Yang Yang, Ying Cai, Yeo Jung Yoon, Hangbo Zhao, Satyandra K. Gupta
{"title":"SENSOR-BASED PLANNING AND CONTROL FOR CONFORMAL DEPOSITION ON A DEFORMABLE SURFACE USING AN ARTICULATED INDUSTRIAL ROBOT","authors":"Yang Yang, Ying Cai, Yeo Jung Yoon, Hangbo Zhao, Satyandra K. Gupta","doi":"10.1115/1.4063560","DOIUrl":null,"url":null,"abstract":"Abstract Robotic manipulators can be used to deposit materials on non-planar surfaces. Conventional sensor-based industrial robots can only work on stationary surfaces, relying on the scanned data prior to printing. As a result, performing depositions that involve changes in plane motion presents significant challenges. The deposition of conformal materials on a time-varying deformable surface requires the manipulators to update coordinates in real time on the plane for positioning and orientation. This can be achieved by employing multiple sensors for manipulator motion planning and control, in order to prevent collisions between the tool and the surface. In this paper, we propose simple tool center point calibration, initial point coordinate estimation, and a gap compensation scheme to combine real-time feedback control and direct conformal deposition. Combining these elements allows us to maintain a controlled gap between the tooltip and the deformable surface during the deposition. We test the efficacy of the proposed approach by printing a single layer of ink patterns with approximately 950 μm line width on a deformable surface. We also characterize the printing quality with different gaps and printing steps and show that sensor-based control is critical in smooth printing. Finally, the effects of changing the relative position of the tooltip, different surface colors, and laser sensor position are characterized.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4063560","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Robotic manipulators can be used to deposit materials on non-planar surfaces. Conventional sensor-based industrial robots can only work on stationary surfaces, relying on the scanned data prior to printing. As a result, performing depositions that involve changes in plane motion presents significant challenges. The deposition of conformal materials on a time-varying deformable surface requires the manipulators to update coordinates in real time on the plane for positioning and orientation. This can be achieved by employing multiple sensors for manipulator motion planning and control, in order to prevent collisions between the tool and the surface. In this paper, we propose simple tool center point calibration, initial point coordinate estimation, and a gap compensation scheme to combine real-time feedback control and direct conformal deposition. Combining these elements allows us to maintain a controlled gap between the tooltip and the deformable surface during the deposition. We test the efficacy of the proposed approach by printing a single layer of ink patterns with approximately 950 μm line width on a deformable surface. We also characterize the printing quality with different gaps and printing steps and show that sensor-based control is critical in smooth printing. Finally, the effects of changing the relative position of the tooltip, different surface colors, and laser sensor position are characterized.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining