4.1 Introduction

Digital twin design and technology is the most important strategic trends in the modern Industry 4.0 standards. This methodology helps in better understanding the machinery and meeting industrial expectation. The digital twin technology provides better prognostics and care to the machines [1–4]. The virtual/digital representation of the physical industrial machines and process makes the engineers and researchers to easily predict the performance efficiency and characteristics. This methodology of monitoring, i.e., digital representations, is represented throughout the product lifecycle for simulation purposes. This approach capable to demonstrate and mimic the design changes, scenario on usage, and other environmental conditions. This also reduces the approach on reducing the development time and aids in increasing the quality of finalized products. Important developments in Internet of Things (IoT), data science, and artificial intelligence have made the possibility to propose new standards in manufacturing strategies [5–9]. The common objective of this Industry 4.0 digital twin modeling is to increase the productivity and enhance the lifetime of the machine with high production quality. The reach of smart interconnection and operability in physical and digital space creates more challenges in interfacing monitoring. Figure 4.1 illustrates the technologies aiding Industry 4.0 modeling opportunities.

Security, reliability, and fault tolerant are the most expected features in Industry 4.0 for better operation and production [10–13].

4.2 Related Works

Numerous researches on digital twin and smart factories manufacturing have come in recent days. The existing IDS are well suited in real time, and deployment in virtual domain creates more complexities. The challenges in security of IDS are defined in [6], in which light weight cryptography algorithms are mainly focused. The state machine replication through stochastic mathematical approach is described in detail in [14–16]. The assumption on fault-based state machine realization in many approaches. The article explains the important objective of digital design as health analysis and lifetime monitoring of equipment. Penetration-based testing is also done in [17–19] through Industry 4.0 which is more challenging in nature. The approach on state synchronization with IoT security was suggested in article [15]. This mainly covers only modeling and does not address the complete digital twin security frameworks. An autonomous ML and PLC middle attack was deeply discussed in article [17]. The input physical parameters are considered in passive replication problem modeling and purely based on physical world parameters.

4.3 Industry 4.0 Production and Dashboard Design

Remote production monitoring is the request from many of the production companies to the IoT engineers. The industrial expectation on IoT is really high and expects remote monitoring through intranet and secure data communication. This design is done through economical single-board computer (SBC) along with the node java scripting. The sensors used for monitoring is ultrasonic or laser-based light-depended resistors. The sensors used in the industries are normally 12 or 24 volts. A voltage divider circuit is designed to convert the sensor value as per the SBC power constraint. The AMS1117 IC in the board avoids increasing voltage and spikes due to industrial actions and safeguards the SBC. The SBCs are interfaced with the wireless network or existing wired infrastructure throughout the industries, so that the workers can able to see the production outcome anywhere within the industries. This happens with the secret key sharing and authentication mechanism. The data is secured through encryption algorithm. Figure 4.2 illustrates the proposed system architecture; key sharing is done through authenticated channel.

Algorithm

Begin Process

        Input from PLC controller is recorded in single-board computer; Share data to the industrial Cloud;

        Once authentication is received share data to the concerned worker Data security through elliptic curve cryptography

End Process

4.4 Results and Discussion

The digital twin creation setup for a battery production monitoring system is created through the proposed architecture. Raspberry Pi–based SBC is used for monitoring the production process and the same is interfaced with the industrial internet. Figure 4.3 illustrates the battery production unit with anode-cathode plate’s assembly. The anode and cathode plates are assembled and moved for welding zones.

The production monitoring dashboard design is done through Node. js scripting language in SBC. The SBC is connected with the network and can able to deploy remote GPIO and programming logic through network programming methodology. The production is verified and the status is updated within microsecond delay to the cloud environment. Figure 4.4 illustrates the digital twin creation using Node.js with gauge and dashboard design.

Figure 4.5 illustrates the application interface both monitor and android-based application design.

Figure 4.6 illustrates the minimum and maximum delay time to update the data in private cloud. The delay caused is due to various network traffic conditions inside the network.

4.5 Conclusion

A digital twin design is modeled through IoT SBCs. A battery manufacturing process is mimicked in thispaper. The data is also routed to the private cloud through securechannel. The sensor production to PLC circuitis tapped and programming is done to monitor the working of PLC logic. The time delay in uploading the data in the cloud is also calculated in this approach. The model is efficient and can be interfaced with any Industrial PLC circuits.