Optimization of the energy consumption of a Dorna 1 commercial robot was carried out by replacing the original materials of the links (aluminum) with a lighter and more resistant material (carbon fiber) with the aim of lowering the operating costs of the robot. For this reason, a reduction in the total mass of the robot of 11.08% was achieved by replacing the original materials. In addition, simulations were carried out using finite element analysis to verify that the mechanical resistance of the optimized parts was adequate according to the level of demand that occurs during the operation of the robot. Subsequently, a comparison of the energy consumption of the original robot and the robot with the optimized parts was carried out using the Internet-of-Things device. The tests were carried out at three different speeds—1000, 3000, and 9000 deg/min—for 15 min by executing a pre-established routine starting from home. The results showed that at all test speeds, there were energy savings, but the greatest energy savings occurred at the speed of 3000 degrees/min in the range of 3.66%. With this result, it has been shown that the integration of light materials in robots can achieve energy savings.

Industrial automation in the manufacturing environment has revolutionized production and manufacturing in many industries, generating significant improvements in efficiency, quality, and process effectiveness. However, it has also posed challenges related to feedback in manufacturing environment monitoring systems, and increasing the effectiveness, productivity, and quality in industrial production. Feedback systems in the manufacturing environment are fundamental to industrial automation, which is why an application has been developed for the detection of elements in a printed circuit board manufacturing cell. The solution presented in this article proposes implementing a continuous feedback system with the ability to provide real-time information to identify the location of elements in a manufacturing cell and potentially detect anomalies, with the goal of improving the manufacturing process appropriately.

In this research, a proposed model aims to automatically identify patterns of spatial and temporal behavior of moving objects in video sequences. The moving objects are analyzed and characterized based on their shape and observable attributes in displacement. To quantify the moving objects over time and form a homogeneous database, a set of shape descriptors is introduced. Geometric measurements of shape, contrast, and connectedness are used to represent each moving object. The proposal uses Granger’s theory to find causal relationships from the history of each moving object stored in a database. The model is tested in two scenarios; the first is a public database, and the second scenario uses a proprietary database from a real scenario. The results show an average accuracy value of 78% in the detection of atypical behaviors in positive and negative dependence relationships.

HUB3D represents a cutting-edge solution for managing and operating a 3D printer farm through the integration of advanced hardware and software. It features intuitive, responsive interfaces that support seamless interaction across various devices. Leveraging cloud services ensures the system’s stability, security, and scalability, enabling users from diverse locations to effortlessly upload and manage their 3D printing projects. The hardware component includes a purpose-built rack capable of housing up to four 3D printers, each synchronized and managed by a manipulator arm controlled via Raspberry Pi technology. This setup facilitates continuous operation and high automation, optimizing production efficiency and reducing downtime significantly. This integrated approach positions HUB3D at the forefront of additive manufacturing management. By combining robust hardware capabilities with sophisticated software functionalities and cloud integration, the system offers unparalleled advantages. It supports continuous manufacturing processes, enhances workflow efficiency, and enables remote monitoring and management of printing operations. Overall, HUB3D’s innovative design and comprehensive features cater to both individual users and businesses seeking to streamline 3D printing workflows. With scalability, automation, and remote accessibility at its core, HUB3D represents a pivotal advancement in modern manufacturing technology, promising increased productivity and operational flexibility in the realm of additive manufacturing.