpolation. 18,19 Kriging models are extremely Fexible because a wide range of correlation functions can be chosen in the metamodel building, 19,20 which attract the interests of many authors in the field of space shuttle, 11 wing structures, 21 structural safety, 18 touch probe radius compensation for coordinate measurement in CMMs, 22 sheet-metal-forming processes, designing local cam profile, 23 robust structural design, 24,25 sensitivity of the probabilistic constraint, 26 relationship between the contact pressure and crack width, 27 and path-generating linkage. 28 In this paper, Kriging approximation method is used to evaluate position-dependent natural frequencies for a machine tool and the relative position between the tool and the workpiece during the machining process as well.…”

Section: Introductionmentioning

confidence: 99%

Dynamic optimization method with applications for machine tools based on approximation model

Ding

Cheng

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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Natural frequencies and modal shapes of machine tools have position-dependent characteristics owing to their dynamic behaviors changing with the positions of moving parts. It is time-consuming and difficult to evaluate the dynamic behaviors of machine tools and their machining accuracy at different positions. In this paper, a Kriging approximation model coupled with finite element method is proposed to substitute the dynamic equations for obtaining the position-dependent natural frequencies of a machine tool, as well as relative positions between the tool and the workpiece during the machining process. Based on the proposed method, dynamic performance optimization design of the machine tool is conducted under the condition of minimum relative positions. Three case studies are illustrated to demonstrate the implementation of the proposed method.

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The effect of crack propagation on the quality of fineblanking surface of cold-rolled steel sheet detected by PVDF film stress sensors

Ding

Wang

Tang

et al. 2018

Int J Adv Manuf Technol

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Piezoelectric-based non-destructive monitoring of contact pressures in fine-blanking process

Cited by 7 publications

References 36 publications

Optimization of roller burnishing process using Kriging model to improve surface properties

Optimization of roller burnishing process using Kriging model to improve surface properties

Dynamic optimization method with applications for machine tools based on approximation model

The effect of crack propagation on the quality of fineblanking surface of cold-rolled steel sheet detected by PVDF film stress sensors

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