Manufacturing has been on the Industry 1.0 revolution. From there, there are lot of happenings till the simulation softwares that we have in abudance. The entire value chain can be simulated and predicted for the output to happen.
Digital twin with the Aritifical Intelligence is capable of recording, evaluating production, performance data for the products in real time along with historian data. These data becomes the basis of enabling a machine 'intelligent', so that it makes the decision to optimize the output of the product to match with the 'machine-health index', automatically. On the basis of programs and simluation, machines can perform tasks automatically, upto a certain level. The learning culture inside the machine, makes the difference in the growth of this sector. Efficiency can be gained easily, at the cost of fewer errors is the need of the hour.
Digital twin is the phrase used to describe a computerized (or digital) version of a physical asset and/or process. The digital twin contains one or more sensors that collects data to represent real-time information about the physical asset.
The machine enabled with IIOT gives tons of data onto the cloud and everything is populated and reasoned out for furthur calculations. A digital twin for performance can be gained and used to furthur optimize the machines and indicate the predictive maintenance activities. It is possible for the designers, engineers and programmers to work on the same project/ machine simultaneously, while constantly exchanging ideas and sharing experiences. The machine builder has a greater efficiency in terms of design effort, assembly and commissioning.
Sooner or later, all the machines data needs to be on the cloud. All the cloud data needs to be analyzed and collobrative algorithms shall help the OEM to minimize the down time of the product in an efficient manner. Digital networking of machines shall minimize the downtime and increase the efficiency.
Said all this, a pre-requisite for Industry 4.0 and AI would be state-of-the-art, end-to-end infrastructure regardless the data size. The science of cyberspace along with the cyber security needs to go hand-in-hand. The risk of cyber theft along with the data loss, would be very costly and erroneous.
Machine Learning is a subset of AI. Patterns are recognized in unstrcutured datasets using algorithmsand decisions are to be taken themselves based on the key-in knowledge. The black box of the ML, shall learn with the data coming in and based on this, use the experiences it acquires to constantly improve its ability to perform better tasks.
Deep Learning relies on the Neural network algorithm. The computer accesses data at several nodes simultaneously in order to identify connections and make conclusions and predicts the outcome. Self-learning algorithm enable the machine to solve complex non-linear problems on its own. It also makes its own decisions after a certain point of time.