The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming

Huang, Tianlun; Song, Xiaojie; Liu, Min

doi:10.1007/s00170-017-0927-4

Cited by 6 publications

(3 citation statements)

References 60 publications

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“…The exact finite element model of the forming process was prepared, and the created model verified in comparison with the experimental specimen. Huang et al [19] optimized the loading path in the hydroforming of a Tshaped tube based on the response surface and nonprobabilistic methods. The purpose of this research was to achieve the maximum protrusion and minimum thinning ratio with optimization of pressure path.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of HMGF Process Parameters for Manufacturing AA6063 Stepped Tubes using FEM-RSM

Rajaee

Hosseinipour

Aval

2021

IJE

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Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of HMGF Process Parameters for Manufacturing AA6063 Stepped Tubes using FEM-RSM

Rajaee

Hosseinipour

Aval

2021

IJE

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“…Huang ve ark. [9] T şekilli tüpü, hidro şekillendirme prosesi için yükleme şartlarını Pareto ve Taguchi yöntemlerini kullanarak optimize etmişlerdir. Yaptıkları nümerik çalışmalarla optimizasyon sonuçlarını doğrulamışlarıdır.…”

Section: Introductionunclassified

Paslanmaz Çeli̇klerde Hi̇dro-Kesme Yöntemi̇ni̇n Sonlu Elemanlar Yöntemi̇ İle İncelenmesi̇

Korkmaz

Toros

Halkacı

2018

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖZYapılan bu çalışmada kesme operasyonunun özellikle son yıllarda birçok alanda yaygın olarak kullanılmaya başlanan hidro şekillendirme prosesine uyarlanabilirliğinin nümerik olarak değerlendirilmesi yapılmıştır. Nümerik çalışmalar kapsamında malzemenin kesilme yüzeyleri değerlendirilmiş ve çapak oluşum durumu zımbanın farklı hareketi için incelenmiştir. Modelleme çalışması kapsamında Johnson-Cook (J-C) pekleşme ve hasar modeli kullanılarak, hedeflenen proses parametrelerinde kesme durumunun gerçekleşip gerçekleşmediği belirlenmeye çalışılmıştır. Sonuç olarak hedef proses parametrelerinde 304 paslanmaz çeliğin bu proses ile kesilebilirliği gösterilmiştir.Anahtar Kelimeler: Hidro-kesme, Johnson-Cook hasar kriteri, 304 paslanmaz çeliği INVESTIGATION OF HYDRO-PIERCING METHOD FOR STAINLESS STEELS BY FINITE ELEMENT METHOD ABSTRACTIn this study, numerical evaluation of the adaptability of the cutting operation to the hydroforming process, which has been widely used in many areas especially in recent years, has been made. In the numerical studies, the cut surfaces of the material were evaluated and the burr formation state was investigated for the different movement of the punch. Within the scope of the modeling study, it was tried to determine whether the cutting condition in the target process parameters was realized by using the Johnson-Cook (J-C) hardening and damage model. As a result, 304 stainless steel can be cut by this process in target process parameters.

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“…Tapia et al built a surrogate model by applying Gaussian processes regression (GPR) to predict the melt pool depth of the laser powder bed fusion process [14]. Numerous methods have been applied in other studies to construct surrogate models; these methods include support vector regression (SVR) [15][16][17][18], the response surface methodology [19][20][21], kriging [22][23][24][25], and the adaptive neuro fuzzy inference system (ANFIS) [26].…”

mentioning

confidence: 99%

Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning

Shiu

Liu

2022

Int J Adv Manuf Technol

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In this study, the clamping stress and force involved in the centering of optical glass lens were evaluated and quantified. On the basis of the key design parameters of the examined clamps, the finite element method was applied to measure clamping stress under various parameter combinations. Support vector regression, Gaussian process regression, and adaptive neuro fuzzy inference system algorithm of surrogate models were established using the results obtained through finite element simulation. These surrogate models, which can predict clamping stress on the basis of key parameters, can reduce the time required to perform finite element analysis while providing references for optimizing clamp configuration.Keywords：Centering process, Surrogate model, Finite element analysis, Machine learning IntroductionAn optical axis is defined as the line that connects the centers of the curvature of the curved surfaces on both sides of a lens. If the optical axis deviates from the lens' geometric center axis, the imaging position of the lens will be affected and cause aberration. Centering is a key procedure in optical glass lens manufacturing because it is required to minimize the aforementioned phenomenon [1-3]. During centering, a lens is secured by a pair of bell-shaped clamps that come into contact with both of the polished surfaces of the lens. The forces between the clamps and the lens are radially balanced. Under this condition, the optical axis coincides with the geometric center line. The lens is then grounded by a grinding wheel, such that its shape becomes perfectly symmetrical with respect to the optical axis. To prevent lens clamps from scratching the polished surfaces of lens, they must be made from soft materials.Consequently, lens clamps are prone to deforming under clamping stress, which causes the central axis of a lens to become offset. With unstable clamping, a lens can be easily pushed and decentered by a grinding wheel feed. Therefore, effective methods for evaluating and analyzing clamping stress are required for the centering process.The finite element method (FEM) is widely used to solve engineering problems, including the evaluation of stress distribution [4] and simulation of mechanical behavior [5,6]. FEM-derived results can also be used to conduct pre-machining assessments [7,8] and support parameter optimization [9,10].However, to obtain accurate simulation results, precise engineering modeling and meshing are required.Simulating and analyzing a complex finite element model are usually tasks that require substantial time and computational resources [11,12]. Surrogate models are often applied as substitutes of complex computational models in engineering design; they are established by using simple computational models

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The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming

Cited by 6 publications

References 60 publications

Multi-objective Optimization of HMGF Process Parameters for Manufacturing AA6063 Stepped Tubes using FEM-RSM

Multi-objective Optimization of HMGF Process Parameters for Manufacturing AA6063 Stepped Tubes using FEM-RSM

Paslanmaz Çeli̇klerde Hi̇dro-Kesme Yöntemi̇ni̇n Sonlu Elemanlar Yöntemi̇ İle İncelenmesi̇

Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning

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