The document presents the development of a machine vision system using deep learning to inspect gear profiles for defects. It aims to develop an automated inspection method that is more accurate and efficient than human inspection. The proposed system uses a novel image acquisition system and image processing methods to detect various gear defects. Experimental results show the vision system achieves high accuracy that is comparable to skilled human inspectors. References are provided on related work using computer vision and machine learning for defect detection.

1. DEVELOPMENT OF GEAR PROFILE
INSPECTION USING DEEP LEARNING

2. PRESENTED by
PRASAD S. KADI – MEBA37
SURAJ S. JUGDAR – MEBA57
AKASH B. SUPE – MEBA56
BHOJRAJ A. SONAWANE – MEBA55
Department of Mechanical Engineering
Sinhgad Academy of Engineering, Kondhwa-Pune
Savitribai Phule Pune University,
Pune-411048.

3. ABSTRACT
• Appearance defects inspection plays a vital role in gear quality
control. Human inspection is a traditional way to remove defective
gears, which is instable and time consuming.
• we develop a machine vision system for gear defect inspection,
which can inspect various types of defects on gear, such as
deformations, rusts, scratches and so on.
• The proposed system designs a novel image acquisition system to
enhance the defects appearances and get controlled image
acquisition environment.
• A series of image processing methods are proposed or utilized to
inspect the defects.
• The proposed system is evaluated and compared with skilled
human by the recall, precision and F-measure. Experimental results
show that the proposed vision system has high accuracy and
efficiency

4. INTRODUCTION
• In the industry of machinery, gears are important
components that connect different machine parts to
transmit motion.
• They have been widely used in cars, and many other
rotating machines. The quality of gears can directly
influence the performance of many machines, and
may even cause serious disasters.
• Gears are usually mass-produced with high demand
of precision, and a lot of inspection measures have
been adopted in the production process to ensure the
quality of gears.

5. TYPES OF GEAR DEFECTS

6. LITERATURE SURVEY
Sajjad Taheritanjani, Ralf Schonfeld, Automatic
Damage Detection of Fasteners in Overhaul
Processes, 2019 IEEE 15th International
Conference on Automation Science and
Engineering, August 2019 [1]
In this paper, they propose an automatic damage
inspection of the fasteners, using computer
vision and machine learning. They built a setup
to automatically record and preprocess the data
and compared multiple supervised and
unsupervised machine learning models for
detecting damages of 12 different fasteners.
Xian Tao , Dapeng Zhang , Wenzhi Ma , Xilong
Liu and De Xu, Automatic Metallic Surface
Defect Detection and Recognition with
Convolutional Neural Networks, Applied
Sciences 8(9):1575 September 2018 [2]
This paper discusses the automatic detection of
metallic defects with a two fold procedure that
accurately localizes and classifies defects
appearing in input images captured from real
industrial environments. Metallic defects under
various conditions can be successfully detected
using an industrial dataset. The experimental
results demonstrate that this method meets the
robustness and accuracy requirements for
metallic defect detection.

7. Xiaohong Sun, Jinan Gu, Shixi Tang and Jing Li,
Research Progress of Visual Inspection
Technology of Steel Products, Applied Sciences
8(11):2195, November 2018 [3]
The purpose of this article is to study the latest
developments in steel inspection relating to the
detected object, system hardware, and system
software, existing problems of current inspection
technologies, and future research directions. The
paper mainly focuses on the research status and
trends of inspection technology. The network
framework based on deep learning provides
space for the development of end-to-end mode
inspection technology, which would greatly
promote the implementation of intelligent
manufacturing.
Limei Song , Xinyao Li , Yangang Yang , Xinjun
Zhu , Qinghua Guo and Huaidong Yang,
Detection of Micro-Defects on Metal Screw
Surfaces Based on Deep Convolutional Neural
Networks, Sensors (Basel), v.18(11), October
2018.[4]
This paper proposes a deep convolutional neural
network (CNN) -based technique for the
detection of micro defects on metal screw
surfaces. The defects we consider include surface
damage, surface dirt, and stripped screws.
Images of metal screws with different types of
defects are collected using industrial cameras,
which are then employed to train the designed
deep CNN.
P.Arjun and T.T.Mirnalinee, Machine parts
recognition and defect detection in automated
assembly systems using computer vision
techniques, Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº
1, 71 - 80, 2016. [5]
This paper presents a computationally efficient
2D computer vision based approach to recognize
the machine parts and detect damaged parts on
the assembly line.

