Automated Surface Defect Detection Of LPG Cylinders - IIoT Case Study

CASE‌ ‌STUDY‌

AUTOMATED SURFACE INSPECTION OF LPG CYLINDERS TO IDENTIFY DENTS USING 2D CAMERA AND LASER GRIDS (AI VISION SYSTEM)


CLIENT’S PROBLEMS

Defects like dents bend, cuts, dig, etc. were not being identified during the inspection process due to the limited visibility i.e. only one side of the cylinder could be seen by operators 

False acceptance and false rejection cause the prolonged in production time and hence TTM (To the Market) time

Operators manually take cylinders off the conveyor as soon as they see a defect as there is no automated rejection mechanism

Image Acquisition of dented cylinders







PROBLEM IMPLICATIONS

Preventing defective cylinders from reaching the customer is the client's major concern. Improper inspection may result in sending defective cylinders to the customer

Improper inspection may result in sending defective cylinders to gas filling stations as they may store less amount of gas or no gas at all because of the defects

Supplying defective cylinders in the market would hamper the image and profitability


CLIENT REQUIREMENTS

To automate the 360-degree surface inspection of the cylinders with high accuracy and precision with the help of a vision system

Deploy an automated rejection mechanism in the same line to reject and collect all the defective cylinders in one place

To reduce the current inspection cycle time 10 seconds to 1.5 seconds in order to increase production volume

Two different variants need to be inspected i.e. domestic and commercial cylinders


Training AI models for Automated defect detection using AI and Vision System

Read full Case Study

AI Machine Vision | Vision Systems For Quality Inspection

Comments

Post a Comment

Popular posts from this blog

How do you overcome uneven reflections during image processing?

Image
  By  Qualitas Editorial Team May 20, 2022 Quality Control Insights ,  Blogs  No Comments Table Of Contents 1.  Reflections are a common problem in MV image acquisition 2. What are the reasons for reflections causing challenges? 3. How are such challenges overcome? Machine vision  is a technology that uses one or more cameras to analyze and inspect objects automatically. Machine vision has gained wide popularity in industrial and production environments. Data acquired by the machine vision system can be utilized in controlling any manufacturing process or activity. A typical application of machine vision on an assembly line is to capture images of parts passing through it, and process the image to detect defects, faults, or anomalies. It can even detect the color, shape, and size of the object for information storage. Apart from defect detection, machine vision is also used in robot guidance, real-time process control, and data collection. However, to overc...
Read more

Machine Vision in the Paint Shop for Inspecting primer on the coat

Image
Table Of Contents 1. Problem Scenario 2. Challenges and concern 3. Solution 4. Results Quality control is a critical element in the automotive manufacturing industry supply chain with visual part inspection being a heavily relied upon activity in the process. This task has been traditionally carried out by highly experienced and qualified staff. However, the inherent nature of visual inspection and difference in perception between individuals, make defect detection subjective. These inconsistencies have an adverse impact on quality and lower the overall productivity of the plant. One of our clients, a leading car manufacturer, approached us to find a solution to a similar problem. The application uses Machine Vision in the Paint Shop for Inspecting primer on the coat. Problem Scenario In the given scenario, one car passed through the painting station every 30 seconds during the primer coat application process at the paint shop. As the robot applicator deployed for the task performe...
Read more