This thesis suggests robotic calibration method and implementation of digital twins for energy consumption estimation. During the drilling process, the energy consumption was evaluated to compare the drilling performance before and after applying the robotic calibration. Since the experimental evaluation of energy consumption requires a time-consuming and expensive procedure, the digital twin model was applied for intelligent processing.
The digital twin framework was established in advance to integrate the various devices and support the data processing. To verify the proposed calibration method and digital twin model, practical experiments were conducted using curved CFRP and 6-axis industrial robot arm. Hole quality and power consumption were analyzed before and after applying the calibration method.
When drilling was performed after normal adjustment was applied, a delamination deterrent effect was observed at lower thrust. Because virtual machining in the Digital Twin model drills vertically based on the ideal tool path, the results imply that both virtual drilling and actual calibrated robotic drilling ensure perpendicular drilling. The result indicates that vertical drilling affects both hole quality and energy consumption, which are the performance indicators in the machining process.
INTRODUCTION
Background and motivation
Then, the power consumption before and after the application of the calibration is analyzed using a twin-based digital cutting power assessment as an indicator of robotic drilling efficiency.
Research objectives
Outline of the thesis
LITERATURE REVIEW
- Machining characteristics of CFRP
- Robotic drilling system
- Calibration methods in application of normal measurement in robotic drilling
- Implementation of digital twin in robotic machining
Among the various methodologies, the most widely used methodology is the conventional Fd delamination factor proposed by Chen. 2-3, the conventional distortion factor is defined as the sum of the hole and damaged area divided by the area of the drilled hole. Laser tracker-based calibration is one of the most widely used methods to compensate for real-time position errors.
Meanwhile, the calibration method using multiple laser displacement sensors is actively studied to replace the laser tracker. To verify the results, robotic drilling of aluminum alloys was performed to compare hole quality [21]. Structure of a robotic drilling system equipped with laser displacement sensors: (a) Robotic drilling system using four laser sensors.
In this study, the calibration method for normal adjustment is presented using a single laser profiler. The developed calibration method in this study focuses on the advanced precision drilling system with regard to less load, cost effectiveness, high accuracy and real-time feedback. Digital twins can be used for virtual processing and act as process monitoring systems, making users or subsystems identify processing performance and essentially optimize the physical system [23].
Robotic arm is one of the most widely used machining devices in the high-tech industry, especially for a wide range of automation tasks. Digital Twin automates the material removal process by analyzing the required grinding force parameters and reconditioning the grinding status [25]. The exercise planning is optimized by decision making based on the estimated energy consumption [26].
However, there are very few studies of digital twin combined with robotic drilling system. A digital twin powered approach to smart manufacturing: reduced energy consumption in road planning (Vatankhah Barenji, A., et al. [26]).
SURFACE GENERATION AND CALIBRATION FOR DRILLING PERPENDICULARITY 11
- Scanning process of workpiece surface
- Generation of surface mesh
- Calculation of normal vector on meshed surface
- Calibration of systematic error
- Analysis of measurement uncertainties
- Calibration process of systematic error
- Implementation of robotic position adjustment
- System components description
- Process flow of normal adjustment
- Summary
Tilted angle of drill bit axis is calculated from the normal vector of drill bit on mesh surface. a) Geometric relationship between adjusted and unadjusted tools (b) Coordinate transformation of the tool. 3-2 (a), feed direction of the drill tip is set to z-axis of the tool coordinate system. Perpendicularity can be ensured by the feedback of calculated turned angle to the robot arm.
Therefore, relative position and orientation between robot arm and the sensor were defined to calibrate the error due to the positioning of laser sensor. External uncertainties are caused by external factors of the sensor such as the positioning of the sensor with respect to the scanned surface, data processing by the computer and roughness of the workpiece surface [29]. Among them, uncertainties due to the positioning and orientation of the sensor are mainly considered and calibrated.
-4, this paper mainly focuses on two factors: tilt angle 𝛼 and 𝛽. 𝛼 is the angle between the normal axis of the scanned surface and the axis of the laser scan plane, indicating how tilted the sensor is relative to the x-axis. These two angles can affect the displacement data 𝑑, which is the distance between laser profiles and the projected points on the surface of the workpiece. a) Tilted angle relative to the x-axis. The overall robot drilling system consists of three coordinate systems: the coordinate system of the laser profiler 𝑃𝐿, the spindle installed on the robot 𝑃𝑅𝑆 and the robot base 𝑃𝑅𝐵.
The TRSL homogeneous transformation matrix transforms the coordinate matrix from the laser profile to the axis. Since the axis system can be disconnected from the robot system, the dimension of the axis-laser profile assembly is measured. After calculating the normal vector, the angle data is sent to the robot controller via TCP/IP socket communication.
The toolpath is generated and fed to the robot arm controller using the CAM program. After the scan is completed, normal vector at drill tip and tilted angle of the tool is calculated based on the generated 3D local surface. Then it makes angle feedback to the robot arm controller from the computer via wireless communication.
