Improve products quality with artificial intelligence

AI Quick Summary

In the fashion industry, achieving high quality standards of production involves setting various production parameters. As a research partner, we offer state-of-the-art algorithms to improve product quality by exploiting quantitative measures (sensor data, images, or videos) collected during the production stages. If quantitative data is scarce or costly to obtain, we can also optimize the production process by combining our techniques with information extracted by the judgement of experts.We have previous experience with such optimization methods applied to improving the quality of machined metal pieces where we maximized the quality of the product with fewer samples compared to traditional design of experiments. Our method also allows for a hybrid solution, using both numerical measures and the preference of the expert.Our innovation hypothesis is that we can bring this novel technique to the fashion industry, for which we seek an implementation partner. In the fashion industry, achieving high quality standards of production involves setting various production parameters. As a research partner, we offer state-of-the-art algorithms to improve product quality by exploiting quantitative measures (sensor data, images, or videos) collected during the production stages. If quantitative data is scarce or costly to obtain, we can also optimize the production process by combining our techniques with information extracted by the judgement of experts.We have already applied a similar method to improve the quality of machined metal pieces. In that case, an expert operator was asked to choose the best among two products obtained with different production settings. Our optimization method was then used to suggest optimal process parameters and the expert evaluation was repeated sequentially. By following this procedure, we maximize the rating of the operator on the final product with fewer attempts compared to traditional design of experiments. A hybrid solution, using both numerical measures and the preference of the expert, is also possible.Our innovation hypothesis is that we can bring this novel technique to the fashion industry, for which we seek an implementation partner.

Project Idea Metadata

• Project Idea Name: Improve products quality with artificial intelligence
• Date: 2/6/2025 2:49:34 PM
• Administrators:
  • Dario Azzimonti

Project Idea Description

In the fashion industry, achieving high quality standards of production involves setting various production parameters. As a research partner, we offer state-of-the-art algorithms to improve product quality by exploiting quantitative measures (sensor data, images, or videos) collected during the production stages. If quantitative data is scarce or costly to obtain, we can also optimize the production process by combining our techniques with information extracted by the judgement of experts.

We have already applied a similar method to improve the quality of machined metal pieces. In that case, an expert operator was asked to choose the best among two products obtained with different production settings. Our optimization method was then used to suggest optimal process parameters and the expert evaluation was repeated sequentially. By following this procedure, we maximize the rating of the operator on the final product with fewer attempts compared to traditional design of experiments. A hybrid solution, using both numerical measures and the preference of the expert, is also possible.

Our innovation hypothesis is that we can bring this novel technique to the fashion industry, for which we seek an implementation partner.

In the fashion industry, achieving high quality standards of production involves setting various production parameters. As a research partner, we offer state-of-the-art algorithms to improve product quality by exploiting quantitative measures (sensor data, images, or videos) collected during the production stages. If quantitative data is scarce or costly to obtain, we can also optimize the production process by combining our techniques with information extracted by the judgement of experts.

We have previous experience with such optimization methods applied to improving the quality of machined metal pieces where we maximized the quality of the product with fewer samples compared to traditional design of experiments. Our method also allows for a hybrid solution, using both numerical measures and the preference of the expert.

Our innovation hypothesis is that we can bring this novel technique to the fashion industry, for which we seek an implementation partner.