AI improves anomaly detection precision in consumer goods packaging

Objective

Reduce waste and improve efficiency in production by implementing computer vision AI

Technology

AI for visual inspection
+ Defect detection +
Edge detection +
Machine learning

Industry

Manufacturing + Consumer goods
+ Chemical

Consumer goods providers benefit from AI in visual inspection

Traditional Quality Control (QC) methods that rely on manual inspections and rule-based systems can often be inefficient and prone to errors, especially when it comes to detecting irregular shapes and subtle defects. These methods consume a significant amount of time and labor, and can compromise the precision of product quality.

Specifically prevalent in industrial and consumer goods manufacturing sectors, challenges arise in semi-liquid packet production lines, especially with viscous liquid. The current manual inspection process struggles to identify defects like non-standard contours and barely visible issues such as bubbles and foreign inclusions, resulting in high defect rate. Moreover, the dependence on random inspection often forces the disposal of entire batches upon defect discovery, leading to substantial waste and financial losses. Furthermore, rule-based automatic inspection systems fall short when detecting edge defects due to wrinkles, indicating a notable technological gap.

Addressing these critical issues and leveraging expertise in AI and automated inspection technologies revolutionises QC by automating processes and enhancing defect detection capabilities. AI-powered visual inspection minimises waste, boosts efficiency, and significantly improves the accuracy of quality assessments.

Areas of focus

Conventional QC methods reveal limitations when detecting irregular defects, especially within pliable, amorphous, semi-solid materials. However, integrating AI computer vision solutions has emerged as a remedy, facilitating the identification of diverse irregularities, inclusions, and defects in challenging environments like liquid, soft, or granular materials, wrinkled surfaces, and so on.

AI Computer Vision Solutions

This transformative advancement surpasses the constraints of traditional optical inspection approaches, offering comprehensive defect detection across various material compositions and surfaces, enhancing quality control processes significantly.

Defect detection across various textures and shapes.

Inspection of translucent or semi-transparent objects, viscous liquids, irregular surfaces.

Detection of anomalies and defects such as bubbles, inclusions, tears, dirt, discoloration.

Our approach

Our solution used a text+image foundation model in conjunction with our proprietary pre- and post-processing, offering superior performance over a more traditional approach.

Model training

01

Visual data
extraction

02

Video
labeling

03

Model
creation

04

Bench-
marking

05

Ready for
deployment

Model inference

01

Camera
integration

02

Pre-
processing
step

03

Irregular
shape
detection

04

Post
processing
step

05

Business
Logic

06

Output

Aicadium impact

Aicadium View™ for revolutionises irregular defect detection in consumer goods packaging and manufacturing by transcending the limitations of traditional optical inspection methods.

This tailored solution focuses on minimising scrap items and costs by avoiding the unnecessary discard of entire batches due to a single detected defect. It increases the efficiency and consistency of quality inspection processes, shifting from random checks to automatically inspecting each item meticulously. Implementing this sophisticated inspection system ensures higher accuracy in defect detection, significantly reducing both defect and false positives rates, thereby effectively minimising waste and enhancing production efficiency.

Embracing Aicadium’s technology empowers consumer goods manufacturers to improve customer satisfaction by exceeding existing benchmarks and setting a new standard of quality excellence.

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