Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques
Abstract
:1. Introduction
2. Previous Studies in Auto-Vision of Inspection System
2.1. The Minimum Zone Circles
3. Materials and Methodology
3.1. Materials
3.2. The Experimental Setup and Procedure
Case Study
4. The Development of 3SMVI Software and Integration
4.1. The 3SMVI Hardware and Software Integration
4.2. Software Utilization and Algorithms
4.3. Image Analysis and Segmentation
4.4. An Algorithm for Detecting Edges
Objects or Models Detection
4.5. Data Acquisition
4.6. The Algorithm for Edge Labelling
- The edge-pixel image is encoded using the run-length algorithm;
- After scanning the runs, each run is given a preliminary label. The label equivalent is then entered into a local equivalent table;
- The classes that are resolved have equivalence;
- Finally, based on the resolved equivalence classes, the runs are given labels.
4.7. The Error Algorithm for Roundness Holes
- The labeled image is scanned from left to right and top to bottom. During this process, the coordinates of the edge pixels of a selected part (color) are extracted and stored in an array known as Edge Pixels;
- The minimum zone method is then applied to the pixels in the Edge Pixels array to determine the center and radius of the minimum zone circle;
- The center of the minimum zone circle is calculated, and the distances between this center and all pixels in the Edge Pixels array are computed. The minimum and maximum distances are identified as .
5. System Calibration
- The user provides the 3SMVI platform with the actual diameter (in millimeters) of the object being measured. If the object is comprised of multiple circular parts, the outer part’s size (maximum diameter) should be utilized.
- The software searches for the two edge pixels on the outer contour with the minimum and maximum x coordinates to determine the maximum diameter of the captured image in the x direction (Dmaxx) using the formula
- The calibration factor in the x direction is calculated as follows:
- In the same manner, the calibration process also involves determining the calibration factor in the y direction ) by identifying the two pixels with the minimum and maximum y coordinates, which can be obtained by using the formula
- After calculating , all coordinates of the edge pixels are multiplied by these calibration factors.
- The primary roundness error is then calculated using the least squares circle technique on the edge pixels.
- Furthermore, using a formula derived from Excel, the fixed error in pixels is determined based on the user-provided value of the smoothing factor .
- The fixed error is then calibrated and expressed in millimeters () using the following equation:
- The final roundness error is calculated as follows:
Camera-to-Workpiece Distance Estimation
6. CMM Inspection of Contact Measurement
7. Result and Discussion
8. Conclusions and Future Research
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ASME | American Society of Mechanical Engineers |
ACO | Colony Optimization |
CNC | Computer Numerical Control |
CMM | Coordinate measuring machine |
C | Center of this circle |
CMOS | Complementary metal–oxide–semiconductor |
EO | Engineering Ontology |
eMZC | Error-based Minimum Zone radial circles |
CLIM | Closed-loop inspection manufacturing |
CAIP | Computer-aided Inspection Planning |
FTP | File Transfer Protocol |
GA | General Assembly Simulation |
GD&T | Geometric Dimensioning and Tolerance |
I 4.0 | The Fourth Industrial Revolution |
IoT | Internet of Things |
WSET | Wire spark erosion machining |
ISO | International Standard Organization |
