相关实验视频

Updated: May 2, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.9K

根据过程参数预测材料挤出部件的线性维度准确性,使用由进化算法优化的神经网络

Carsten Schmidt1, Jonas Funk1, Rainer Griesbaum1

  • 1Institute of Applied Research, Karlsruhe University of Applied Sciences, Karlsruhe, Germany.

3D printing and additive manufacturing
|March 28, 2025
PubMed
概括

这项研究开发了一个神经网络 (NN),以准确预测使用材料挤出 (MEX) 制造的3D打印部件的尺寸精度. 通过对打印参数进行精确控制,NN模型显著提高了工艺效率.

关键词:
维度准确度 维度准确度 维度准确度 维度准确度线性回归是一种线性回归.材料挤出 材料挤出神经网络的神经网络的神经网络预测 预测 预测 预测质量质量质量质量质量质量.

更多相关视频

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

1.1K
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

673

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
438
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70