Automated 3D burr detection in cast manufacturing using sparse convolutional neural networks

For automating deburring of cast parts, this paper proposes a general method for estimating burr height using 3D vision sensor that is robust to missing data in the scans and sensor noise. Specifically, we present a novel data-driven method that learns features that can be used to align clean CAD models from a workpiece database to the noisy and incomplete geometry of a RGBD scan. Using the learned features with Random sample consensus (RANSAC) for CAD to scan registration, learned features improve registration result as compared to traditional approaches by (translation error (Δ18.47 mm) and rotation error(Δ43∘)) and accuracy(35%) respectively. Furthermore, a 3D-vision based automatic burr detection and height estimation technique is presented. The estimated burr heights were verified and compared with measurements from a high resolution industrial CT scanning machine. Together with registration, our burr height estimation approach is able to estimate burr height similar to high resolution CT scans with Z-statistic value (z=0.279).publishedVersio

An Additive Manufacturing Path Generation Method Based on CAD Models for Robot Manipulators

Traditional extrusion based Additive Manufacturing (AM) is realized using a 3 Degrees of Freedom (DOF), translation only, 3D printer. Here, the printer must be larger than the printed part. One way of enabling AM in large-scale is to combine AM with robotics. By using a 6 DOF robot manipulator to extrude a fast-curing material, the workspace of the build would be greatly expanded and it would be possible to increase the flexibility of the building process itself since the structure would no longer have to be built from the bottom-up approach which is necessary for most existing forms of AM. This could possibly reduce the need for support structures to the point of only relying of anchoring and stabilizing. In this thesis, a method for generating a path for AM using robot manipulators that takes advantages of the robots DOF is presented. The path is generated based on simple surfaces in CAD models. First, the surface(s) is sampled and the samples are gathered in a point cloud. Then, a path is generated based on the point cloud using a path generation algorithm. Three different path generation algorithms was implemented and tested: greedy choice, weighted greedy choice and Travelling Salesman Problem (TSP). Out of the three algorithms, the weighted greedy choice algorithm shows the most promise. With this algorithm, paths that enable printing along curved surfaces and reducing the need for support structures was generated. The method is effective, and by interfacing with FreeCAD, it is easy to review the generated paths through visual aids. It is, however, important to mention that the method only generates paths based on simple surfaces and is based on the assumption of fast-curing material enabling mid-air printing

Additive Manufacturing Path Generation for Robot Manipulators Based on CAD Models

Traditional extrusion based additive manufacturing (AM) is realized using a 3 degrees of freedom (DOF), translation only, 3D printer. It then follows that the printer must be larger than the printed part. One way of enabling AM on a larger scale is to combine AM with robotics. By using a 6 DOF robot manipulator to extrude a fast-curing material, the workspace of the build would be greatly expanded. In addition, since the structures would no longer have to be built with the bottom-up or top-down approach which is necessary for most existing forms of AM, the flexibility of the building process would also increase. This could possibly reduce the need for support structures to the point of only relying of anchoring and stabilizing. In this paper, a method for generating a path for AM using robot manipulators that takes advantage of the robot’s DOF is presented. The path is generated based on simple surfaces in CAD models. First, the surface is sampled and the samples are gathered in a point cloud. Then, a path is generated based on the point cloud. Three different approaches for generating a path are tested where the weighted greedy choice algorithm gave the most promising result. With this algorithm, printing along curved surfaces and in nonlinear paths are enabled

Deburring Using Robot Manipulators: A Review

Deburring of cast parts can be a very challenging task. Today, large burrs on large casting are mostly removed manually. Workers are exposed to hazardous working conditions through, among other things, high noise and vibration levels. Special purpose CNC-machines are available for deburring tasks, but they have a high investment cost that makes them unfit for high-mix low-volume processes. Deburring with robot manipulators are seen as a suitable and less expensive alternative, and have been in the focus of research topic for the last 50 years. Unfortunately, it has failed to move from research into industrial applications. One reason is the long system setup time that makes the cost of automatic deburring too high. This paper deals with the status and usage of robot manipulators in deburring applications with focus on solutions for cast parts. The deburring pipeline and its components are investigated. There is a special focus on the solutions that lead to a more flexible and automatic deburring system by using sensors such as laser, vision and force control. The solutions are evaluated with regards to the current challenges with robotic deburring and what needs to be improved for robotic deburring to become available for high-mix low-volume processes