The paper covers key issues of metal and alloys' microstructure control using cast iron microstructure examples, and ways of resolving these issues by integration of neural networks into algorithms of SIAMS software. Paper lists key specifics of using the technology and training neural network, aimed at improving algorithm reproducibility, analysis acceleration and simplification. The method for training neural network models as part of the SIAMS software includes functionality for assessing the quality of training. The described method allows you control the model error using the value of the loss function. Developed algorithms in form of ready solutions were integrated into the SIAMS software package, and can be recommended for serial microstructure control in industrial laboratories. Microstructure control, comparison with reference scales, cast iron microstructure evaluation, microstructure image segmentation, neural network for microstructure
Materials microstructure control is a complex process that requires special skills and knowledge from a specialist. However, the necessity to provide quantitative and qualitative description of microstructure in question requires a professional to make a decision on material compliance or noncompliance with requirements.
In order to simplify decision making, most of regulatory documents referring to quality control of metallurgy products include variants of microstructures that can appear in finished products under certain conditions, including certain range of chemical component content, processing stages, and heat treatment. Russian GOST standards include reference charts where images are arranged in ascending or descending order with regard to the number of phases, number of inclusions, or dimensional characteristics for the objects of interest. These references really assist in decision making; however, it is impossible to preselect and preliminarily evaluate all versions of microstructures that can occur in course of production. Thus, reference scales include limited number of variants, and it is not always possible to find a suitable image.
Besides that, tightening quality requirements for metallurgy products and operational characteristics
of the goods, require production technology changes that cause new microstructures not covered by
standards. As there is no systematic work in the field of actualizing national standards [
For example, according to GOST 3443-87 [
2021 Copyright for this paper by its authors.
of comparing grey cast iron sample microstructure with reference scale 1A “Shape of graphite
inclusions” from GOST 3443-87 [
Analytical method using reference scales is considered to be a semi-quantitative method. In order to
improve precision of metallographic analysis, companies implement image analysis systems featuring
automated quantitative analysis methods. These methods include software-aided identification of
structural contents with following computation of necessary parameters [
Figure 2 demonstrates results of automated graphite microstructure description performed for grey
iron with SIAMS 800 software [
Object recognition in image analysis is performed using various segmentation types [
Despite positive results of applying the said segmentation algorithms, they are not universal and are unsuitable for solving more complex image analysis problems. It is often necessary to recognize (select) of classify objects on the same image that are similar in terms of brightness and color, texture and internal gradients, and also in terms of shape and size, while not having visible separation boundaries. Such microstructures can be referred to as “complex” in terms of automated image analysis. In an example shown on figure 3 it is necessary to select graphite particles from pearlite matrix, and distinguish structurally free cementite from ferrite and lamellar cementite present in pearlite. 2.1.
In order to analyze “complex” microstructures, SIAMS software uses semantic segmentation
method using neural network [
Figures 4-6 show fragments of cast iron microstructure after chemical etching. Graphite particles are barely visible upon the pearlite background (figures 4, 5). Arrows show fragments, where there are no visible boundaries separating phases. One can see that cementite particles are identical to ferrite fragments in terms of brightness (figure 6). Color contours show results of automatically separating structural components, demonstrating that the models are performing well.
Figure 7 shows microstructure of Ni-resist type cast iron, with metal matrix consisting of austenite with particles of excess cementite, after mild etching. Cementite particles have similar brightness parameters with austenite and are barely distinguishable. Automated recognition task was further complicated by excluding phase of final polishing, and image was captured with disabled equalization of microscope illumination. That caused increase in brightness gradient for objects belonging to both phases. Figure 7b demonstrates that the change in specimen preparation and capturing conditions did not cause degradation of recognition results. Figure 7c demonstrates results of simultaneous selection of graphite and cementite particles from the soft austenitic matrix with remaining sample preparation scratches and stains.
Final results of recognizing cast iron structural components are satisfactory and suitable for further calculation of quantitative characteristics required by standards. Analytical algorithms used in the examples were integrated in the SIAMS software solutions package and can be recommended for serial microstructure control in industrial laboratories.
Mechanism for training neural network models used in SIAMS to perform automated recognition of structural elements includes functionality for assessing training quality. Quality is assessed by measuring trained system recognition bias with the value of loss function. Minimum value of the loss function indirectly informs about minimum bias of analysis results for a model.
Figures 8 and 9 show dependency graphs for loss function upon the number of training epochs that were formed in course of training with training and validation sets.
Graph for a training set (figure 8) demonstrates that value of loss function decreases while the number of epochs grows, and the model learns to recognize specific cases. When the number of epochs exceeds 50, the decrease in the value of loss function becomes insignificant.
Minimum for distribution of loss function built using validation set (figure 9) is located in the
region close to 10 learning epochs, which demonstrates that model ends generalization of useful data
and starts analyzing specifics for a given set [
Analysis of the graphs allows understanding that it makes no sense to continue training a given model after a certain number of epochs. Long-term training allows improving object-of-interest recognition for similar cases, but change of conditions (i.e., specifics of specimen preparation, etching, and image capturing) causes increaseв number of errors. Therefore, the training mechanism for models used in SIAMS allows both determining an optimal point to end training, and returning system to the conditions of minimum bias. 2.3.
After complex efforts aimed at integration of neural network technology into SIAMS software, the
following applicable practical results were obtained:
stability of algorithms with regards to instability of external factors [
Paper demonstrates microstructure specifics for high-tensile cast iron, grey cast iron, and alloyed cast iron of Ni-resist type that require use of novel image analysis approaches, and create new opportunities to control quality of different materials.
The use of described methods for cast iron microstructure analysis creates the following advantages for SIAMS software users: ready solution of a specific task in form of a trained algorithm that is stable with regards to conditions of specimen preparation, etching, and image capture; lack of necessity to customize sensitivity thresholds that are usually used in major segmentation methods; ability to use one-step analysis instead of two-step one (graphite analysis before etching and metal matrix analysis after etching); shortened analysis process duration due to abovementioned factors.
Complex approach to research used by developers in order to integrate neural networks into SIAMS software along with development of universal tools for developing new models (or algorithms) allows extending the number of problems solved in the area of image analysis.
Analytical algorithms used to demonstrate solutions on examples used in this paper were integrated into SIAMS software solutions package, and can be recommended for mass control of cast iron microstructure in industrial laboratories.