Metallographic Images Research Articles

Study on the morphological characteristics of coal particle flocs under fractal theory

Based on fractal theory, the geometric size, surface morphology, and spatial structure of flocs in the hydrophobic flocculation process of coal particles were examined via particle size distribution detection, image analysis technology, and numerical simulation. The floc size distribution curve was classified and discussed via the dual-domain fractal rule, and the variation law of the composition and distribution of the coarse- and fine-grained coal particle flocs with the flocculation process was clarified. The various levels and characteristics of the floc growth geometry were explained by the analysis of the fractal dimension of the individual floc surface and the multiple spectra of the floc groups in the metallographic microscopy image. The hydrophobic flocculation process of coal particles was simulated via the diffusion-limited cluster aggregation model. The gyration fractal dimension was used to examine floc fragmentation and reconstruction and clarify the growth law of the floc spatial structure during the hydrophobic flocculation process.

Contour Detection Algorithm for α Phase Structure of TB6 Titanium Alloy fused with Multi-Scale Fretting Features

Aiming at the problems of inaccuracy in detecting the α phase contour of TB6 titanium alloy. By combining computer vision technology with human vision mechanisms, the spatial characteristics of the α phase can be simulated to obtain the contour accurately. Therefore, an algorithm for α phase contour detection of TB6 titanium alloy fused with multi-scale fretting features is proposed. Firstly, through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting, the information maps of the α phase contour of the TB6 titanium alloy at different scales are obtained; then the information map of the smallest scale contour is used as a benchmark, the neighborhood is constructed to judge the deviation of other scale contour information, and the corresponding weight value is calculated; finally, Gaussian function is used to weight and fuse the deviation information, and the contour detection result of TB6 titanium alloy α phase is obtained. In the Visual Studio 2013 environment, 484 metallographic images with different temperatures, strain rates, and magnifications were tested. The results show that the performance evaluation F value of the proposed algorithm is 0.915, which can effectively improve the accuracy of α phase contour detection of TB6 titanium alloy.

Attention Pyramid Dilated Region-based Model for Metallurgical Defect Detection

Abstract Defect recognition is the key to realizing automatic visual inspection and quality control in intelligent manufacturing. Deep learning (DL) has been widely applied to locate damaged areas in images, characterize impurities of materials and further analyze the quality of products. However, automatic defect detection by DL in practical industrial applications is still a challenge due to the lack of sufficient datasets and appropriate recognition methods. Here, a novel Attention Pyramid Dilated Region-based Convolutional Neural Network (ADRCNN) is proposed to realize the multi-scale defect recognition and pixel-level instance segmentation of metallographic images. We also collected a dataset containing 900 images of commercial A356 aluminum alloy with different casting defects. The ADRCNN model is evaluated on our dataset with an average detection accuracy of 87.2% and 93.3% on validation and test sets respectively. To address the overfitting problem, a pretraining fine-tuning strategy is implemented by using the pretraining weights from large-scale datasets and temporarily freezing the weights of the backbone network during the training process. The experimental results show that the proposed model can achieve satisfactory defect segmentation in metallographic images, promoting the development of intelligent manufacturing in alloy industries.

A comparative study on corrosion and mechanical properties of laser-cladded X2CrNiMoN22-5-3 duplex stainless steel on 42CrMo4 steel substrate

Duplex stainless steel is an alloy that combines the advantages of austenitic stainless steel and ferritic stainless steel. It has excellent corrosion resistance, high strength, and good weldability. One of the main problems in marine shafts using X2CrNiMoN22-5-3 Duplex Stainless Steel (2205) is bending and warping over time. In this study, 42CrMO4 (MO40) steel was clad with 2205 dual-phase steel using direct metal deposition with wire to simultaneously utilize the mechanical properties, and corrosion resistance. Perform metallographic imaging, corrosion test, and tensile test, to maintain the quality of the cladding. The results show that the corrosion potential of 2205 cladding layer is −0.65 (including semi-passive zone), while the control sample of 2205 had a corrosion potential of −0.26, and the MO40 substrate had a corrosion potential of −0.1 (highlighting the advantage of the cladding in thermodynamics term). EIS results showed that the REISMO40=2325Ω, REIS2205=24315Ω, and REIS2205cladded=216133Ω. The corrosion rate for the substrate, control sample, and laser-clad sample were 0.33, 0.15, and 0.00095 mm/year, respectively, without significant loss in tensile properties. The final strength for MO40(with 2205 clad) reaches 672 MPa with a 14 % decrease from 788 for MO40. In this case, we still have a 36 % increase in strength compared to the 2205 sample. It seems that the bent problem of the pure 2205 marine shaft can be solved completely in this case and continues to be solved in our ongoing work.

