REFERENCE AI·Big Data

REFERENCE

Based on over 20 years of experience in building smart factories,
DL Information Technology Co. Ltd. is creating manufacturing big data analysis
and AI application cases in various industrial fields

AI/Big Data Construction Case_
Establishment br.mobof a heterogeneous mixing defect prevention system through machine vision data learning for automobile parts manufacturers

DL Information Technology Co., Ltd. has built and is currently operating a heterogeneous mixing defect prevention system through machine vision data learning for automobile parts manufacturers as a manufacturing data AI problem-solving solution demonstration project

Raw data Analysis environment/Technology Data collection/Loading Visualization

Raw Data

  • 127 screw threads 30 degrees
  • 227 screw threads 45 degrees
  • 328 screw threads
  • 431 screw threads
H/Shaft top photo taken during manufacturing process (black and white)
Image resolution: 5MP (2592 × 1944)
Can be divided into 27 screw threads 30 degrees, 27 screw threads 45 degrees, 28 screw threads and 31 screw threads, and there are several types of products for each category
Each product has a different height, which makes lighting and camera focus different
A new product photo is collected every 30 seconds, resulting in about 1TB of original data

Analysis environment / technology

Test ResNet, VGG, YOLO, Inception deep learning models and SVM machine learning models to select the optimal model
Characteristics of Inception V4 model - Stabilizing deep neural network training by introducing Residual Connection - Optimizing the existing Inception module to achieve high efficiency using fewer parameters and shortening learning and inference time

Data Collection / Loading

When the product is located under the camera, PLC transmits a shooting signal to the PC connected to the camera
Camera shooting
Save the image as a work date_time_sequence_item number.bmp file

Visualization

  • Real-time AI detection screen for incorrect items

  • View history of incorrect item detection

Paint Point
  • Visual quality inspection

  • Occurrence of
    defective products

  • Low detection rate
    of incorrect items

  • A lot of time required

Defective products based on visual quality inspection
Sampling jig inspection performed because the worker cannot visually determine each product
Low detection rate of incorrect items
Existing quality time = (Existing jig quality inspection time + model change time) = 11.68 seconds
Introduction effects
  • Improved quality inspection accuracy
    Total inspection through vision inspection, resulting in 99.2% accuracy in detecting incorrect items
  • Reduced quality inspection time
    Improved quality inspection time = 0.7 seconds
    (average of 6,440 analyzed data)
  • Reduced claim processing cost
    Reduction of costs wasted for handling customer claims when shipping incorrect items due to reduction of shipping rate
    of incorrect items
Client company