No abstract

Recent advancements in Artificial Intelligence (AI), deep learning (DL), and computer vision have revolutionized various industrial processes through image classification and object detection. State-of-the-art Optical Character Recognition (OCR) and object detection (OD) technologies, such as YOLO and PaddleOCR, have emerged as powerful solutions for addressing challenges in recognizing textual and non-textual information on printed stickers. However, a well-established framework integrating these cutting-edge technologies for industrial applications still needs to be discovered. In this paper, we propose an innovative framework that combines advanced OCR and OD techniques to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework adept at detecting and recognizing textual and non-textual information on printed stickers within a company, harnessing the latest AI tools and technologies for sticker information recognition. Our experiments reveal an overall macro accuracy of 0.88 for sticker OCR across three distinct patterns. Furthermore, the proposed system goes beyond traditional Printed Character Recognition (PCR) by extracting supplementary information, such as barcodes and QR codes present in the image, significantly streamlining industrial workflows and minimizing manual labor demands.

Many industrial sectors still lack automation resources to optimize their production processes, aiming to make manufacturing leaner and offer better working conditions to operators. Without these improvements, workers can suffer physical and even psychological damage from the ergonomic risks of the activities performed. Thus, the aim of this paper is to present the ergonomic evaluation of packaging tapes workstation before and after the implementation of an automatic packaging machine, called Guzzetti. In the Guzzetti context, the paper shows the implementation of an electrical system based on controlling a mechanical device powered by servomotors and controlled by a PLC is necessary. For ergonomic evaluation, the paper presents the application of three methods: Suzanne Rodger, Strain Index, called Moore and Garg and REBA (Rapid Entire Body Assessment). With the results collection, was possible to obtain improvements in ergonomic risks that changed from the intermediate level to low level in all methods.

Smart buildings provide opportunities for technological transformations in building environments to improve resource management, comfort, and efficiency of the systems present in these facilities. For this, Internet of Things (IoT) solutions contribute, with monitoring and remote control features, to automate these environments. However, these solutions can promote the disposal or replacement of outdated but still-needed legacy systems. Thus, a reference model that uses retrofit techniques to update pre-existing systems would be an alternative to enable smart building convergence. The lack of models that advocate this type of strategy provides an opportunity for the emergence of methods capable of filling this gap. Thus, this work presents a strategy for implementing monitoring, control, and communication resources to achieve smart building convergence in legacy building systems. This strategy consists of the use of retrofit techniques based on the adaptation of the SmartLVGrid metamodel. To validate this proposal, we developed hardware platforms and, respectively, their firmware to implement the premises established in a legacy building lighting circuit. The results obtained present a new possibility of implementing smart buildings from the retrofit of legacy infrastructures, as the pre-existing building lighting circuit obtained new functionalities and was preserved as much as possible.