A glimpse into the future with orthodontics’ smart brackets

O aparelho ortodôntico fixo tipo multibandas atual não permite medir ‘in-vivo’ as forças e torques aplicados ao dente individual. Para um tratamento ideal e para reduzir os efeitos iatrogênicos, o ‘Smart Bracket’ foi desenvolvido para uma próxima geração de aparelhos ortodônticos fixos fornecendo ao ortodontista uma medida quantitativa sobre as forças e torques aplicados a cada dente ao longo da terapia. O presente trabalho pretende ser uma revisão narrativa da literatura tendo como objetivo descrever o conceito de ‘Smart Bracket’, comparando-o com os aparelhos ortodônticos fixos atuais. Além disso, procura analisar e resumir o seu desenvolvimento e a evolução dos seus vários protótipos existentes. A pesquisa foi realizada entre Fevereiro e Agosto de 2020 por meio do motor de busca B-On (entre outros), para o período temporal 2005-2020, com o objectivo de sintetizar a literatura sobre o sistema, identificar seus limites e, eventualmente, recomendar novos temas de pesquisa. Adicionalmente, artigos de revisão e livros científicos foram consultados a partir de 2000 para apresentar os atuais aparelhos multibandas e seus efeitos iatrogênicos.The current multi-bracket appliances do not allow to measure ‘in-vivo’ the forces and torques applied to the individual tooth. For an ideal treatment and to reduce iatrogenic effects, the ‘Smart Bracket’ has been developed for a next generation of fixed orthodontic appliances providing the orthodontist with quantitative measure of the forces and torques applied to each tooth throughout therapy. The present work intends to be a narrative review of the literature aiming to describe the concept of ‘Smart Bracket’, comparing it with the current fixed orthodontic appliances. In addition, it seeks to analyze and summarize its development and the evolution of its various existing prototypes. The literature research was carried out between February and August 2020 using the search engine B-On (among others), for the period 2005-2020, with the aim of synthesizing the literature on the system, identifying its limits and, eventually, recommend new research topics. In addition, review articles and scientific books were consulted from 2000 onwards to present the current multiband devices and their iatrogenic effects

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way