TY - JOUR

T1 - Laser Scanning Based Object Detection to Realize Digital Blank Shadows for Autonomous Process Planning in Machining

AU - Denkena, Berend

AU - Wichmann, Marcel

AU - Heide, Klaas Maximilian

AU - Räker, René

N1 - Funding Information: Acknowledgments: The authors would like to thank the Sieglinde-Vollmer-Foundation, the Federal Ministry of Economics and Technology (BMWi), and the Lower Saxony Ministry for Science and Culture (MWK) and their partners for funding this paper as part of the research projects, “AdaPES” and “MOBILISE”. Funding Information: Funding: The Federal Ministry of Economics and Technology (BMWi) funded this research, with the grant number, ZF4070527. The Lower Saxony Ministry for Science and Culture (MWK) funded this research, with the grant number, ZN3246. Furthermore, the Sieglinde-Vollmer-Foundation funded this research.

PY - 2022/2

Y1 - 2022/2

N2 - The automated process chain of an unmanned production system is a distinct challenge in the technical state of the art. In particular, accurate and fast raw-part recognition is a current problem in small-batch production. This publication proposes a method for automatic optical raw-part detection to generate a digital blank shadow, which is applied for adapted CAD/CAM (computer-aided design/computer-aided manufacturing) planning. Thereby, a laser-triangulation sensor is integrated into the machine tool. For an automatic raw-part detection and a workpiece origin definition, a dedicated algorithm for creating a digital blank shadow is introduced. The algorithm generates adaptive scan paths, merges laser lines and machine axis data, filters interference signals, and identifies part edges and surfaces according to a point cloud. Furthermore, a dedicated software system is introduced to investigate the created approach. This method is integrated into a CAD/CAM system, with customized software libraries for communication with the CNC (computer numerical control) machine. The results of this study show that the applied method can identify the positions, dimensions, and shapes of different raw parts autonomously, with deviations less than 1 mm, in 2.5 min. Moreover, the measurement and process data can be transferred without errors to different hardware and software systems. It was found that the proposed approach can be applied for rough raw-part detection, and in combination with a touch probe for accurate detection.

AB - The automated process chain of an unmanned production system is a distinct challenge in the technical state of the art. In particular, accurate and fast raw-part recognition is a current problem in small-batch production. This publication proposes a method for automatic optical raw-part detection to generate a digital blank shadow, which is applied for adapted CAD/CAM (computer-aided design/computer-aided manufacturing) planning. Thereby, a laser-triangulation sensor is integrated into the machine tool. For an automatic raw-part detection and a workpiece origin definition, a dedicated algorithm for creating a digital blank shadow is introduced. The algorithm generates adaptive scan paths, merges laser lines and machine axis data, filters interference signals, and identifies part edges and surfaces according to a point cloud. Furthermore, a dedicated software system is introduced to investigate the created approach. This method is integrated into a CAD/CAM system, with customized software libraries for communication with the CNC (computer numerical control) machine. The results of this study show that the applied method can identify the positions, dimensions, and shapes of different raw parts autonomously, with deviations less than 1 mm, in 2.5 min. Moreover, the measurement and process data can be transferred without errors to different hardware and software systems. It was found that the proposed approach can be applied for rough raw-part detection, and in combination with a touch probe for accurate detection.

KW - Autonomous machine tool

KW - Digital twin

KW - Object recognition

KW - Process planning

UR - http://www.scopus.com/inward/record.url?scp=85123952746&partnerID=8YFLogxK

U2 - 10.3390/jmmp6010001

DO - 10.3390/jmmp6010001

M3 - Article

AN - SCOPUS:85123952746

VL - 6

JO - Journal of Manufacturing and Materials Processing

JF - Journal of Manufacturing and Materials Processing

IS - 1

M1 - 1

ER -