# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 3 Quality Control Using In-Situ Methods
### In-Line Quality Control  
Due to the increasing possibilities of integrating algorithms into data processing systems, there are now a large number of different approaches for the QC of products during manufacturing. This is specifically being enforced by the increasing accessibility to the usage of ML, AI, and Big Data. For example, neural networks are in focus for the integration in different systems. Especially for visual inspection, these methods are progressively used in the metalworking industry [53; 54; 55]. Surface inspection systems are particularly important in this respect. These are designed to detect and classify defects on the surface of the material, on the basis of images. This is usually done by the usage of training data sets, which show various defects on the surface, and which class they belong to. For this purpose there are countless approaches, for example [56] where ML algorithms like the support vector machine are applied to classify the defects. Here it was shown that the classification speed is sufficient to achieve acceptable defect detection. Another possibility to implement a statistically based approach into a neural network is the Principal Components Analysis (PCA), which is not only able to detect a defect using a large variation of defect images as input data, but also to classify the defect in order to understand its history. Other approaches can eventually be found in [57; 58; 59; 60; 61; 62]. However, this is a very complex endeavor, since the variety of defects can be very high for different steel products alone. The defects occurring in hot-rolled steel products, for instance, are divided into nine main and 29 secondary classes [63]. An exact classification based on images is therefore an enormous challenge. Furthermore, it is a major hurdle to use image processing systems, due to the high temperatures and the exit speed of the strip of up to 100 km/h [56]. Even with increasing image acquisition frequency and improved computer performance, this type of inspection can be suppressed by oxidation and the harsh environment [64]. While the surface inspection systems are designed only for the detection of defects on the surface, there is a method that can also determine the microstructure of the product in-line. The 3 MA (Multiparameter Micromagnetic Microstructure and stress Analyzer) technique is based on four micromagnetic principles. The detection of Barkhausen noise, the incremental permeability, the eddy current, and the tangential magnetic field method, which together provide many parameters which are used to gain information about the prevalent microstructure. For example, by measuring the coercive field, remanence, and maximum permeability, the change in the hysteresis can be related to dislocation density, and thus to the prevailing strain. The implementation in a rolling mill in three directions relative to the rolling direction (\(0^{\circ}\), \(45^{\circ}\) and \(90^{\circ}\)) allows anisotropy parameters to be determined, in addition to the common mechanical parameters [65; 66]. Although this method has already been implemented in some steel rolling mills and is a promising method for in-line QC, there are still some drawbacks. The operating temperature is currently still limited to 300 \({}^{\circ}\)C and can only be realized by intensive cooling of the probe head. In a rolling mill, the distance between the probe and the cold strip is decisive for measuring accuracy. Therefore, the strip must be straightened by additional rollers to ensure an approximately constant lift-off. In addition, this method can only be used with ferromagnetic materials and requires complex calibration for each grade and strip thickness [65]. Eddy current inspection is a well-established method for detecting (near-) surface defects. In industry, these are also used for inline quality control, for example in wire rolling mills. Combined with high-speed cameras, they are able to detect manufacturing defects at temperatures of up to 1200 \({}^{\circ}\)C and wire speeds of up to 150 m/s.