Have you ever imagined using Artificial Intelligence for Quality Testing?

The industrial revolution and the growing technological evolution are transforming the industrial landscape like never before. By combining artificial intelligence, IoT (Internet of Things), and advanced automation, this transformation provides a highly connected and intelligent production environment.

Implementing processes involving AI in companies has become essential to achieving new levels of efficiency, productivity, and competitiveness. With automated processes and real-time analysis, strategic decisions are increasingly data-driven, allowing resources optimization and waste minimization.

One of the main challenges in the industrial sector is to ensure the quality of the final product to be shipped to the customer. If the product does not meet the necessary quality standard, it leads to massive waste, as it will need to be reproduced, sometimes involving tons of material

Industry Quality Test Automation

To address this ongoing need in the industrial sector, supporting companies to have a massive reduction of quality tests, ensuring high confidence in the final product, Habber Tec has developed the solution: Quality Predictor. This solution aims to predict the likelihood of a product passing or failing the quality test.

Quality Predictor is an artificial intelligence predictive model that indicates, before the moment of the Quality Test, the probability that will be obtain in the product quality test. Associated with this model are alerts based on trends to ensure the quality of all products, minimizing deviations that may lead to quality failures, thus reducing waste, associated costs, and accelerating production times.

How does it work?

Quality Predictor anticipates the production line moment where quality tests are performed, indicating whether or not manual verification is necessary. Preventively, it starts sending alerts in advance so that corrections can be made on the line to avoid future quality failures.

The implementation process of this solution involves five phases:

1. Understand existing data on the production line

In this phase, we study available data and assess its quality to identify possible outcomes. It’s essential to gather as much information as possible about the business process and how production works.

2. Setup and Parameterization

This phase involves data cleaning and variable selection to adapt the Quality Predictor to your data and organization’s reality.

3. Test Evaluation

At this stage, we analyse the results of the model adapted to your organization. We use different metrics to share performance, which will then be monitored over time.

4. KPI Evaluation

After evaluation, necessary KPIs are set up to monitor the chosen model’s predictions and later configure dashboard reports to control and assess results and model performance. Alerts and proactive notifications are also configured during this phase.

5. Production Installation

In this phase, we compare historical data with production data to evaluate the model’s consistency, allowing it to be adjusted according to the organization’s needs. Additionally, service configuration is completed to run in the production line, receiving new data from the factory.

Benefits of Smart Industry with AI

Intelligent use of production data brings several benefits that directly impact increased productivity, reduced production time, and operational cost. In addition to the previously mentioned benefits, Quality Predictor also offers:

• Flexibility and efficiency in human and machine collaboration;
• Maximizing employee time;
• Minimization of human error and workload;
• Improving quality and consistency;
• Quick and assertive decision-making.

Success Case

We implemented Quality Predictor in various industrial processes, one of which stands out Volkswagen Autoeuropa, which reduced vehicle production time using quality models with artificial intelligence: See the case “Road Test Predictor” in the video below.

Conclusion

Innovation using artificial intelligence is a significant differentiator for automating industrial processes, as it allows the creation of agile solutions to meet the needs of a constantly evolving market with large volumes of data. Quality Predictor enables the industry to know the quality of the final product in advance, allowing actions to prevent waste and reduce production costs, with the possibility of real-time results monitoring. It also facilitates the analysis and evaluation of results over time.

This solution can help any company in the industrial sector to improve its production quality, if it has historical data to work with.

Accept our challenge and request a free Proof of Concept (POC) with a use case adapted to your organization’s reality!