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In Smart machining, if we want to achieve overall process forecasting, we must rely on the characteristic of IIoT. Therefore, if you want to make the cutting data reaction fast, you will certainly need to go toward Digital Twin. Through feedback data and self-learning, presenting the real situation of entities in the Digital world in real-time can shorten the product development cycle and cost, reduce maintenance costs and enhance the service quality…etc.
Digital Twin refers to the establishment of a digital analogy by integrating physical feedback data and through Artificial Intelligence, machine learning and software analysis. And this can be applied in various industries, which can shorten product development cycle and cost, reduce maintenance cost, and enhance the service quality…etc.
Simulations and digital twins both employ digital models to replicate the operations of a system, but a digital twin is essentially a virtual environment that offers a much richer platform for analysis. The key distinction between a digital twin and a simulation primarily lies in their scope: Simulations typically focus on examining a specific process, whereas a digital twin has the capability to conduct numerous valuable simulations to investigate multiple processes.
Not just the above; for example, simulations usually can't benefit from real-time data, but digital twins are designed around two-way information flow.Information flow initially takes place when sensor data is transmitted to the system processor. Subsequently, it occurs once more when the insights generated by the processor are shared with the original source objects.
By possessing enhanced and continuously updated data across a broad spectrum of domains, coupled with the augmented computational capabilities inherent to a virtual environment, digital twins can investigate a broader array of issues from multiple perspectives compared to traditional simulations. This heightened potential ultimately facilitates the enhancement of products and processes.
The video above shows the on-machine version of TCC_easyinline's Smart cutting tool, combined with the software Spike®_map, which can easily import the Digital Twin directly into the machine tool. The left side is the actual aerospace blade cutting, and the right side is the actual signal during the cutting process.
The constructed 3D model, which integrates clicks-and-mortar, allows the processing industry to judge the quality of the workpiece, the constitution of the machine tool, the cutting efficiency, and even grasp the cost quickly and easily.
For example, the picture on the right is a 3D model graph constructed from a real signal. You will see some strange lines on it. The colors (blue→red = weak→strong) represent different cutting forces, and there is a blue light on the waist of the blade. From the line of cutting force, we can clearly see that the upper and lower parts of the blade are composed of different programs. Then we can see that the lower half of the blade has similar celluloid patterns. This pattern is caused by the error of the screw gap and the simultaneous motion of the motor.
Such a 3D virtual image has gone beyond the virtual and reflects the real quality of the blade, fully embodying the benefits of Digital Twinning.
Main image photo by Google images.
Reference site Wikipedia.
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