Revolutionizing Grain and Particle Size Measurement in Metals: The Role of Sensor-Assisted Metallographic Image Analysis.

Metallographic image analysis is vital in the field of metal science due to its potential to automate the sensing process for grain and particle size estimation. To ensure the good quality and reliability of metal products, analysis of the integrity of metallic components is required. In contemporary manufacturing processes, microscopic analysis is a crucial step, mainly when complex systems like gearboxes, turbines, or engines are assembled using various components from multiple suppliers. A final product's quality, durability, and lifespan are determined via the quality analysis of properties of a material with proper tolerances. A flaw in a single component can cause the breakdown of the entire finished product. To ensure the good quality of a material, micro-structural analysis is necessary, which includes the routine measurement of inclusions. The particle and grain sizes of particulate samples are the most crucial physical characteristics of metals. Their measurement is routinely conducted across various industries, and they are frequently considered essential parameters in the creation of many products. This paper discusses the role of sensors in enhancing the accuracy and efficiency of metallographic image analysis, as well as the challenges and limitations associated with this technology. The paper also highlights the potential applications of sensor-assisted metallographic image analysis in various industries, such as aerospace, automotive, and construction. The paper concludes by identifying future research directions for this emerging field, including the development of more sophisticated algorithms for grain and particle size estimation, the integration of multiple sensors for more accurate measurements, and the exploration of new sensing modalities for metallographic image analysis.

Effect of laser process parameters on the pores, surface roughness, and hardness of laser selective melting of dental cobalt-chrome alloys.

To address the quality problems caused by high porosity in the preparation of dental cobalt-chrome alloy prosthetics based on selective laser melting (SLM) technology, we investigated the influence mechanism of different forming process parameters on the microstructure and properties of the materials. Moreover, the range of forming process parameters that can effectively reduce defects was precisely defined. The effects of laser power, scanning speed, and scanning distance on the pore properties, surface roughness, and hardness of dental cobalt-chrome alloy were investigated by adjusting the printing parameters in the process of SLM. Through metallographic analysis, image analysis, and molten pool simulation, the pore formation mechanism was revealed, and the relationship between the porosity and energy density of SLM dental cobalt-chrome alloy was elucidated. When the linear energy density was higher than 0.18 J/mm, the porosity defect easily appeared at the bottom of the molten pool. When the laser energy density was lower than 0.13 J/mm, defects occurred in the gap of the molten pool due to insufficient melting of powder. In particular, when the linear energy density exceeded the threshold of 0.30 J/mm or was below 0.12 J/mm, the porosity increased significantly to more than 1%. In addition, we observed a negative correlation between free surface roughness and energy density and an inverse relationship between macroscopic hardness and porosity. On the basis of the conditions of raw materials and molding equipment used in this study, the key process parameters of SLM of molding parts with porosity lower than 1% were successfully determined. Specifically, these key parameters included the line energy density, which ranged from 0.13 J/mm to 0.30 J/mm, and the scan spacing should be strictly controlled below 90 μm.

Effects of Er2O3 contribution on microstructure and mechanical properties of high entropy and dual phase alloys

The study aimed to look at the differences between a high entropy alloy and a dual phase high entropy alloy with high iron and Mn content. It also wanted to find out what happened when a small amount of the rare earth element erbium oxide (Er2O3) was added to these alloys, especially on the mechanical and microstructure of the alloy. The first step in this study was to get 99 % pure metal powders, which were then made by dividing the powders into 30 g of equal weight. The powders were mixed evenly and then pressed into 10-g blocks under 6 tons of pressure. They were then set up to be melted in an arc. Four of the eight samples that were melted in an arc under vacuum were treated with heat. The four different compositions were then turned into small metallographic samples for SEM, EDX, Mapping, XRD, and microhardness tests. The study's imaging and analysis processes were also carried out. After that, SEM images were taken at various magnifications (100X, 500X, 1000X, 2500X, and 5000X), and EDS and XRD analyses were done on different structures found in these images. These analyses were based on literature research about the clarity of the metallographic images in the alloy, which showed the study's metallographic differences. The samples were put through microhardness and wear tests to see which one was stronger and less worn. The dual phase high entropy alloy (A2) was stronger and less worn than the high entropy alloy (A1). Er2O3 had little effect on the alloys' mechanical properties or microstructure. The heat treatment that was done to the samples helped the metallographic structure. In terms of physics, it didn't change the hardness much, but it did make the material wear less quickly.

