Optimization of blank shape and segmented variable blank holder force trajectories in deep drawing using sequential approximate optimization

Kitayama, Satoshi; Koyama, Hiroki; Kawamoto, Kiichiro; Miyasaka, Takuji; Yamamichi, Ken; Noda, Takuya

doi:10.1007/s00170-016-9877-5

Cited by 29 publications

(19 citation statements)

References 29 publications

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“…

\begin{matrix} left \\ {([], \sum_{i = 1}^{2} {(w_{i} {\overset{f}{true}}_{i} (boldx))}^{p})}^{1 / p} \to \min \\ \begin{array}{c} x_{i}^{L} \leq x_{i} \leq x_{i}^{U} & i goodbreak= 1, 2, \dots, n \end{array} \end{matrix}\}

where

w_{i}

represents the weight of the i th objective function and p is the parameter. According to references 25–29, p is set to 4. Various weights are assigned to find a set of pareto‐optimal solutions

{boldx}^{p}

under

w_{1} + w_{2} = 1

.…”

Section: Multiobjective Optimization Of Multistage Packing Pressure P...mentioning

confidence: 99%

“…Since the pareto-frontier between warpage and cycle time is unknown in advance, the SAO is used to identify the pareto-frontier. We have already resolved several engineering issues such as sheet metal forming using servo press, [25][26][27] process parameters optimization in cold forging 28 and energy management system for hybrid electric vehicle 29 using the SAO with the RBF network. Then, this system is used as the design optimization tool.…”

Section: Sequential Approximate Optimization To Identify Pareto-frontiermentioning

confidence: 99%

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Numerical optimization of multistage packing pressure profile in plastic injection molding and experimental validation

Kitayama

Matsubayashi

Takano

et al. 2022

Polymers for Advanced Techs

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Plastic injection molding (PIM) is a crucial processing technology for producing lightweight products. Process parameters such as melt temperature, packing pressure, and cooling time directly affect not only the product quality but also the productivity, and they are conventionally adjusted by the trial‐and‐error method. A constant packing pressure is conventionally used, but the multistage packing pressure profile recently attracts attention. However, the packing pressure profile is unknown in advance, and it is difficult to determine it through the trial‐and‐error method. In this paper, the multistage packing pressure profile as well as other process parameters so as to minimize the warpage and cycle time are determined by using computational intelligence and design optimization. It is found from the numerical result that 27% warpage and 50% cycle time reduction can be achieved, compared with ones using reference process parameters. In addition, it is clarified that clamping force can be reduced. This indicates that PIM using the optimal multi‐stage packing pressure profile can produce plastic product with small clamping force that makes the tool life long. Finally, the experiment using PIM machine equipped with servo controller (MS100, Sodick) is carried out to examine the effectiveness of the multistage packing pressure profile.

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“…

\begin{matrix} left \\ {([], \sum_{i = 1}^{2} {(w_{i} {\overset{f}{true}}_{i} (boldx))}^{p})}^{1 / p} \to \min \\ \begin{array}{c} x_{i}^{L} \leq x_{i} \leq x_{i}^{U} & i goodbreak= 1, 2, \dots, n \end{array} \end{matrix}\}

where

w_{i}

represents the weight of the i th objective function and p is the parameter. According to references 25–29, p is set to 4. Various weights are assigned to find a set of pareto‐optimal solutions

{boldx}^{p}

under

w_{1} + w_{2} = 1

.…”

Section: Multiobjective Optimization Of Multistage Packing Pressure P...mentioning

confidence: 99%

Section: Sequential Approximate Optimization To Identify Pareto-frontiermentioning

confidence: 99%

Numerical optimization of multistage packing pressure profile in plastic injection molding and experimental validation

Kitayama

Matsubayashi

Takano

et al. 2022

Polymers for Advanced Techs

Self Cite

View full text Add to dashboard Cite

show abstract

“…The advantage of ANN not requiring any explicit formula or calculation model makes itself a very effective method for this paper. Many scholars have obtained the optimal parameters in different forming fields by neural networks [25,26,27,28]. Research into mechanical properties of MMnS under servo conditions is at an initial stage and the mechanism is not quite clear.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Effects of Servo Tensile Test Parameters on Mechanical Properties of Medium-Mn Steel

Chi

Han

2019

Materials

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As a new type of third-generation automotive steel with high strength and plasticity, medium-Mn steel (MMnS) has been widely used in automotive industries for its excellent properties. In recent years, servo stamping technology for high-strength metal forming is a hot topic due to its good performance in forming under complex processing conditions, and servo parameters determine the forming quality. In this paper, experiments considering tensile speed and position where speed changes (PSC) were carried out on MMnS to investigate the influences of tensile parameters on mechanical properties including strength and total elongation (TE). The results show that PSC does not significantly impact total elongation. Initial tensile speed (ITS) and final tensile speed (FTS) significantly impact the total elongation. The interaction between all tensile parameters can impact total elongation. Two artificial neural networks, back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were used to establish analytical models. The results of supplemental experiment and residual analysis were conducted to verify the accuracy of the analytical models. The BPNN has a better performance and the analytical model shows that with the increase of PSC, it has a slight impact on the changes of optimal and minimum total elongation, but the combinations of tensile parameters to obtain total elongations higher than 40% change significantly.

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“…In this context, the main concerns are technological parameters such as the forming fluid pressure, closing pressure, shape of die, friction, workpiece material, etc. [3,[13][14][15][16][17][18][19]. Among them, a large amount of research on forming liquid pressure parameters has been published to determine their influence on the forming process and product quality [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Experimental Modeling of Pressure in the Hydrostatic Formation of a Cylindrical Cup with Different Materials

Nguyen

Bui

2021

Applied Sciences

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Forming complex sheet products using hydrostatic forming technology is currently a focus of the majority of forming processes. However, in order to increase stability during the forming process, it is necessary to identify and analyze the dependency of the forming pressure and the quality of a product on input parameters. For the purpose of modeling the forming pressure, this paper presents empirical research on the product of a cylindrical cup made of various materials, including carbon steel (DC04), copper (CDA260), and stainless steel (SUS 304) with different thicknesses (0.8 mm, 1.0 mm, and 1.2 mm), under a defined range of binder pressures. The regression method is selected to formulate an equation that shows the relationship between the input parameters, including the materials (ultimate strength and yield stress), workpiece thickness, binder pressure and the output parameter, and the formation of fluid pressure. The mathematical equation allows us to determine the extent of the effect of each input on the forming pressure. The experimental results can be used for the easier planning and forecasting of the process and product quality in hydrostatic forming.

show abstract

Optimization of blank shape and segmented variable blank holder force trajectories in deep drawing using sequential approximate optimization

Cited by 29 publications

References 29 publications

Numerical optimization of multistage packing pressure profile in plastic injection molding and experimental validation

Numerical optimization of multistage packing pressure profile in plastic injection molding and experimental validation

Effects of Servo Tensile Test Parameters on Mechanical Properties of Medium-Mn Steel

Experimental Modeling of Pressure in the Hydrostatic Formation of a Cylindrical Cup with Different Materials

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