The introduction of the I4.0 concepts and the complete digitalization of the production process is the main challenge of the next years and it is even more important in mass production sectors, such as the automotive field. And we know that shot peening is widely used for improving the strength of many parts of a car, from gears to springs.
One of the most interesting aspects of the implementation of I4.0 is the creation and the development of the so-called digital twins. What is this?
A digital twin is generally defined as a digital replica of a product or a process. By bridging the physical and the virtual world, data is transmitted seamlessly and allows the virtual entity to exist simultaneously with the physical entity (BTW, in literature there are many definitions of digital twins; I consider this one as a definition that fits well to shot-peening applications).
This means that on the basis of the real data of the process and of their comparison with the theoretical/numerical model implemented in the digital twin, we are able to assess, in real time, how the real process is diverging from the expected results. This means also that we are able, based on the real data, to correct the process parameters and improve the final result.
By increasing the amount of data to manage, the control of the process and the final result will become more and more accurate. It is clear that an approach like this is very important in the automotive and industrial sectors with high production rates.
To be successful, this approach needs, among others, two things: an efficient and accurate set of sensors, able to provide the information needed by the digital twin; and an appropriate model, able to describe the process itself and to address the corrective actions if needed.
Focussing on shot peening, the definition of an efficient digital twin must consider the application of proper sensors in the right positions, in such a way that the signals can accurately describe the process and can be used to assess if it is running in the right way or if something should be corrected. This is not easy, and it means that we should pass from a static tuning of the parameters that considers the Almen intensity, the coverage, and how to reach the desired values, to a dynamic adaptation of the process, where, on the base of real-time measurements, we continuously adjust the parameters.
The second issue is related to the definition of the proper model. They are many different approaches. One could be based on process simulation, such as finite element analysis. The second is based on artificial intelligence and the development of algorithms based on previously acquired data that describe the behaviour of the system, to predict it and to address corrective actions. This second approach will be effective if a very large amount of data is available and if we are able to correctly use these data (we are talking about a big-data strategy). Indeed, this second approach does not allow having a clear understanding of the physics of the peening process.
No matter what the best approach is or if both should be considered for a complete control and understanding of the process, the next challenge for shot peening is the ability of developing efficient digital twins for a complete 4.0 implementation.

Shot Peening in the Automotive Industry
by Mario Guagliano
Contributing Editor MFN and
Full Professor of Technical University of Milan
20156 Milan, Italy
E-mail: mario@mfn.li