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Related Concept Videos

Bearings: Problem Solving01:24

Bearings: Problem Solving

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Understanding the calculations and concepts related to double-collar bearings is essential for engineers and designers to optimize the performance of these components in various applications. By analyzing the bearing under different conditions, one can ensure that it can withstand the forces and moments experienced during operation. This knowledge enables better decision-making when designing and selecting bearings for specific purposes and configurations. Consider a double-collar bearing with...
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Temperature Dependent Deformation01:12

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In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
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Consider the elastic torsion formula, which applies to a circular shaft with a consistent cross-section. This formula assumes that the shaft's ends are loaded with rigid plates firmly attached. However, in many cases, torques are applied to the shaft through mechanisms like flange couplings or gears, which are connected by keys inserted into keyways. This application method modifies the stress distribution near the point of torque application, causing it to deviate from the distributions...
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Designing a solid shaft that transmits power from a motor to a machine tool involves a series of calculations to ensure the shaft can withstand the stresses applied by bending moments and torques. First, calculate the torque exerted on the gear, considering the power transmitted by the shaft and its rotational speed. Following this, compute the tangential forces acting on the gears, which directly relate to the torque and the gear radius.
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Updated: Jun 23, 2025

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction.

Feng Guo1, Hoyoung Jeong1,2, Donghwi Park1

  • 1Department of Mechanical Engineering, Sogang University, Seoul 04107, Republic of Korea.

Materials (Basel, Switzerland)
|June 19, 2024
PubMed
Summary

An intelligent optimization technology using deep neural networks, genetic algorithms, and Monte Carlo simulation effectively reduces defects like failure, wrinkling, and springback in sheet metal forming.

Keywords:
defect predicationmulti-objective optimizationsurrogate model methodologiesvariable blank holder force trajectories

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Computational Mechanics

Background:

  • Sheet metal forming processes are prone to defects such as failure, wrinkling, and springback.
  • Optimizing process parameters is crucial for mitigating these defects and improving product quality.

Purpose of the Study:

  • To develop and validate an intelligent optimization technology for reducing multi-defects in sheet metal forming.
  • To elucidate the relationship between processing parameters, material properties, and defect formation.

Main Methods:

  • A hybrid approach combining deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS) was developed (DNN-GA-MCS).
  • Variable blank holder force (VBHF) trajectories were implemented and optimized using numerical simulations of an oil pan model.
  • Machine learning algorithms, based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model, predicted Forming Limit Diagrams (FLDs) to evaluate sheet failure dynamics.

Main Results:

  • The DNN-GA-MCS method achieved significant improvements in defect reduction: 18.89% for failure, 13.59% for wrinkling, and 14.26% for springback compared to training set averages.
  • A two-segmented VBHF strategy improved the average defect reduction by 12.62% and reduced total VBHF by 14.07%.
  • Statistical analysis and material flow analysis informed optimization strategies for die structures to enhance material flow efficiency.

Conclusions:

  • The proposed intelligent optimization technology demonstrates considerable potential for improving sheet metal forming processes.
  • The methodology effectively reduces defects and offers a pathway for enhanced material flow and die structure optimization.