AI and industry, a perfect alliance. Companies in the manufacturing sector have an increasingly demanding goal: to deliver tangible benefits in terms of efficiency and automation, thereby reducing multiple costs. The challenges to achieving this result are numerous.
AI and manufacturing, the areas of application
Artificial intelligence can be instrumental in various areas: from complexity management to maintenance and predictive quality to workforce productivity and logistics optimization.
- Complexity management : AI is a facilitator par excellence of complexity management, both in the technological and process sense. Concerning the technological part, AI places itself between man and machine, thus bridging the technical gap. However, when it comes to the process, it makes various tasks and activities easier. This is why we are talking more and more often about Double Digital , a digital and dynamic representation model of an entire production line or a single factory which offers the possibility of actually measuring the state of health of the machines, of intercept possible problems, carry out simulations and manage a large number of cases.
- Predictive Maintenance And Predictive Quality : artificial intelligence makes it possible to access and create value from all the data produced by the installation, the machine and the sensors. Through careful analysis of the data and identification of the patterns it contains, we are then able to identify hypothetical faults or malfunctions in advance and therefore intervene in the most appropriate way to avoid production stoppages. With predictive quality, we move from machine to finished product to streamline the quality control process.
- Workforce productivity : with the intervention of AI, which implies automation, it is possible to relieve human operators of repetitive, low value-added and highly time-consuming tasks by focusing on more relevant activities.
- Inventory & logistics : Machine learning models allow you to optimize the entire product traceability process in warehouses, spaces and logistics. Let's imagine factories in which part of the logistics does not involve human beings, but machines that move autonomously and which, thanks to AI, are able to manage the shipping and organization of all products in the factory in a much more efficient and orderly manner than would be possible through human operation, managing spaces more efficiently.
Image metadata tagging and defect detection, how they work
To support companies in the manufacturing sector in optimizing costs, deadlines and resources, we create Computer Vision solutions that generate numerous advantages in the production process from image analysis.
Combining the techniques Object detection , Text extraction and Object positioning we create solutions Image data tagging capable of making it more effective traceability of products inside industrial facilities and warehouses. Developed with the services made available to the main cloud providers present on the market (e.g.: Recognition and Extraction of text from AWS ), these solutions make it possible, for example, to automate the recovery of all the information and metadata encoded in the labels affixed on the products.
Like, how? Using object detection, all instances of the finished product and any associated labels attached are identified in each photographic image. Subsequently, in the Text Extraction phase the informative content of the metadata which is then brought to "metabolize" by the client's data platform . Finally, with Object Positioning we proceed to the positional connotation of each individual object in the image. This last phase is particularly important to overcome critical problems frequent in factory processes, such as the recognition of possible incorrectly applied labels on products. Once the error has been identified, the human operator can intervene directly in the specific case.
Defect Detection Solutions , on the contrary, they allow for example to automate the process of cataloging finished products and identifying those that are defective, even and especially where the defects are almost imperceptible to the human eye. A solution like this, created with AWS Vision Recognition services, quickly and accurately captures every defect, distinguishing each product as “correct” or “ defective,” and focuses on the defective product to further segment error types in a fully automatic manner.
AI and manufacturing, a five-step analytical approach
The analytical approach we use to create this type of solution can be summarized in five phases :
- collection of images for a good starting data set;
- image preprocessing: understanding what actions to take to make the images homogeneous and ready for subsequent stages of the supply chain;
- annotation of defects : we teach the machine to recognize imperfections, such as a hole or a crack, thus achieving moderation of human intervention on the model;
- model training submit new images;
- inference : once trained, the model will provide the answer.
Among the differentiating elements that have allowed us to create an AI solution capable of increasing efficiency, generating significant time savings and employing human operators in value-added activities, there is the approach “from start to finish”. A methodology that we use for both hardware and software components, also thanks to numerous partnerships.
Added to this are open architecture and vendor-independent approaches as well as a certain number of project accelerators, in particular data augmentation or data profiling techniques which, thanks to the experience acquired in the field, we systematically integrate and in a manner adapted to the context of the project on which we are working
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