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

Abstract

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.

Predictive use of modern reference osteological collections for disentangling the shape of Eurasian equid cheek teeth and metapodials in archaeological material

“…It captures the complex characteristics of a dataset and has a high potential for discrimination and classification of patterns. Moreover, there is a low risk of noise and outliers using this method (Dastres & Soori, 2021; Haykin, 2008). In comparison to the LDA and k‐NN algorithms, it is more powerful and provides better classification rates (Ripley, 1996, 1998).…”

“…In this study, we present the use of GM techniques as a useful tool for identifying different equid species based on their morphological characters (Bookstein, 1991; Rohlf & Marcus, 1993), which have been proven to be more powerful than traditional morphometrics. We then combined GM techniques with three classification methods, more precisely linear discriminant analysis (LDA) (Baylac & Frieß, 2005), k‐nearest neighbors (k‐NN) algorithm (Guo et al, 2003; Venables & Ripley, 2002), and artificial neural networks (ANN) (Dastres & Soori, 2021; Dobigny et al, 2002; Ripley, 1996) in order to optimize specific identification of modern equids.…”

Cutting tool wear minimization in drilling operations of titanium alloy Ti-6Al-4V

“…A sophisticated virtual machining technique was established by Soori et al 28 in order to enhance surface quality during five-axis milling operations of turbine blades. To increase the efficiency of energy consumption during part production process, the quality and transparency of data across the supply chain, and precision and reliability throughout component manufacture, Dastres et al 36 recommended evaluating radio frequency identification (RFID)-based wireless manufacturing systems.…”

Анализ Зарубежных Систем Поддержки Принятия Решений В Информационно-Технологической Отрасли

“…[7] R. Dastres и M. Soori рассматривают возможности применения систем принятия решений в различных отраслях, в том числе и отрасли информационных технологий. Согласно подходу авторов, информационно-технологическая отрасль характеризуется огромными объемами данных, которые нужно обрабатывать и анализировать, также для отрасли важны такие параметры как скорость принятия решений, защита данных, конфиденциальность, а также возможности интеграции с другими системами и приложениями [5].…”

“…СППР обеспечивают доступ предприятий отрасли информационных технологий к наиболее актуальным данным, аналитическим инструментам и моделям, которые позволяют анализировать информацию, а также прогнозировать результаты реализации различных стратегических опций и сценариев. В результате предприятия могут принимать лучшие решения, основанные на фактах и аналитике, что в конечном итоге позволяет им стать более адаптивными, эффективными и коммерчески успешными в динамичной и конкурентной среде [5]. Например, Google активно использует аналитические инструменты и СППР для того, чтобы принимать обоснованные управленческие решения, основанные на актуальных данных, оптимизировать свою деятельность и поддерживать инновационный подход к разработке и предоставлению продуктов и услуг.…”