EXPERIMENT - 9
Devansh Sharma (22IM10015)
Inala Jahnavi Mehar Sai (22IM10016)
Jatin Gupta (22IM10018)
Jishnu Mukherjee (22IM10019)
Objectives of the Study:
(i) To explore state-of-the-art machine learning (ML) tools for
object detection
(ii) Identify object characteristics using image processing
(iii) CUSUM Chart
Measuring Instruments/Apparatus/Software Required:
Hardware:
1. Conveyor belt
2. Hikvision camera
3. ESP 32 motor control unit
4. Wifi Router
5. Workstation
6. Washer
AI/ML Tools:
1. Yolo V8
2. Roboflow
3. Byte track
Coding platform:
1. Python (Google colab and desktop)
Connectivity Tools:
1. Rest API
Measurement Tools:
1. Vernier caliper
Methodology
Measurement:
Using the program measure the length of the diagonal of each of
the bounding boxes, around the washers.
Collect observed value of for several washers. Determine the
outer diameter of the same washers using vernier calipers.
Establish the relation .
Tasks
1. Recall the model created for Experiment No. 5. Use the model
to detect objects from the video
file “output_roi.avi”.
2. Upload a video file on the MS Teams channel demonstrating
detection of washers using
YOLO V8, in the video, show green bounding boxes for washers
with outer diameters within
specification limits, and red bounding boxes for non-conforming
washers.
3. Create a tabular CUSUM chart with the collected data and
identify the instance from which the process started producing
non-conforming units. Assume average of first 10 values,
σ=standard dev of first 10 values.
4. Write a python program for generation of CUSUM chart,
indicating control limits, and origin of shift in the process.
INFERENCE:
Effectiveness of Object Detection:
The YOLO V8 model proved very effective in detecting washers,
accurately placing bounding boxes around them in real-time. This
high detection accuracy shows that the model is well-suited for
real-time inspection tasks in an industrial environment, where
reliable detection is critical.
Accuracy in Measurement:
The measurements of the bounding box diagonals gave a useful
initial estimate of washer dimensions, which we then
cross-checked with measurements taken using a vernier caliper.
The close match between these measurements shows that the
model is robust in detecting size variations, making it a dependable
tool for identifying dimensional inconsistencies.
CUSUM Chart Analysis:
The CUSUM chart was successful in tracking the stability of the
process. It helped us identify the point at which washers started
to deviate from specified limits, indicating a possible production
issue. This shift detection highlights the value of the CUSUM
chart as an early warning tool, which could allow for quick
adjustments and help maintain quality.
Impact of Model and Measurement:
Using machine learning together with traditional measurement
tools provided a well-rounded quality control system. This
approach not only ensures accurate detection but also improves
the precision of our dimensional assessments, enabling us to
efficiently spot non-conforming washers.
Overall Implications:
Combining AI with CUSUM control charts in quality control shows
strong potential for maintaining consistent product quality. This
approach can easily be scaled and applied to other types of
production lines, offering a pathway to more effective quality
control processes across various manufacturing setups.