Solutions

AI Quality Control Toolkit

Reduces the manual labor required to detect quality problems in manufacturing.

Wasting too much effort on visual quality inspection?

The average accuracy of manual inspection is under 85%, and results in high level of employee burnout. Additionally, creating ML models for Artificial Vision with current technologies is a very complex problem that requires a high level of expertise.

Facing challenges to scale up your operations?

Facing challenges to scale up your operations?

Manual or traditional machine vision inspection approaches make it hard for enterprises to scale up their operations: it is very resource intensive, or very costly due to the need for highly specialized labor.

disadvantage

Knowledge transfer takes time.

disadvantage

Low process consistency.

Are you thinking of further automation beyond Visual AI?

Are you thinking of further automation beyond Visual AI?

Not being able to link the results from your Visual AI stage to other metrics in the production line prevents you from achieving additional benefits in terms of automated root cause analysis of manufacturing problems and preventive maintenance.

disadvantage

Unexpected downtime of your manufacturing lines.

disadvantage

Lack of consistent failure root cause analysis.

disadvantage

Poor quality control model.

AI Quality Control Toolkit overcomes the challenges of industrial digital transformation

Video Smart Factoryplay video

What benefits can you expect with AI Quality Control Toolkit?

90%

Less manual labour

95%

Detection Accuracy

100%

Consistency

< 6"

Prediction latency

Improve quality control in your manufacturing lines

Watch our 3-part video series to see the AI Quality Control Toolkit PoC implemented with Premo, a leading automotive parts manufacturer.

Grupo Premo
video Training module: creation and training of AI models for non-AI expertsplay video

Training module: creation and training of AI models for non-AI experts

advantage

UI-based; easy to create AI models for different parts.

advantage

Detection & classification capabilities leveraging state-of-the art Convolutional Neural Networks (CNNs).

advantage

Creation of whole-part or specific component models, to enable further investigation.

advantage

Active Learning capabilities to facilitate labeling work.

video Real-Time Monitoring: AI predictions and traceabilityplay video

Real-Time Monitoring: AI predictions and traceability

advantage

Centralized solution, works with any type of camera.

advantage

Real-time prediction of parts (under 6s).

advantage

Keeps track of each part prediction for analysis and retraining.

advantage

Increases inspection accuracy and prevents stopping of production lines.

video Centralized Dashboard: quick overview of company's performanceplay video

Centralized Dashboard: quick overview of company's performance

advantage

Individual and aggregated statistics at different levels (production line, factory, country, company).

advantage

Drill-down capabilities on each manufacturing facility and production line.

advantage

Possibility to add other metrics to monitor performance and perform Root Cause Analysis of problems.

advantage

It lays the foundations for implementation/integration with a real-time MES (Manufacturing Execution System).

Get a demo of AI Quality Control Toolkit today

Get started and request a demo to learn how AI Quality Control Toolkit can help you.

Frequently Asked Questions

Below you will find answers to the most common questions about AI Quality Control Toolkit.

How does Tupl AI Visual Inspection Toolkit work?

ampliar

This quality control solution for automating manufacturing lines uses cutting edge deep learning-based technologies to detect and classify anomalies found in the manufactured parts, with processing times up to 6s, enabling real time actuation on the production line. The solution allows keeping track of each part prediction for model performance analysis and retraining.

The centralized dashboard allows an overview of the company's metrics and performance, which results in accurate and early detection of production rate deviations, thus saving time and money.

How does Tupl SaaS work?

ampliar

AI Visual Inspection Toolkit SaaS is delivered in cloud service (e.g. AWS, Azure, etc.) and can also be deployed on-premises, in your private cloud, or data center.

Get started with a functional solution in operation within 2-3 weeks. Monthly subscription. No strings attached. Stop at any time.

What are the examples of AI-based visual inspection applications?

ampliar

Tupl AI Visual Inspection Solution helps build intelligent visual quality control systems with human-level accuracy and without deep diving into coding.

Our pre-built application is designed for product or damage defects detection and can be adjusted to your manufacturing challenges.

How accurate are the ML models?

ampliar

The more images are used for the model training - the higher the algorithm's accuracy. Our algorithms, tested on Premo's manufacturing lines, have reached 95% of detection accuracy, while the average accuracy of manual inspection is under 85%.

Are the machine learning models self-trained?

ampliar

ML Performance Drift Detection and Correction with Active Learning features a mechanism to identify drift in a machine learning model over time and subsequently correct it by retraining, with the help of an active learning module. The machine learning models are self-trained and 100% consistent.

Manual vs Automated Visual Inspection?

ampliar

Automated visual inspection provides significant, long-lasting benefits to manufacturers. AI Quality Control Toolkit reduces 90% of manual labor; therefore, it decreases the overall cost of production and essentially increases the revenue. The business case for implementing vision systems is recognized on a return on investment (ROI) basis. Contact us now, and we can go through calculations based on your data.

Cloud VS On-Premise?

ampliar

In the digital manufacturing environment, introducing AI into the manufacturing process has become possible due to the cloud computing capabilities of Industry 4.0. Modern manufacturers leverage this development to marry computer vision hardware along the production line with AI-powered cloud-based digital tools.

How much data / how many images do I need to get started with AI Quality Inspection tool?

ampliar

An inspection line can be set up with just tens of images to build the AI model. The more data is used as input, the more accurate the model will be, and the deep learning models will learn from any additional data, improving accuracy over time.

Do I need an AI expert or developer on staff?

ampliar

No-code solutions for AI application development help manufacturers take advantage of this emerging technology without the need to hire technical specialists or investing significant time and capital.

Tupl's software has a simple and intuitive user interface that enables existing personnel to build Vision AI applications for quality assurance in very little time with relatively small datasets and with no programming required.

Get a demo of AI Quality Control Toolkit today

Get started and request a demo to learn how AI Quality Control Toolkit can help you.

tupl automation by AI for Network Operations

Operations made simple with AI

Associations we belong to:

Technology Park of Andalusia TuplTelecom infra project Tupl5G Open Innovation LabAmetic la voz de la industria digitalOur EU values has been the seed to transform an idea and a dream proposed 20 years ago into a real bottom-up movement lead by the Global StartupCities initiativeAtlas Tecnológico es la plataforma donde conectar con aquellos agentes que generan innovación en los sectores de la industria 4.0.