Reliability-based robust process optimization of multi-point dieless forming for product defect reduction

Abebe, Misganaw; Park, Ji-Woo; Kang, Beom-Soo

doi:10.1007/s00170-016-9172-5

Cited by 19 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Latin hypercube design (LHD) introduced by Qian 46 is then used to generate 30 training inputs by using Equation (25). As provided in Section 3.1, the Gibbs sampling procedures are used to estimate the hyperparameters of the GP model.…”

Section: Numerical Examplementioning

confidence: 99%

See 1 more Smart Citation

Life‐cycle reliability‐based robust design optimization for GP model with response uncertainty

Feng

Wang

et al. 2021

Quality & Reliability Eng

View full text Add to dashboard Cite

Reliability-based robust design optimization (RBRDO) is a crucial tool for lifecycle quality improvement. Gaussian process (GP) model is an effective alternative modeling technique that is widely used in robust parameter design. However, there are few studies to deal with reliability-based design problems by using GP model. This article proposes a novel life-cycle RBRDO approach concerning response uncertainty under the framework of GP modeling technique. First, the hyperparameters of GP model are estimated by using the Gibbs sampling procedure. Second, the expected partial derivative expression is derived based on GP modeling technique. Moreover, a novel failure risk cost function is constructed to assess the life-cycle reliability. Then, the quality loss function and confidence interval are constructed by simulated outputs to evaluate the robustness of optimal settings and response uncertainty, respectively. Finally, an optimization model integrating failure risk cost function, quality loss function, and confidence interval analysis approach is constructed to find reasonable optimal input settings. Two case studies are given to illustrate the performance of the proposed approach. The results show that the proposed approach can make better trade-offs between the quality characteristics and reliability requirements by considering response uncertainty.

show abstract

Section: Numerical Examplementioning

confidence: 99%

“…Echard et al 24 developed an active learning reliability approach to assess the probability of failure. Then, for the finite‐element simulation experiment, Abebe et al 25 developed a hybrid model to solve RBRDO problems. Doh et al 26 improved transfer efficiency and reliability of the process by using the weighting models.…”

Section: Introductionmentioning

confidence: 99%

Life‐cycle reliability‐based robust design optimization for GP model with response uncertainty

Feng

Wang

et al. 2021

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…The contact friction coefficient between the blank and elastic cushion, and elastic cushion and punch is considered as 0.1 [19]. Blank sheet and elastic cushions are meshed using C3D8R solid element and R3D4 shell element is also used for the rigid punch element.…”

Section: Simulation Modelmentioning

confidence: 99%

Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation

Abebe

Yoon

Kang

2017

Metals

Self Cite

View full text Add to dashboard Cite

Springback in multi-point dieless forming (MDF) is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF) to replace the numerical simulation model by using statistical analyses that are based on a design of experiment (DOE). Punch holding time, blank thickness, and curvature radius are chosen as effective process parameters for determining the springback. The Latin hypercube DOE method facilitates statistical analyses and the extraction of a prediction model in the experimental process parameter domain. Finite element (FE) simulation model is conducted in the ABAQUS commercial software to generate the springback responses of the training and testing samples. The genetic algorithm is applied to find the optimal value for reducing and compensating the induced springback for the different blank thicknesses using the developed RBF prediction model. Finally, the RBF numerical result is verified by comparing with the FE simulation result of the optimal process parameters and both results show that the springback is almost negligible from the target shape.

show abstract

“…Quan et al 12 studied the influence on sheet metal formability of using elastic cushions in multi-point bending process. Abebe and co-workers proposed multi-objective 13 and reliability-based robust 14 optimization techniques to reduce wrinkling and dimpling defects in MPF. Moreover, a concept of reconfigurable tooling for flexible fabrication was proposed.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation for the flexible stretch-stamp forming process of sheet metal

Wang

Liu

2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Flexible stretch-stamp forming is a sheet metal forming method derived from flexible stretch forming and multi-point forming, aimed at solving wrinkling defects of large skin parts especially with complex curvatures, prone to occur in the later type of forming processes. To help understand the forming principle and forming characteristics of flexible stretchstamp forming, a large sheet metal part with wavelike surface was selected as the study object and extensive numerical simulations have been conducted using a general commercial finite element software. The main processing parameters of pre-stretching force and wrapping force, respectively, exerted by horizontal and sloping cylinders on forming quality were investigated in detail. Finally, experiments were conducted to estimate the forming performances of flexible stretch-stamp forming.

show abstract

Reliability-based robust process optimization of multi-point dieless forming for product defect reduction

Cited by 19 publications

References 21 publications

Life‐cycle reliability‐based robust design optimization for GP model with response uncertainty

Life‐cycle reliability‐based robust design optimization for GP model with response uncertainty

Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation

Numerical investigation for the flexible stretch-stamp forming process of sheet metal

Contact Info

Product

Resources

About