首页|Researchers from Ecole de Technologie Superieure Report Recent Findings in Robotics (Influence of Machining Parameters On Dynamic Errors In a Hexapod Machining Cell)
Researchers from Ecole de Technologie Superieure Report Recent Findings in Robotics (Influence of Machining Parameters On Dynamic Errors In a Hexapod Machining Cell)
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NETL
NSTL
Springer Nature
Researchers detail new data in Robotics. According to news reporting originating from Montreal, Canada, by NewsRx correspondents, research stated, “Dynamic errors from the robotic machining process can negatively impact the accuracy of manufactured parts. Currently, effectively reducing dynamic errors in robotic machining remains a challenge due to the incomplete understanding of the relationship between machining parameters and dynamic errors, especially for hexapod machining cells.” Funders for this research include Fonds de recherche du Quebec - Nature et technologies (FRQNT), Natural Sciences and Engineering Research Council of Canada (NSERC), Natural Sciences and Engineering Research Council of Canada (NSERC). Our news editors obtained a quote from the research from Ecole de Technologie Superieure, “To address this topic, a dynamic error measurement strategy combining a telescoping ballbar, an unscented Kalman filter (UKF), and particle swarm optimization (PSO) was utilized in robotic machining. The machining parameters, including spindle speed, cutting depth, and feeding speed, were defined using the Taguchi method. Simultaneously, vibrations during machining were also systematically measured to fully comprehend the nature of dynamic errors. Experimental results indicate that dynamic errors in a hexapod machining cell (HMC) are significantly amplified in machining setups, ranging from 4 to 20 times greater compared to those of non-machining setups. These errors are particularly influenced by machining parameters, especially for spindle speed. Furthermore, the extracted dynamic errors exhibit comparable frequency distributions, such as spindle frequency and tool passing frequency, to the vibration signals obtained at the chosen sampling rate.”
MontrealCanadaNorth and Central AmericaEmerging TechnologiesMachine LearningRoboticsRobotsEcole de Technologie Superieure
NETL
NSTL
Springer Nature
