Automating defect detection with AI in manufacturing company

Automating defect detection with AI in manufacturing company

Key Impact

  • 25 percent

    cost saving due to preventive AI defect detection

  • 2x

    the speed to build AI training dataset through online learning

  • Production efficiency and employee productivity


A global manufacturing company was exploring ways to reduce 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.

Detecting thin cracks and scratches with computer vision

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.” Greg, Quality Assurance Director.

Artificial intelligence is considered to be one feasible solution to significantly improve the process and therefore reduce the impact of defective products to customers by both preventive and reactive measures. Using a computer vision model, defect detection on component level would prevent the defaulted unit to be assembled to become a defective product. On top of that, defective products are also detected to be taken away directly with robotic arms from being packaged. To implement this solution, the company needed to ensure that the AI model is trained well to deliver an accurate detection of defects.

The challenges of building an AI defect detection

To build the AI model that is able to 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 labelled images and videos of well-conditioned and defective products, so that the AI model could identify defects and act properly.

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 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

– Mélanie, Head of Digital Transformation.

Why Kili Technology

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.

– Mélanie, Head of Digital Transformation.

Deciding to partner with Kili afterwards, the company found that the simplicity of the tool really helped to be productive to quickly build training datasets for its AI model for defect detection. The customisable 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 Kili platform – became the efficient 80/20 method the company needed to train the AI model with 30% less data to achieve high accuracy of performance. In addition, Kili facilitated the company to leverage online learning to create automated predictions in the labelling process, doubling up the data training speed.

“Positive results are here”

Having implemented the AI automated defect detection systems in its assembly lines, the manufacturing company realized the positive impact on cost saving and increased employee productivity. The AI model also pulled up the rate of defects found in components by 50% compared to manual inspection by humans, preventing them from being assembled to become faulty products.

The newly enabled product defect prevention also facilitated the company to significantly reduce the amount of product returned by customers, demand for reimbursement or product replacement.

We have prevented faulty components to be assembled and defective products to be shipped – which allow us to reduce cost by 25%. I certainly see the impact, positive results are here.

– Greg, Quality Assurance Director.

Lesson Learned

  • Leveraging artificial intelligence for defect detection in manufacturing could significantly prevent human errors with less time and efforts, avoiding production bottlenecks
  • Accurate AI defect detection as a preventive measure in manufacturing could result in impactful cost saving. To achieve this, training the AI model with accurately labelled data is crucial.
  • Unique solution to build training datasets with labelling specifically addressed to manufacturing defect detection will greatly improve efficiency and productivity as positive results