چكيده لاتين
The rapid advancements in technology have brought significant changes across various fields, and the manufacturing industry is no exception. Industry 4.0, known as the Fourth Industrial Revolution, marks a fundamental transformation in production processes through advanced digital technologies. This industry is characterized by the integration of new digital technologies with traditional production systems, leading to the emergence of smart factories and intelligent production systems. Among these technologies are the digital twin, the Internet of Things (IoT), big data, artificial intelligence (AI), cyber-physical systems (CPS), cloud computing, 3D printing, augmented reality (AR), robotics, and blockchain. Each of these technologies plays a crucial role in improving various aspects of the production process, from design and development to supply chain management and customer interaction. The aim of this research is to provide a comprehensive understanding of the digital twin as one of the key technologies of Industry 4.0 and to explore its components, benefits, and real-world applications. Additionally, after reviewing the injection molding process and its components, a proposal is made for implementing the digital twin in one of the plastic injection machines at HaierPlast, a subsidiary of Entekhab Industrial Group (SNOWA). This system consists of two main parts: real-time data collection and the digital twin framework, which is composed of three subsystems: simulation, artificial intelligence, and a user interface. The system was implemented for the production of a plastic part, aiming to reduce the production cycle time and improve the process by using optimized parameters obtained from simulation and artificial intelligence. The simulation was performed using MoldFlow software, and the AI model was developed using an artificial neural network with two hidden layers to predict the production cycle time, utilizing a set of 100 real production data points. A user interface, written in Python, was created to integrate and visualize the results from these two subsystems, forming the core of the digital twin framework. Implementing this system resulted in the optimization of the productʹs production cycle time, reducing it by 13.3%. Overall, the results of this study show that utilizing next-generation technologies and shifting away from traditional approaches in the injection molding process can lead to better monitoring and control of production, more efficient data usage and processing, and optimization of the production cycle time.
