Implement of digital twin for predicting and monitoring grinding process behavior

Abstract

The market is rapidly evolving in response to the growing demands of customers. To overcome these difficulties, innovative and efficient approaches for issue identification and visualization are required. Digital twin gives an approach to manufacturers to gain a clearer picture of real-world performance and operating conditions of a manufacturing asset via near real-time data captured from the asset and make proactive optimal operation decisions. It was discovered that the digital twin is a complicated notion currently in its early stages of growth, with differing viewpoints on how it should be defined across various professions and academia. The presented dissertation aims to develop and implement a process prediction system through a digital twin-based system for testing the process in virtual space before implement to shopfloor, as well as guidelines for implementing and creating a digital twin for companies within the production industry. Grinding is one of the most common precision machining methods because of its high machining efficiency and good finish quality. In the traditional grinding process, in order to set the appropriate machining parameters, researcher use the trial error method to calculate the appropriate machining parameters, which takes a lot of time. The grinding force is the most important parameter in the grinding process which affects the product quality. Today's products are become more individualized and high uncertainty, it is necessary to reduce the product design cycle of products and improve production efficiency. In this dissertation, the digital twin technology was implemented to the grinding process for predicting process behavior. By testing the machining parameters in advance, predicting the machining behavior, optimizing the machining parameters through the test results. At the same time, in the actual on-site machining process, by connecting with industrial PLC and sensors, input real-time machining parameters and sensors data, and calculate the cutting state under ideal conditions. These values are compared with the actual values as reference values, and if there is a deviation and exceeds the threshold, it means that there is a failure in the production process. On-site operators will inspect the machine and product. The proposed methodology for grinding behavior prediction using a digital twin approach, with the vertical double side grinding machine performing the required work while connecting the PLC program. The proposed approach integrates the information obtained from the sensors, physic models, and operation of the system to establish the grinding machine model and carry out grinding force. Simulation results show the proper connection between models and communication. The digital model was established to exactly match the operation of the physical system. Comparison between predicted result obtained from the proposed digital twin model and experiment, revealed a good agreement between proposed model and practice, indicating therefore that the model may be suitable for industrial applications further. The created system is installed on a SIEMENS-based platform in industrial PC and is compatible with a variety of additional programming tools. The technology makes a substantial contribution to Industry 4.0 and the autonomous operation of production facilities. It has increased total brake disc production productivity by lowering the number of quality failures and minimizing reliance on operators for process knowledge. This dissertation will begin with an overview of related literature before moving on to the research strategies used in the current study. It assesses the notion of digital twins for smart factories as well as the technology underlying them. It examines the different definitions of a digital twin, how far technology has progressed, and what manufacturing organizations need to deploy and develop digital twins. The development of a digital twin-based system, as well as its mechatronic modeling, deployment, and manufacturing implementation, are covered in the following sections. The final section informs the producers about the advantages of this technology.