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 a 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.

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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 ($$\Delta $$ Δ 18.47 mm) and rotation error($$\Delta 43 ^\circ $$ Δ 43 ∘ )) 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 ($$z=0.279$$ z = 0.279 ).

Advanced manufacturing technologies play an important role in keeping production in high‐cost countries. Due to their flexibility, robot‐based solutions have been one of the major enablers in establishing advanced manufacturing capabilities in the traditional high‐cost countries. This paper concerns the problem of automated deburring of cast parts. If performed manually, this operation introduces major health, safety, and environmental (HSE) concerns. As such, removal of highly variable casting burrs in the Norwegian context requires a solution based on robots, smart sensors, and advanced algorithms to tackle the problem in a flexible and cost‐effective way. Due to the complexity of the task, one is confronted with a breadth of various alternatives to choose from when realizing the desired functionality. These alternatives expand as one considers a pipeline of the subtasks involved in the process. The decisions made in each step cascade throughout the whole pipeline. To tackle the complexity while developing a robotized deburring system, a systemic approach based on cascading trade‐off studies is proposed in this paper. This paper is also a contribution to the gap in the literature for cases of trade‐off studies in the domain of mechanical engineering. The SPADE (stakeholders, problem formulation, analysis, decision making, and evaluation) methodology has been used as a framework to resolve automation of complex mechanical engineering manufacturing process decisions. The systems engineering (SE) approach proved useful to identify and prioritize the stakeholders and their needs, as well as analyzing the different alternatives in a complex engineering system when dealing with cascading trade studies.