首页|Data from AVIC Manufacturing Technology Institute Advance Knowledge in Robotics (Research on Damage Caused by CarbonFiber-Reinforced Polymer Robotic Drilling Based on Digital Image Correlation and Industrial Computed Tomography)

Data from AVIC Manufacturing Technology Institute Advance Knowledge in Robotics (Research on Damage Caused by CarbonFiber-Reinforced Polymer Robotic Drilling Based on Digital Image Correlation and Industrial Computed Tomography)

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Investigators publish new report on robotics. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “In order to enhance application scenarios and increase the proportion of industrial robots in the field of drilling composites, the damage caused by carbon-fiber-reinforced polymer robotic drilling is studied.” Funders for this research include National Natural Science Foundation of China. Our news journalists obtained a quote from the research from AVIC Manufacturing Technology Institute: “The shortcomings of the existing damage evaluation factors are analyzed, and new damage evaluation factors for carbon-fiber-reinforced polymer laminates made of unidirectional prepreg are proposed. A robot and a brad-and-spur drill were used to drill carbon-fiber-reinforced polymer laminates to study the influence of the process parameters on robotic drilling damage. Digital image correlation equipment and industrial computed tomography were used to study the formation process and the damage forms of the hole on the exit side with different process parameters. The test results show that delamination and tearing are significantly affected by the feed rate and spindle speed, while burrs are less affected by the cutting parameters. Appropriately increasing the spindle speed and reducing the feed rate are beneficial to reducing the comprehensive damage factor and improving the hole quality.”

AVIC Manufacturing Technology InstituteBeijingPeople’s Republic of ChinaAsiaComputed TomographyEmerging TechnologiesImaging TechnologyMachine LearningRoboticsRobotsTechnology

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.9)
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