8. PROBLEM STATEMENT
• After the manufacturing process of Gears,
to maintain the quality of the product,
inspection of the manufactured gears is
necessary.
• But precise inspection of such high
quantity gears is the difficult task. So to
inspect the bulk quantity of manufactured
gears this project is invented.

9. OBJECTIVE
 To inspect the manufactured Gears.
 To study the nature of defect using the data collected
during the ML process.
 To design a CATIA model.
 To carry out the experimental testing & draw the results
& conclusion.

10. 3D MODEL OF SYSTEM

11. 3D MODEL OF SYSTEM
RACK AND
PINION
CAMERA
GEAR
COVEYOR BELT
SLIDING ARM
OK BIN
REJECTION
BIN

12. CALCULATIONS
• Conveyor belt:
1. Speed of belt:
V= 3.14*D*N/60 m/s
V=3.14*0.230*30/60
V=0.361 m/s is the speed of belt.
2. Length of belt:
L = 2.C + 3.14(D+d)/2 + (D-d)^2/4C
C = center to center distance.
D= large pulley diameter.
d=small pulley diameter.
L = 2*0.85 + 3.14(0.230+0.230)/2+ 1/(4*0.85) * (0.230-0.230^2)
L=1.7 m this is length of belt.

13. • Torque of motor:
T = ½ D (F+μ Wg)
T = Torque
D = roller diameter
W =mass of load
g = gravity acceleration
μ = friction coefficient
f = external force
T = ½ * 0.230 * (10+ 0.05* 1*10)
T = 0.5* 0.230(10+0.05*10)
T = 0.5*0.230(10+0.5)
T = 0.5*0.230*10.5
T = 1.2075 NM torque of motor.

14. • Motor calculations:
power=120W
speed =30 rpm
V =12V DC
P = 2*3.14*n*T/60
120 = 2*3.14*30.7/60
120*60/2*3.14*30 =T
T = 38.197NM

15. SEMESTER I
• We started our work with a literature survey.
• Search many research papers from various articles and published
journal papers.
• Reference sites:
– http://explore.ijert.org/
– http://www.ijetcse.com/
– http://industrialscience.org/
– http://www.ijist.net/
• Worked on diff. Mechanisms that can be useful for our project.
• We have done a rough 2D sketch of model in Auto-CAD.
• After getting rough model we started calculation of some
components.
• We selected standard components.
• Simultaneously we have done work of report for semester I.

16. PLAN OF PROPOSED WORK
Sr.
No
Activity/month July Aug Sep Oct Nov Dec Jan Feb Marc
1 Search of topic
2 Selection of topic and research papers
3 Finalizing of project
4 Literature review
5 Basic diagram and study of components
6
Cad diagram and starting the calculation of
components
7 Calculations
8
Finalizing the calculations and preparing the final
cad diagram with dimensions
9 Starting manufacturing
10 Buying the standard components from market
11 Testing of model
12 Rough draft of report
13 Final report

17. CONCLUSION
• This study develops a machine vision system
for the inspection of gear surfaces. In the
system, a novel inspection algorithm is
proposed for the inspection of defects on gear
surfaces.

18. REFERENCE
• Sajjad Taheritanjani, Ralf Schonfeld, Automatic Damage Detection of
Fasteners in Overhaul Processes, 2019 IEEE 15th International Conference
on Automation Science and Engineering, August 2019 [1]
• Xian Tao , Dapeng Zhang , Wenzhi Ma , Xilong Liu and De Xu, Automatic
Metallic Surface Defect Detection and Recognition with Convolutional
Neural Networks, Applied Sciences 8(9):1575 September 2018 [2]
• Xiaohong Sun, Jinan Gu, Shixi Tang and Jing Li, Research Progress of Visual
Inspection Technology of Steel Products, Applied Sciences 8(11):2195,
November 2018 [3]
• Limei Song , Xinyao Li , Yangang Yang , Xinjun Zhu , Qinghua Guo and
Huaidong Yang, Detection of Micro-Defects on Metal Screw Surfaces
Based on Deep Convolutional Neural Networks, Sensors (Basel), v.18(11),
October 2018.[4]
• P.Arjun and T.T.Mirnalinee, Machine parts recognition and defect detection
in automated assembly systems using computer vision techniques, Rev.
Téc. Ing. Univ. Zulia. Vol. 39, Nº 1, 71 - 80, 2016