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Industry 4.0 aims at harnessing industrial data to drive better improvements and efficiencies to add across the value chain. The digital twin is the key tool that’s driving industry 4.0 as it integrates and ties information of human assets in a plant. It fulfills the vision of smart manufacturing. The leveraging applications of IoT, machine learning, and subsets of AI have changed the way industries operate on real-world operational and environmental data. The emergence of the digital twin totally side swapped other firm tech applications by delivering actionable and consumable insights. Digital twin provides more valuable insights, context-text outputs available for the right user at the right time, develop new usecases for industries using statistics, smart technologies, and data.
A digital twin is a near-time digital footprint of prototypes that replicate the physical objects in the real world. Sensors connected to physical objects enable digital twin in the acquisition and exchange of data through the heterogeneously connected IoT devices. By integrating IoT, machine learning, AI, and deep learning networks digital twin creates living digital simulation models. It differs from the computed-aided images and serves merely as sensor-enabled IoT. It can self-learn according to the corrections and changes in the sensor data with the change in physical objects and their counterparts.
Why a Digital Twin is an important tool for Manufacturing enterprises?
Enterprise data maintenance, growth predictions, and investment analysis are complicated tasks. And humans may mislead with errors, reducing the accuracy level of information communicated between machines and humans with existing technologies. Implementing Digital twin can help companies in portraying the digital footprint of their products, from product manufacturing to product after-sales reports, that solves physical errors sooner, by predicting them faster. Digital twin provides values in areas like- new product market predictions, improved operations, reduced defects, design and building better products, and strategizing new business models for high returns.
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Visualization of data analytics:
Data visualization is a crucial task, as providing the right insights to an enterprise at the right time might have bottlenecks in its path. Companies are using big data analytics to harness gallons of data daily to address consumer wants. With digital disruption, the evolving technologies have more to offer to fulfill the vision of smart manufacturing. The digital twin merged with data analytics can provide real-time virtual access to data-driven insights, that provide an edge in the industry 4.0 revolution.
Whirlpool, the world’s leading electronic goods manufacturer, is experimenting with IoT tools in integrating action with digital twins and data analytics to advance their technology to address consumer needs. They are building a prototype of refrigeration that allows their consumers to automatically refill the diminishing and out-of-stock products from nearby stores online.
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Monitoring tool in product and production life cycle:
Quality inspection, product re-designing, machine assembly planning, logistics planning, and product development, have been key pain points in production processes for decades. Continuous monitoring at every stage of product life cycle development using an IoT system is an advantage over random monitoring by humans. Using digital twin integrated with IoT reduces quality time and maintenance costs by optimizing day-to-day monitoring systems. This technology performs a better job of designing and testing, product and processes of production with low maintenance in perfect conditions.
Maserati has used a digital twin system to create a simulation model that reduces 30% of production time. It minimizes expenditure spent on real-world prototype experiments, wind tuner tests, and test drives. Maserati workshop has a digitally-enabled production line that changes the position of robots to eliminate quality time spent on inefficient movements.
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A predictive maintenance tool:
Transportation Industry, particularly aviation and automobile, spends millions of dollars every year in working prototype trials in predicting the maintenance tenure of the engine and other body parts. With the help of digital twins, these industries can resolve the issues by creating virtual prototypes in real time that mimic real-world physical objects. These models can solve problems included in maintenance costs by suggesting improvements in product design and testing.
GE’s aircraft engines integrated digital twins as a part of predictive maintenance tools to drive data insights from digital images of engines. Digital twins used the information gathered from the sensors on the aircraft, to evaluate engine performance according to different environmental conditions. Based on this model the company detected the lifespan of engines that reduced the maintenance cost by saving millions of dollars.
As discussed in the article, the digital twin is emerging as a prominent tool in driving industry 4.0. It defines all the needs of smart manufacturing by providing value throughout the production, product, supply, and CRM value chains. The implementation of this technology is cost-efficient and adds more value to the revenue system by integrating this technology into IoT, AI, data Intelligence, plant logistics management, etc. It provides a competitive edge in the era of smart manufacturing by advancing enterprise thought processes in addressing its consumers.
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