Automation of the Edge Deburring Process and Analysis of the Impact of Selected Parameters on Forces and Moments Induced during the Process

The article concerns the possibility of the automation and robotization of the process of deburring jet engine components. The paper presents the construction of a laboratory stand enabling the automation of selected production operations of typical low-pressure turbine blades. The work identifies important parameters and results of the technological process related to the removal of burrs that affect the exactness of the process. The results of the analysis of the impact of individual process parameters on the magnitude of forces and moments occurring during deburring were carried out and presented. The results of initial and detailed tests were presented. Based on the results obtained, it was noticed that doubling the rotational speed of the brush results in a linear increase in torque and an increase in the engagement of the detail in the disc brush, leading to a non-linear increase in torque. It has also been shown that with tool wear, the value of the torque generated by the rotating tool decreases. Based on the results of a comparison of manual and automated process and histogram analysis, results from an automated stand are centered more correctly inside of the required radius range. This means that the repeatability of the process is higher for an automated test stand, which is one of the key aspects of large-scale aviation component manufacturing. Additionally, it was confirmed by visual inspection that all burs had been removed correctly—the deburring operation for all tested work pieces was successful. Based on the results obtained, it was proven that introduction of an automated stand can improve working conditions (by the elimination of the progressive fatigue of employees and the possibility for injury) and allows for the elimination of the negative impact of the machining process on workers. Further areas in which the optimization of the process parameters of the edge deburring can be developed in order to reduce unit costs have also been indicated.

[1]  P. Bandyopadhyay,et al.  Influence of tool wear on chip-like burr formation during micro-milling, and image processing based measurement of inwardly-deflected burrs , 2023, Wear.

[2]  N. Martyushev,et al.  Provision of Rational Parameters for the Turning Mode of Small-Sized Parts Made of the 29 NK Alloy and Beryllium Bronze for Subsequent Thermal Pulse Deburring , 2023, Materials.

[3]  M. Pellicciari,et al.  An Overview of Industrial Robots Control and Programming Approaches , 2023, Applied Sciences.

[4]  A. Burghardt,et al.  Application of a 3D Scanner in Robotic Measurement of Aviation Components , 2022, Electronics.

[5]  T. Schmitz,et al.  Primary Testing of an Instrumented Tool Holder for Brush Deburring of Milled Workpieces , 2022, Journal of Machine Engineering.

[6]  A. Burghardt,et al.  Robotic Grinding Process of Turboprop Engine Compressor Blades with Active Selection of Contact Force , 2022, Tehnicki vjesnik - Technical Gazette.

[7]  P. Doerffer,et al.  The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges , 2022, Inventions.

[8]  P. Rzucidło,et al.  In-Flight Tests of Intruder Detection Vision System , 2021, Sensors.

[9]  M. Brezocnik,et al.  Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table , 2021, Applied Sciences.

[10]  Bingxiao Ding,et al.  Design of a spatial constant-force end-effector for polishing/deburring operations , 2021, The International Journal of Advanced Manufacturing Technology.

[11]  E. Uhlmann,et al.  Modeling of Contact Forces for Brushing Tools , 2021, Ceramics.

[12]  Krzysztof Kurc,et al.  Automatic Detection of Industrial Robot Tool Damage Based on Force Measurement , 2020, Tehnicki vjesnik - Technical Gazette.

[13]  J. Xiang,et al.  Optimization of Grinding Parameters for the Workpiece Surface and Material Removal Rate in the Belt Grinding Process for Polishing and Deburring of 45 Steel , 2020 .

[14]  T. Beno,et al.  Effects of high-pressure cooling in the flank and rake faces of WC tool on the tool wear mechanism and process conditions in turning of alloy 718 , 2019, Wear.

[15]  Alessandra Caggiano,et al.  Digital factory technologies for robotic automation and enhanced manufacturing cell design , 2018 .

[16]  Christian Moeller,et al.  Real Time Pose Control of an Industrial Robotic System for Machining of Large Scale Components in Aerospace Industry Using Laser Tracker System , 2017 .

[17]  Andrzej Burghardt,et al.  MONITORING THE PARAMETERS OF THE ROBOT-OPERATED QUALITY CONTROL PROCESS , 2017 .

[18]  Shashank Soni,et al.  Modeling of burr size in drilling of aluminum silicon carbide composites using response surface methodology , 2016 .

[19]  Victor Songmene,et al.  Milling burr formation, modeling and control: A review , 2015 .

[20]  Onur Guven,et al.  Application of the Taguchi method for parameter optimization of the surface grinding process , 2015 .

[21]  Sudhir Kumar,et al.  Multi Response Optimization in Drilling Al6063/SiC/15% Metal Matrix Composite , 2014 .

[22]  S. Das,et al.  Influence of Drill Geometry on Surface Roughness in Drilling of Al/sic/gr Hybrid Metal Matrix Composite , 2013 .

[23]  Bernd Kuhlenkoetter,et al.  Application and Analysis of Force Control Strategies to Deburring and Grinding , 2013 .

[24]  S. Melkote,et al.  Effect of process parameters on the rate of abrasive assisted brush deburring of microgrooves , 2012 .

[25]  M. Konneh,et al.  Optimization of Precision Grinding Parameters of Silicon for Surface Roughness Based on Taguchi Method , 2011 .

[26]  Ming-June Tsai,et al.  Robotic polishing of precision molds with uniform material removal control , 2009 .

[27]  Fengfeng Xi,et al.  Modeling and control of automated polishing/deburring process using a dual-purpose compliant toolhead , 2008 .

[28]  O. Khatib,et al.  Springer Handbook of Robotics , 2008 .

[29]  Ju Long Yuan,et al.  Parameters Optimization on the Lapping Process of 9Cr18 with Taguchi Method , 2007 .

[30]  V. N. Gaitonde,et al.  Methodology of Taguchi optimization for multi-objective drilling problem to minimize burr size , 2007 .

[31]  J. Norberto Pires,et al.  Force control experiments for industrial applications: a test case using an industrial deburring example , 2007 .

[32]  J. Yuan,et al.  Parameters Optimization on the Lapping Process for Advanced Ceramics by Applying Taguchi Method , 2006 .

[33]  David Dornfeld,et al.  Analysis of Burr Formation Mechanism in Orthogonal Cutting , 1999 .

[34]  David Dornfeld,et al.  Effect of In-Plane Exit Angle and Rake Angles on Burr Height and Thickness in Face Milling Operation , 1999 .

[35]  David Dornfeld,et al.  Burr/Breakout Model Development and Experimental Verification , 1996 .

[36]  S. S. Pande,et al.  The role of deburring in manufacturing: A state-of-the-art survey , 1994 .

[37]  Xingwei Zhao,et al.  Robotic Grinding Process Monitoring by Vibration Signal Based on LSTM Method , 2022, IEEE Transactions on Instrumentation and Measurement.

[38]  Yuliya I. Karlina.  Improvement of the technological process of processing parts of coaxial radio components using thermal impulse deburring , 2021 .

[39]  Christian Brecher,et al.  Robots in machining , 2019, CIRP Annals.

[40]  Andrzej Burghardt,et al.  Calibration and verification of an original module measuring turbojet engine blades geometric parameters , 2019 .

[41]  Anders Robertsson,et al.  Increasing Time-Efficiency and Accuracy of Robotic Machining Processes Using Model-Based Adaptive Force Control , 2012, SyRoCo.

[42]  Ahmet Yardimeden,et al.  Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm , 2011 .

[43]  David Dornfeld,et al.  A study on Burr formation mechanism , 1991 .