相关实验视频

Updated: May 2, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

基因算法为基础的数据驱动过程选择系统用于工业4.0中的增材制造

Bader Alwomi Aljabali¹, Joseph Shelton², Salil Desai^1,3

¹Department of Industrial & Systems Engineering, College of Engineering, North Carolina A & T State University, Greensboro, NC 27411, USA.

Materials (Basel, Switzerland)

|September 28, 2024

概括

本研究介绍了一种用于选择最佳增材制造 (AM) 工艺的自动化系统. 它使用数据驱动方法来改进增材制造 (DFAM) 框架的设计,提高3D打印效率.

科学领域:

制造业工程制造工程
计算机科学计算机科学
材料科学材料科学材料科学

背景情况:

增材制造 (AM) 可以创建复杂的3D对象,但缺乏自动化设计规则.
选择合适的AM流程需要专门的专业知识,这阻碍了广泛采用.
工业4.0要求为增材制造设计 (DFAM) 提供数据驱动的解决方案.

研究的目的:

在DFAM框架内开发一个自动化,数据驱动的系统,用于AM过程选择.
捕获和应用专家知识,以优化AM过程选择.
为了应对3D打印零件的可制造性挑战.

主要方法:

使用3D CAD数据的遗传和进化特征权衡技术.
开发了一个两阶段的预测模型.
基准稳态遗传算法 (SSGA) 与分布算法估计 (EDA) 和粒子群优化 (PSO) 相比.

主要成果:

两阶段模型的平均精度达到70% (第一阶段) 和高达97.33% (第二阶段).
与EDA和PSO相比,SSGA在AM过程选择方面表现优越.
该系统成功地为特定的3D对象几何形状识别了最佳的AM过程.

结论:

开发的自动化系统准确地识别出最佳的AM流程,支持DFAM.
数据驱动的方法有效地捕获和应用专家知识.
该系统提供了一个可扩展的解决方案,用于改善AM工艺选择和可制造性.

关键词:

工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是什么?设计用于增材制造的设计.专家系统专家系统遗传算法是一种遗传算法.

更多相关视频

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Published on: March 13, 2018

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

Published on: August 4, 2018

相关概念视频

Methods of Medium Optimization

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...

Upstream Processing

Upstream Processing

Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...