ML6 Solution for manufacturers

AI Machine Vision Solutions

We help you automate complex production and quality control
processes with cutting-edge AI solutions

Trusted by leading manufacturers in Europe

Solution overview

AI Machine Vision

AI software presents new opportunities for manufacturing companies to bridge the gap between quality control, production efficiency and process automation.

We can collaborate with local hardware partners (e.g. camera, sensors) and machinery manufacturers, to guarantee seamless integration for your end-to-end retrofit solution.

Advanced machine learning models enable us to solve much complexer vision use cases compared to traditional machine vision, unlocking new process automation opportunities.

Why we use machine learning for computer vision projects:

10x faster image labelling

Full defect detection traceability & transparency

Proficient in complex object processing and quality parameters

No constant retraining for product variations

Scalable & robust

Quality insights & alerting

Monitor product quality in real-time and get insights for future root cause analysis (RCA). This enables proactive interventions, ensuring consistent product quality and minimal machine downtimes." Alert operators for urgent interventions.

Automated defect removal

In many cases, complex products still need manual defect removal, even with quality grading. We offer operators assistance by automating some tasks or having them focus solely on validating our automated defect removals.

Machine process steering

Getting insights into detailed aspects of production, such as defects at various stages, allows for swift adjustments to machine settings like heating times and temperatures. We can assist in leveraging this data to enhance quality, speed, and efficiency in production with AI digital twins.

Use case examples

Here are some complex use cases we've solved for clients:

Carpet folding - machine process steering

Detecting rug edge lines with our AI model, enabling precise folding despite texture, pattern, and colour variations, enhancing production efficiency.

Steel phosphage coverage - microscopy analysis

Splitting microscopic SEM images into multiple parts to classify phosphate coverage on steel wire, leveraging quantitative metrics to adjust production parameters automatically.

Wooden plank - defect detection & removal

Identifying wooden plank defects, halting production for critical issues caused by machine failure. Saving time and costs, reducing waste, improving quality and providing machine improvement insights.

Automated vial inspection

Developing an application using hyperspectral sensing for lab scientists to detect and classify vial defects, achieving 98.4% accuracy.

Palletisation defect removal

Using cameras and depth sensors with an algorithm to identify and reject defect pallets, preventing downtime in the packaging pipeline and avoiding safety risks, export delays, and fines.

Business value

How AI makes the difference

Traditional methods and machine vision struggle with complex defects and underutilise quality insights. Our AI solutions offer an economical way to deliver value fast, while tailoring to your needs where customization is essential.

Implementing AI (Machine Vision) offers many benefits including:

Minimized production waste and machine downtime

More consistent product quality

Enhanced quality inspection efficiency and accuracy

Higher production throughput

Data-driven continuous improvement cycle

Customer stories

Success Stories

Wienerberger & Cosmec (Refractory)

Transforming Wienerberger’s Quality Control with AI

In collaboration with Cosmec, we’ve deployed a system that proactively detects and removes defect bricks, considering complex natural product fluctuations, to enhance production efficiency, reduce costs, minimize waste, and improve product quality.

CNH Industrial (Agriculture)

Deploying AI algorithms on a multispectral camera for CNHi

We’ve implemented an object segmentation algorithm capable of detecting broken rice, sticks, needles and leaves. With the purpose to automate waste detection in rice, adjusting combiner settings accordingly. This increases harvest amount and quality for farmers, providing CNHi with a significant competitive edge.

Balta Group (Textile)

Optimizing Balta’s rug production with AI-driven machine steering

We’ve been helping Balta with multiple automaton projects, with carpet folding automation being one of them. The system is able to detect rug edge lines with our Vision AI model, enabling precise folding despite texture, pattern, and colour variations, enhancing production efficiency.

UNILIN

Improving production processes with computer vision

Over the past few years, ML6 has collaborated with Unilin to help their team to overcome complex computer vision challenges, achieving pixel-perfect accuracy and robustness across different product types. Hear more about the project from Michiel Van Acker (Unilin) as he explains the incredible impact of artificial intelligence in improving their production processes.

Implementation

Implementing & Integrating the Solution

Our unique approach results in a tailored solution based on modular building blocks. This way, we offer the best of both worlds: we customise where needed and provide the cost-effectiveness and scalability of a product.

Capture

We provide the hardware to capture the images of your products, fitted to your needs. We install and monitor them and guarantee their operations.

Inspect

Our ML core processes the captured images using a custom model to detect or classify the products according to your use case.

Tailored

Training

We train a custom machine learning model on your data to meet all your quality inspection needs.

Integrate

We integrate with your PLC’s to communicate the inspection results and send steering instructions.

Tailored

PLC integration

We build a bespoke integration with your machines to communicate, intervene and steer.

Report

We show production metrics on standard reports and dashboards.

Tailored

Labelling and retraining

We expose a custom labelling and retraining setup to support a continuous improvement cycle for your quality inspection process

Maintain

We provide maintenance and support for the solution to guarantee security patches and future functionality upgrades.

Implementation Timeline

We swiftly deliver value with a pre-FAT consultation, FAT approval, hardware integration, 3-4 weeks of data collection, ML model training, a two-week pilot, and SAT within three months, with acceptance criteria outlined beforehand.

Contact us

Get in touch

Stan Callewaert

Manufacturing Business Consultant

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