{"id":17171,"date":"2024-01-10T12:42:46","date_gmt":"2024-01-10T11:42:46","guid":{"rendered":"https:\/\/www.predictland.com\/casos-de-exito\/productivity-improvement-and-quality-defect-detection\/"},"modified":"2025-11-19T12:20:55","modified_gmt":"2025-11-19T11:20:55","slug":"productivity-improvement-and-quality-defect-detection","status":"publish","type":"caso-de-exito","link":"https:\/\/www.predictland.com\/en\/case-studies\/productivity-improvement-and-quality-defect-detection\/","title":{"rendered":"Productivity improvement and quality defect detection"},"content":{"rendered":"\n
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PredictLand is developing for the multinational automotive company Stelllantis an Artificial Intelligence application to detect manufacturing defects, which will be used for predictive maintenance and machine configuration optimization.<\/p>\n\n\n\n

Customer<\/h2>\n\n\n\n

Stellantis <\/a>is an automotive multinational with 14 iconic automotive brands (Opel, Peugeot, Citr\u00f6en, Jeep, Maserati, Fiat, etc.). It has industrial operations in more than 30 countries and customers in more than 130 markets, reaching a turnover of 180 billion Euros in 2022.<\/p>\n<\/div>\n\n\n\n

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Challenge<\/h2>\n\n\n\n

In the press process for the manufacture of different vehicle components, it is very complex to detect quality defects in the final part and identify the factors that cause them, generating scrap costs and bottlenecks. Stellantis is interested in a system that allows it to reduce these quality defects and costs. <\/p>\n\n\n\n

Solution<\/h2>\n\n\n\n

Artificial Intelligence solution that captures images of the manufactured parts, and by means of Computer Vision techniques identifies quality defects, and then, by means of Machine Learning techniques, analyzes the configuration of the pressing process and identifies the correlation between the quality defects detected with Computer Vision and the press parameters.<\/p>\n\n\n\n

Among others, data related to real-time information from sensors on press behavior, physical parameterization of the presses, defects produced in the parts, and images of the finished product will be used.<\/p>\n\n\n\n

Results<\/h2>\n\n\n\n

With the results, Stellantis expects to achieve an increase in OEE, reduction of spare parts stock and scrap, reduction to 0% of manufacturing defects due to cracking, reduction of time between changeovers by more than 20%, increase in machine availability by about 5%.<\/p>\n\n\n\n

As well as the generation of recommendations for process and preventive maintenance through AI analysis.<\/p>\n\n\n\n

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The project, called Mepro Lipre, has been supported by the Ministry of Industry, Trade and Tourism, as well as by the European Union through the Recovery, Transformation and Resilience Plan; and has received funding from the Ministry of Industry, Trade and Tourism within the support program for AEIs to contribute to improving the competitiveness of Spanish industry.<\/p>\n\n\n\n

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