Inline measurement strategy for additive manufacturing. (April 2019)

• Record Type: Journal Article
• Authors: Bordron, Matthias; Mehdi-Souzani, Charyar; Bruneau, Olivier
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 5(2019)
• Page Start: 1402
• Page End: 1411
• Publication Date: 2019-04
• Subjects: Digital manufacturing -- inline digitizing -- additive manufacturing -- optical measurement -- robot -- quality control -- path planning -- post-processing; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405418755826
• Languages: English
• ISSNs: 0954-4054

Abstract

Additive manufacturing takes a growing place in industry tanks to its ability to create free-form parts with internal complex shape. Yet, the quality of the final surfaces of the additive manufacturing parts is still a challenge since it doesn't reach the required level for final use. To address this issue, it is necessary to measure the form and dimension deviation in order to plan post-process operations to be considerate. Moreover in a context of industry 4.0, this measurement step should be fully integrated into the manufacturing line as close as possible to the additive manufacturing process and post-process. We introduce in this article an inline measurement solution based on a robot combined with a laser sensor. Robot allows reaching most of the orientation and positions necessary to digitize complex parts in a short time. The use of robot for digitizing is already addressed but not for metrological applications. Robots are perfectly designed for velocity, ability and robustness but their poor positioning accuracy is not compatible with measuring requirements. The strategy adopted in this article is to provide an algorithm to generate path planning for digitizing additive manufacturing parts at a given quality of the resulting cloud of points. After a discussion about the geometric and elastic model of the robot to identify the one that answers the quality requirements, the performances of the robot are evaluated. Thus, several performances maps are introduced to characterize the behavior of the robot in its working volume. The qualification of the digitizing sensor is also performed to identify relation between digitizing parameters and the quality of final cloud of points. Using data resulting from the qualifications of sensor and robot and the parts CAD model, the algorithm allows generating path planning to ensure the final quality necessary to measure the shape deviation.