Finally, the robot arm performs position adjustment according to the received data and starts drilling.
IMPLEMENTATION OF DIGITAL TWIN FOR THE ENERGY CONSUMPTION ANALYSIS 21
- Configuration of digital twin
- Tool-workpiece engagement regions
- Cutting power model based on material removal rate
- Digital twin-based cutting power estimation
- Experimental cutting power
- Experimental observation of specific cutting energy
- Summary
In this study, the material removal rate calculated via virtual machining is used as a parameter for the predictive model of cutting power. The cutting performance predictive model based on material removal rate (MRR) is combined with ModuleWorks software. The ModuleWorks software calculates the removed volume of the virtual workpiece based on the engagement areas between tool and workpiece.
To predict the cutting power for arbitrary tool-workpiece engagement regions during the drilling process, the cutting power model is established based on the material removal rate. Basically, the energy consumption in the drilling process consists of three components: idle power 𝑃𝑖𝑑𝑙𝑒, cutting power 𝑃𝑐𝑢𝑡𝑡𝑖𝑛𝑔 and additional energy loss 𝑃𝑙𝑃𝑙𝑙. The additional energy loss 𝑃𝑙𝑜𝑠𝑠 is the energy consumption caused by the shear loss in the system.
For the verification of power estimated by the digital twin, the data of experimentally measured cutting force must be collected. Second method measures cutting force indirectly by using the existing cutting force model which has drilling force and machining parameters as variables. The cutting force, one of the parameters of cutting force model, is obtained by the dynamometer which can measure the cutting force in x, y and z directions.
As mentioned above, drilling forces are key parameters of the cutting force model as shown in fig. Since machining parameters and tool geometry are known values, the total cutting force can be obtained by measuring the cutting forces experimentally. These empirical constants are obtained by testing under the cutting conditions given in Table 4-1.
The specific shear energy is adopted as an empirical constant to estimate the shear strength based on the digital twin model suggested in Eq. Considering the material removal rate and the specific cutting energy, which is an empirical constant together, the cutting power was calculated in real time.
EXPERIMENTAL RESULTS AND VALIDATION OF ANGLE CORRECTION SYSTEM
- Experimental setup for curved CFRP drilling
- Comparison of surface quality
- Comparison of delamination factor
- Comparison of thrust force
- Estimation of energy consumption
- Summary
As shown in Fig 5-3, the normal adjustment system is implemented on the left side of the GUI. On the right side of the GUI, the virtual processing is operated based on the robot arm movement data on the hardware part. To verify the effectiveness of the normal adjustment method proposed in chapter 3, the experimental results are compared.
Firstly, the delamination factor is set as a criterion, which allows a direct comparison of the hole quality. In general, the delamination at the exit side of the laminate (push-out delamination) is a more serious defect compared to the delamination at the drill entry (peel-up delamination). Optical images of the machining surface are obtained via the digital microscope (Keyence, VHX-7000) to calculate the delamination factor.
5-4 shows the images of the ejection delamination, according to the variation of feed conditions. Thrust force is set as the second criterion to verify the effectiveness of the normal adjustment. The thrust force generated before and after applying the normal adjustment is compared as in Fig.
Measured shear force after applying normal adjustment and predicted shear force based on digital twin. However, the shear strength before applying normal adjustment is measured to be much higher than after applying normal adjustment. Therefore, the normalized shear strength becomes similar to that of the twin-based prediction.
Therefore, vertical drilling not only affects hole quality, but also energy consumption, which is one of the most important performance indicators in the machining process. The cutting power of the digital twin-based model was comparable to the cutting power measured with normal adjustment.
CONCLUSION AND FUTURE WORK
Chen, D., et al., A normal sensor calibration method based on an extended Kalman filter for robotic drilling. Tao, J., et al., Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method. Nubiola, A., et al., Comparison of two calibration methods for a small industrial robot based on an optical CMM and a laser tracker.
A new method of surface-normal measurement in robotic drilling for aircraft fuselage using three laser distance sensors. Giannaccini, M.E., et al., New design of a soft lightweight pneumatic continuum robotic arm with decoupled variable stiffness and positioning. Oyekan, J., et al., The application of a 6 DoF robotic arm and digital twin to automate fan blade repair for aerospace maintenance, repair and overhaul.
Tian, W., et al., An automatic normalization algorithm for a precision drilling robotic system in aircraft component assembly. Armendia, M., et al., Evaluation of a digital twin machine tool for machining in an industrial environment. Tong, X., et al., Real-time data processing application and service based on IMT digital twin.
Deng, C., et al., Data cleaning for energy conservation: a case of cyber-physical machine tool health monitoring system. Yoon, H.-S., et al., Control of machining parameters for energy and cost savings in microscale drilling of PCBs. Jia, S., et al., Establishing predictive models for feed power and material drilling power to support sustainable machining.
