Manufacturing - Automating defect detection with AI
Manual checks are very time-consuming and costly
Manual inspections present the risk of missing minor defects undetectable by human eyes
Apply lean methodology to increase the speed and efficiency of the training process of the AI model
Intuitive labeling interfaces and tools to create datasets of clean, accurately labeled images
Machine-learning in a loop
Automated labeling predictions
A global manufacturing company was exploring ways to reduce the human effort of manual inspection of defaulted products due to the high cost and lead time, causing bottlenecks and delays in the assembly lines.
The company looked into automated defect detection with artificial intelligence (AI), and Kili Technology was involved in training the AI to detect defective products through data annotation and machine learning loop in the process.
As a global manufacturing company headquartered in France, quality assurance on the assembly line is one of the utmost importance to ensure perfection within the promised lead time delivery for customer satisfaction worldwide. However, as the company was doing manual quality checks with human inspectors one by one per unit product, the process became heavily time-consuming, costly, and required huge efforts that could take hours. This resulted in production bottlenecks and increased lead time. On top of that, manual inspection contained the risk of missing minor defects in the component unit, where each unit is small hence scratches or broken parts are sometimes undetected by human eyes.
Defaulted components often went unnoticed and got assembled with other components, and became defective products. What’s next: customer complaints, returns, and replacements, costly consequences.
To build the AI model that can accurately detect defective products in assembly lines as the company envisioned, it found several challenges, which mostly involved the AI model training process. One challenge was to build excellent quality training datasets that consist of cleanly labeled images and videos of well-conditioned and defective products so that the AI model could identify defects and act properly.
As we are used to being lean, there should be a unique 80/20 method to be efficient while still being rigorous in the quality when training the AI model (for defect detection). We were unsure what is the effective way to do this
Another challenge is to significantly increase the speed and efficiency of the training process of the AI model. As the manufacturing company adopted lean production as its manufacturing philosophy, the company struggled to find ways to apply this value to be more efficient on AI model training – to achieve the same level of accuracy with less training data.
As the challenges focus on being lean to achieve an excellent AI model performance at scale, the manufacturing company understood that it needed to find a partner that could offer simplicity and efficiency while delivering a high quality of training data. The company first explored Kili through the free trial offer and tested the tool.
We tested the tool rigorously initially on our own. Then, after the introduction, Kili set up a dedicated testing project for us and even shipped features specific to our needs. Certainly, I was impressed.
Deciding to partner with Kili afterward, the company found that the simplicity of the tool helped to be productive in quickly building training datasets for its AI model for defect detection. The customizable interface and features addressed directly to the company need to make it intuitive to navigate, improving efficiency.
Furthermore, the company was excited to see that machine learning in the loop – notably active learning when annotating data on the Kili platform – became the efficient 80/20 method the company needed to train the AI model with 30% fewer data to achieve high accuracy of performance. In addition, Kili facilitated the company to leverage online learning to create automated predictions in the labeling process, doubling up the data training speed.
The newly enabled product defect prevention also facilitated the company to significantly reduce the number of products returned by customers, demand for reimbursement, or product replacement.
We have prevented faulty components to be assembled and defective products to be shipped – which allows us to reduce cost by 25%. I certainly see the impact, positive results are here.
Leveraging artificial intelligence for defect detection in manufacturing could significantly prevent human errors with less time and effort, avoiding production bottlenecks
Accurate AI defect detection as a preventive measure in manufacturing could result in impactful cost savings. To achieve this, training the AI model with accurately labeled data is crucial.
A unique solution to build training datasets with labeling specifically addressed to manufacturing defect detection will greatly improve efficiency and productivity as positive results
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