In the food manufacturing industry, ensuring product quality is crucial to maintaining customer satisfaction and upholding brand reputation. This case study presents a real-life example of how a company successfully implemented a bad biscuit detection and rejection system using AI and machine learning technology.
The client is a leading biscuit manufacturing company with a wide range of product offerings. They faced a significant challenge in maintaining consistent quality standards, as manually inspecting each biscuit for defects was time-consuming, subjective, and prone to human error.
The client wanted to improve their quality control process by implementing an automated system that could accurately detect and reject bad biscuits in real-time. The system needed to identify various defects such as cracks, misshapen biscuits, discoloration, and other visual abnormalities.
Product Development, Data Architecture, Data Management, Data Analytics, Data Visualization
Cosmos DB, MS Azure, Gremlin, JanusGraph, Cassandra, Java, Python, Kafka, React, Redis
Biscuits are a popular snack enjoyed by people all over the world. However, it is not uncommon for some biscuits to be of poor quality, with defects such as cracks, joint and broken biscuits. These bad biscuits can be a waste of money for consumers and can also damage the reputation of the manufacturer. To address this problem, artificial intelligence can be used for bad biscuit detection and rejection.
One way to use AI for bad biscuit detection is by implementing computer vision algorithms. This involves training a machine learning model to recognize patterns and identify defects in biscuit images. The model can be trained using a dataset of images that contains both good and bad biscuits. Once trained, the model can be used to detect and reject bad biscuits during the manufacturing process.
Another approach is to use contour detection in OpenCV. Contour detection is a powerful technique in image processing that allows us to identify and extract the boundaries of objects in an image. This is done by detecting changes in intensity and color in the image. In biscuit detection, we can use this technique to identify the edges of biscuits in images.
To address the client's challenge, a team of AI and machine learning experts collaborated with the company to develop a robust bad biscuit detection and rejection system.
A large dataset of labeled biscuit images was collected, comprising both good and bad biscuit samples. This dataset served as the foundation for training the AI model to accurately distinguish between defects and normal variations.
Using advanced deep learning techniques, an AI model was developed and trained on the collected dataset. The model learned to identify complex visual features and specific patterns associated with cracks, breaks, and discoloration.
The AI model was seamlessly integrated into the client's production line. A high-speed industrial camera continuously captured images of each biscuit as it passed through on the conveyor, analyzing frames in real-time.
Upon the AI system detecting a bad biscuit with high confidence, an automated rejection mechanism (such as an air jet or robotic arm) was instantly triggered, removing the defective biscuit from the line without halting production.
The implementation of the bad biscuit detection and rejection system yielded significant results and benefits for the client, revolutionizing quality control processes in the food manufacturing industry.
Accuracy Rate
Reduction in Maintenance
Client Satisfaction
The AI-powered system provided highly accurate and consistent detection of bad biscuits, surpassing the capabilities of manual inspection.
The automated system streamlined the quality control process, eliminating the need for manual inspection and reducing labor costs.
By preventing the distribution of bad biscuits, the client saved costs associated with recalls, customer complaints, and potential brand damage.
Ensuring a higher quality standard of biscuits increased customer satisfaction, loyalty, and brand trust.