LED | Light-emitting Diodes |
MQTT | Message Queuing Telemetry Transport |
MZC | Minimum Zone for Radial Circles |
MTD | Metrology for the Digitalization |
Open CV | Open Computer Vision |
OLP | Offline Programming System |
OOR | Out-of-the-Round |
QM | Quality MITUTOYO Measure |
RGB | Three channels of color: Red, Green, and Blue |
Ri | Radius inner |
RU | Upper radius |
RL | Lower radius |
STEP-NC | The Standard for the Exchange of Product Model Data for Numerical Control |
VNC | Visual network center |
3SMVI | Smart System based on interpreted STEP-NC for Machine Vision Inspection |
3D | Three-dimension model |
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Machine Characteristics | Parameter | Specification |
---|---|---|
Machine Part | Workpiece | Delrin |
Size | 120 × 100 × 50 mm | |
Machining | Feed rate | 0.1 mm |
Spindle speed | 2500 rpm | |
Depth of cut | 22 mm | |
One Circle diameter | 30 mm | |
Cutting speed | 250 m/s | |
Cutting tool | Material type | High-speed steel |
Diameter | 0.6 mm | |
Number of flutes | 2 | |
Tool type | New tool | |
Number of axes | Three axes, X, Y, and Z | |
CMM measurement | Machine type | MITUTOYO QM-353 |
Resolution | 0.0005 mm (0.00002 in.) | |
High accuracy | Accuracy of min 0.0017 mm | |
Versatility | Wide range of probe systems are available | |
Software | ||
Open CV | Open Vision Library2011, windows 10 | |
Python | 3.8.3 | |
Pycharm editor | IDE used in computer programming for Python | |
CAD design | CATIA v5, R21 2020 | |
Operating System | The Raspbian Debian Buster | |
MQTT protocol | Messaging protocol designed for low-bandwidth |
Image No. | Angle in Degree | Mild Delrin Dia. 30 mm |
---|---|---|
1 | 0° | 29.9383 |
2 | 31° | 29.9397 |
3 | 62° | 29.9378 |
4 | 93° | 29.9292 |
5 | 124° | 29.9364 |
6 | 155° | 29.9281 |
7 | 186° | 29.9368 |
8 | 217° | 29.9311 |
9 | 248° | 29.9315 |
10 | 279° | 29.9299 |
11 | 310° | 29.9324 |
12 | 360° | 29.9308 |
2.9 µm |
No. of Point | Points | Mild Delrin Dia. 30 mm |
---|---|---|
3 | A,B,C | 30.0121 |
3 | A,C,B | 30.0099 |
3 | B,A,C | 30.0112 |
3 | B,C,A | 30.0123 |
3 | C,A,B | 30.0133 |
3 | C,B,A | 30.0113 |
3 | a,b,c | 30.0104 |
3 | a,c,b | 30.0109 |
3 | b,a,c | 30.0124 |
3 | b,c,a | 30.0116 |
3 | c,a,b | 30.0128 |
3 | c,b,a | 30.0103 |
Diameter in mm | Hole Circle Error from 3SMVI System in µm | Hole Circle Error from CMM in µm | Difference in µm |
---|---|---|---|
30 | 11.6 µm | 2.9 µm | 8.7 |
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Saif, Y.; Rus, A.Z.M.; Yusof, Y.; Ahmed, M.L.; Al-Alimi, S.; Didane, D.H.; Adam, A.; Gu, Y.H.; Al-masni, M.A.; Abdulrab, H.Q.A. Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques. Appl. Sci. 2023, 13, 11419. https://doi.org/10.3390/app132011419
Saif Y, Rus AZM, Yusof Y, Ahmed ML, Al-Alimi S, Didane DH, Adam A, Gu YH, Al-masni MA, Abdulrab HQA. Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques. Applied Sciences. 2023; 13(20):11419. https://doi.org/10.3390/app132011419
Chicago/Turabian StyleSaif, Yazid, Anika Zafiah M. Rus, Yusri Yusof, Maznah Lliyas Ahmed, Sami Al-Alimi, Djamal Hissein Didane, Anbia Adam, Yeong Hyeon Gu, Mohammed A. Al-masni, and Hakim Qaid Abdullah Abdulrab. 2023. "Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques" Applied Sciences 13, no. 20: 11419. https://doi.org/10.3390/app132011419
APA StyleSaif, Y., Rus, A. Z. M., Yusof, Y., Ahmed, M. L., Al-Alimi, S., Didane, D. H., Adam, A., Gu, Y. H., Al-masni, M. A., & Abdulrab, H. Q. A. (2023). Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques. Applied Sciences, 13(20), 11419. https://doi.org/10.3390/app132011419