Robotic Sanding Toolpath Generation for Variable Part Contours
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Solution Overview
Problem
Current automated finishing systems lack the ability to autonomously and accurately process parts with varying surface contours and materials, leading to inefficiencies and inconsistencies in surface finishing.
Innovation Solution
A robotic system equipped with a sanding head and optical sensor that autonomously scans a part, generates a toolpath, and adjusts the sanding force in real-time to maintain a target force, using a combination of optical images, tool characteristics, and surface contour analysis to ensure precise and efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated finishing systems use fixed toolpaths and constant sanding force, then the system structure is simple and easy to control, but the manufacturing precision and surface finish quality deteriorate when processing parts with varying surface contours
Solution Approach 1:
The system dynamically adjusts the sanding head position and sanding force in real-time based on optical sensor feedback and part geometry detection. The toolpath is no longer fixed but adapts to the actual part contours, allowing high precision surface finishing while maintaining manageable system complexity through automated control algorithms.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring part geometry with optical sensors during scanning and using this information to adjust sanding parameters in real-time. This feedback mechanism enables the system to maintain high manufacturing precision by compensating for surface variations without requiring overly complex manual control.
2Manufacturing precision
If the system scans and processes each part individually with autonomous adaptation, then the manufacturing precision improves, but the productivity decreases due to increased processing time per part
Solution Approach 1:
The system performs preliminary scanning and 3D modeling of the part before actual sanding begins. This preliminary action allows the control system to pre-calculate the adaptive toolpath and identify critical processing parameters in advance, reducing real-time computational burden and enabling faster processing while maintaining high precision.
Solution Approach 2:
The system selectively adjusts sanding parameters (force, speed, head position) only in regions where precision is critical, while using faster, less adaptive processing in less critical areas. This parameter differentiation maintains high manufacturing precision where needed while improving overall productivity by avoiding unnecessary complexity throughout the entire part.
3Productivity
If the system uses high sanding force to process diverse materials quickly, then the productivity increases, but the reliability decreases due to risk of part damage and surface defects
Solution Approach 1:
The system applies different sanding forces and parameters to different regions of the part based on local material properties and surface requirements. High force is applied only where needed for material removal, while lower forces are used in sensitive areas to prevent damage. This local differentiation maintains productivity while ensuring reliability across diverse materials and part regions.
Solution Approach 2:
The system continuously monitors sanding force and part response during processing, using feedback to adjust parameters in real-time. This prevents excessive force that could damage the part while maintaining efficient processing speeds. The feedback mechanism ensures reliability by detecting and responding to material variations and surface conditions during the sanding process.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high-resolution surface processing with high repeatability and reduced execution time, enabling efficient handling of diverse parts and materials by accurately controlling the sanding force and adapting to surface contours.
Implementation Method 1
capture a set of optical images
Data Source
AI summary
One variation of a method for autonomously scanning and processing a part includes: collecting a set of images depicting a part positioned within a work zone adjacent a robotic system; assembling the set of images into a part model representing the part. The method includes segmenting areas of the part model—delineated by local radii of curvature, edges, or color boundaries—into target zones for processing by the robotic system and exclusion zones avoided by the robotic system. The method includes: projecting a set of keypoints onto the target zone of part model defining positions, orientations, and target forces of a sanding head applied at locations on the part model; assembling the set of keypoints into a toolpath and projecting the toolpath onto the target zone of the part model; and transmitting the toolpath to a robotic system to execute the toolpath on the part within the work zone.