Sistema de monitoramento da manufatura baseado em RFID no âmbito da internet of things

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2016.O surgimento de novas tecnologias de comunicação sem fio possibilitou o fenômeno chamado Internet Of Things. A aplicação desse conceito no ambiente de manufatura proporciona novas oportunidades relacionadas à gestão de operações na melhoria de processos, conectando objetos e computadores de forma criativa. No contexto de melhorias nas operações de manufatura, os consagrados princípios da Manufatura Enxuta (Lean Manufacturing) passam a ser cada vez mais disseminados entre os gestores de operações, reduzindo desperdícios e oferecendo benefícios generalizados aos processos analisados. Nesse contexto, o presente trabalho tem como objetivo desenvolver um sistema de monitoramento de objetos com a tecnologia RFID, dispositivos sem fio (wireless) e plug and play, para auxiliar na coleta de dados um sistema de produção, fornecendo informações de tempo e de quantidades, facilitando a execução do balanceamento de carga, do atendimento ao takt time, da avaliação de capacidade e da identificação de desperdícios no processo. O sistema proposto compreende a definição dos requisitos de projeto, o desenvolvimento de um módulo leitor RFID, de um middleware, de um banco de dados e de um software aplicativo. O sistema foi composto por micro controladores Arduino, leitores RFID e módulos de transmissão de dados sem fio Zigbee, possibilitando operação com atualizações em tempo real e com mobilidade. Por fim, o sistema foi aplicado em duas empresas dos setores automotivo e de energia, a fim de atender diferentes demandas na gestão de operações. No setor automotivo, cinco pontos de leitura foram instalados em uma célula de fabricação de yokes e os resultados mostraram que o processo não estava apto a atender o takt time definido. No setor de energia, dois pontos de leitura foram usados para analisar horas improdutivas, que compreenderam 23,4% do tempo gasto. Também verificou-se uma redução de 60% de horas-homem em comparação com o previsto em orçamento da empresa.Abstract : The appearance of new wireless communication technologies has enabled the Internet of Things phenomenon. The application of such concept in the manufacturing environment provides new opportunities related to the management of operations in improving processes, connecting objects to computers in a creative way. In the context of improving manufacturing operations, the longstanding Lean Manufacturing principles become increasingly disseminated among operation managers, reducing waste and providing significant benefits to the analyzed processes. Considering this scenario, this study aims to develop a monitoring system using RFID, wireless, and plug and play technologies in order to help data collection in a production system, providing information about time and quantities, enabling process waste identification, production according to takt time, activity balance, and production capacity assessment. The proposed system comprises the definition of design requirements and the development of a RFID reader, a middleware, a database, and a software. The system is composed by Arduino micro-controllers, RFID readers, and wireless Zigbee transmission modules, enabling operations with real-time updates and mobility. The system was applied in two companies from automotive and energy sectors, in order to answer different demands in operations management. In the company of the automotive sector, five reading points were installed in one cell that manufactures yokes, and the results show that the process is not able to reach the takt time. In the company of the energy sector, two reading points were used to analyze unproductive hours, which comprised 23.4% of the time spent. It was also verified a reduction of 60% in man-hours in comparison with what was predicted in the company's budget

Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms

Identifying specific locations of items such as containers, warehouse pellets, and returnable packages in a large environment, for instance, in a warehouse, requires an extensive tracking system that could identify the location through data visualization. This is the similar case for radio-frequency identification (RFID) pallet level signal as the accuracy of determining the position for specific location either on the level or stacked in the same direction are read uniformly. However, there is no single study focusing on pallet-level classification, in particular on distance measurement of pallet height. Hence, a methodological approach that could provide the solution is essential to reduce the misplaced issues and thus reduce the problem in searching the products in a large-scale setting. The objective of this work attempts to define the pallet level of the stacked RFID tags through the machine learning techniques framework. The methodology started with the pallet-level which firstly determined by manual clustering according to the product code number of the tags that were manufactured for defining the actual level. An additional study of the radio frequency of the tagged pallet box in static condition was carried out by determining the feature of the time series. Various sample sizes of 1 Hz, 5 Hz and 10 Hz combined with the received signal strength of maximum, minimum, mode, median, mean, variance, maximum and minimum difference, kurtosis and skewness are evaluated. The statistical features of the received signal strength reading are analyzed by the selection of the univariate features, feature importance technique, and principal component analysis. The received signal strength of the maximum, median, and mean of all statistical features has been shown to be significant specifically for the 10Hz sample size. Different machine learning classifiers were tested based on the significant features, namely the Artificial Neural Network, Decision Tree, Random Forest, Naive Bayes Support Vector Machine, and k-Nearest Neighbors. It was shown that up to 95.02% of the trained Random Forest Model could be classified, indicating that the established framework is viable for pallet classification. Furthermore, the efficacy of different models based on heuristic hyperparameter tuning is evaluated in which the different kernel function for Support Vector Machine, various distance metrics of k-Nearest Neighbors. The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. In results, it was found that the Random Forest provided 92.44% of the test sets with the highest accuracy. In order to further validate the position of the tagging in the pallet box of the Random Forest model developed, a different predefined location was used to validate the model. The best position that could achieve a classification accuracy of 93.30% through the validation process for position five (5) in the systematic model that is the centre of the pallet box. In conclusion, it can be inferred from the analysis that the Random Forest model has better predictive performance compared to the rest of the pallet level partition model with a height of 12 cm used in this research. Based on the train, validation, and test sets in Random Forest, the RFID capability to determine the position of the pallet can be detected precisely

Radio Frequency Identification (RFID) based wireless manufacturing systems, a review

Radio frequency identification (RFID) is one of the most promising technological innovations in order to track and trace products as well as material flow in manufacturing systems. High Frequency (HF) and Ultra High Frequency (UHF) RFID systems can track a wide range of products in the part production process via radio waves with level of accuracy and reliability.   As a result, quality and transparency of data across the supply chain can be accurately obtained in order to decrease time and cost of part production. Also, process planning and part production scheduling can be modified using the advanced RFID systems in part manufacturing process. Moreover, to decrease the cost of produced parts, material handling systems in the advanced assembly lines can be analyzed and developed by using the RFID. Smart storage systems can increase efficiency in part production systems by providing accurate information from the stored raw materials and products for the production planning systems. To increase efficiency of energy consumption in production processes, energy management systems can be developed by using the RFID-sensor networks. Therefore, smart factories and intelligent manufacturing systems as industry 4.0 can be introduced by using the developed RFID systems in order to provide new generation of part production systems. In this paper, a review of RFID based wireless manufacturing systems is presented and future research works are also suggested. It has been observed that the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers

Using Therbligs to embed intelligence in workpieces for digital assistive assembly

Current OEM (Original Equipment Manufacturer) facilities tend to be highly integrated and are often situated on one site. While providing scale of production such centralisation may create barriers to the achievement of fully flexible, adaptable, and reconfigurable factories. The advent of Industry 4.0 opens up opportunities to address these barriers by decentralising information and decision-making in manufacturing systems through CPS (Cyber Physical Systems) use. This research presents a qualitative study that investigates the possibility of distributing information and decision-making logic into ‘smart workpieces’ which can actively participate in assembly operations. To validate the concept, a use-case demonstrator, corresponding to the assembly of a ‘flat-pack’ table, was explored. Assembly parts in the demonstrator, were equipped with computation, networking, and interaction capabilities. Ten participants were invited to evaluate the smart assembly method and compare its results to the traditional assembly method. The results showed that in its current configuration the smart assembly was slower. However, it made the assembly process more flexible, adaptable and reconfigurable