首页|New Robotics Study Findings Reported from University of Alberta (Target-path Planning and Manufacturability Check for Robotic Clt Machining Operations From Bim Information)
New Robotics Study Findings Reported from University of Alberta (Target-path Planning and Manufacturability Check for Robotic Clt Machining Operations From Bim Information)
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NETL
NSTL
Elsevier
Investigators publish new report on Robotics. According to news originating from Edmonton, Canada, by NewsRx correspondents, research stated, “Mass timber is one of the trending construction styles in the last years thanks to its sustainability and offsite manufacturing properties, where cross laminated timber (CLT) is the most common material used. In an effort to link design and manufacturing information in a single location, this study proposes a generative process planning algorithm for CLT machining in robotic environments.” Financial supporters for this research include SMART Lab at the University of Alberta, CGIAR. Our news journalists obtained a quote from the research from the University of Alberta, “The algorithm focuses on automatic feature-based interpretation of the geometry of CLT panels to obtain the targets required to guide the robots for its machining. The method developed detects primitives geometries of the CLT panels, determines the appropriate operations (either sawing, drilling, or milling), select the robot based on manufacturing capabilities (reach and tool availability), and generates the target-path planning for its machining process. The proposed method is tested in a robotic machining station for CLT panels simulated in RobotStudio ® as a case study. The results showcase the capabilities of the proposed algorithm to provide manufacturing results out of the process planning process from geometric information available at the design stage. These results include process duration, path planning, resource allocation and utilization.” According to the news editors, the research concluded: “This study provides a framework to include manufacturing information in design decisions to facilitate planning or cost estimations and anticipate issues downstream generated during the design phase.”
EdmontonCanadaNorth and Central AmericaAlgorithmsEmerging TechnologiesMachine LearningRoboticsRobotsUniversity of Alberta
NETL
NSTL
Elsevier