Effects of electrochemical corrosion on retrogressed and different reaged friction stir welded AA7075-AA6082 dissimilar welds

ABSTRACT The research is primarily to study the microstructure, tensile and electrochemical corrosion behaviour of friction stir welded AA7075-T651-AA6082-T6 dissimilar welds, subjected to retrogression and reaging at 280⁰C for 5 min followed by aging at 110⁰Cfor 1 , 5 , 10 and 24 h respectively. The AA7075-AA6082 welds were made using optimum parameters such as feed rate 80 mm/min, tool rotation speed 980 rpm, and tilt angle 2 o . The alloys of 100 × 50 × 6mm³ plates were welded with a conical tapered tool pin profile. Further, the welds were scanned for potentiodynamic polarisation with a 3.5 percentNaCl solution. Optical metallographic (OM) images were revealed to determine morphology andcorrelate with the measured corrosion rate. Here, the dissimilar friction stir weld AA7075-AA6082-aged for 5 h had an ‘Ultimate tensile strength’ (UTS) of 269.73N/mm2 due to coherent precipitates. Moreover, at 24 h, the lower tensile strength of 226.06 N/mm2 was observed, due to coarser precipitates across the AA7075-AA6082 weld cross-section. Due to over-aged coarse precipitates, the corrosion rate of 6082BM and weld metal had increased to 5.110e + 003mpy and 1.010e + 003mpy. Under other aged conditions, the corrosion rate was decreased below 0.001mpy.

Enhancing precision of defect 3D reconstruction in metal ultrasonic testing through point cloud completion

During the ultrasound multi-layer focused scanning inspection process, the sequential images characterizing the defect morphology suffer from inter-layer contour information loss, which causes a reduction in the accuracy of defect 3D reconstruction, subsequently leading to errors in the characterization of the defect size and morphology. In order to address the above issues, a new method based on the Attention-based Residual Completion Network (ARC) is proposed for ultrasonic point cloud completion to characterize metal defects. Firstly, the ARC network extracts global contour morphological features and local edge detail features from the ultrasonic point cloud through consecutive residual convolutions. Subsequently, the two sets of features are concatenated and finally fed into a decoder based on self-attention, realizing the reconstruction of lost contour information and enhancing the 3D reconstruction accuracy of defects. In the experiment, an ultrasonic microscope was used to inspect actual steel plates. The internal defects were then completed using the ARC network, and the completion results were compared with the metallographic images of the defects. The experiment results indicated that, after completion, the characterization accuracy of defect morphology and sizes is enhanced by an average of 10.31 %. The ARC network provides a novel method for high-precision 3D characterization of internal defects in metal materials.

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed.

The influence of corrosion time on the observation of metallographic structure of 20CrMnMo

20CrMnMo is one of the most widely used alloy steels, and its mechanical properties are closely related to the metallographic structure of the material. Clearly obtaining the distribution pattern of metallographic structure is of great significance for studying the mechanical properties of 20CrMnMo. In this paper, 20CrMnMo samples were subjected to corrosion treatment at different times, and the microstructure of the samples was observed by optical microscope (OM) and Scanning electron microscope (SEM). Through optical microscope observation, the metallographic structure images under different magnification and corrosion time were compared and analyzed, and the best corrosion time interval for metallographic structure observation was obtained; On this basis, by using Scanning electron microscope and EDS, the microscopic images at higher magnification were obtained, and the mechanism of corrosion time on the metallographic structure was further analyzed and explained.

Accelerated semantic segmentation of additively manufactured metal matrix composites: Generating datasets, evaluating convolutional and transformer models, and developing the MicroSegQ+ Tool

Data-driven applications are penetrating every aspect of Additive Manufacturing (AM) to enable efficient resolution of key existing challenges. One such application is semantic segmentation which automatically quantifies post-process structure data from graphic or 3D computer representations. In industrial settings, these models can expedite the development of new materials or processes. However, Additively Manufactured (AMed) materials offer unique challenges, which require new models to be developed and trained. These challenges include out-of-balance classes (e.g., defects), massive labeling efforts, and significant data preparation costs. Recent applications of semantic segmentation in AM have shown the potential of ensemble-based approaches at the expense of increased parameters and computational burden while ignoring the minority classes. In this work, we propose a reproducible method and develop an associated tool to rapidly segment and subsequently quantify industrial AMed metallographic images. First, two new datasets representing AMed materials are generated from extensive experimentation. Subsequently, state-of-the-art models from convolutional and self-attention categories are evaluated in their ability to segment the datasets of interest. Finally, a modular software tool is developed for industrial applications. The developed Microstructure Segmentation, Quantification, and Fusion (+) tool or MicroSegQ+ enables a weighted pairing of predictions from different semantic segmentation models. The pairing strategy helps to exploit the complimentary performance of convolutional and transformer models on multi-class metallographic images from a Metal Matrix Composite (MMC) material system. In addition to developing models on this MMC dataset, the proposed strategy is evaluated on an open-source metal AM dataset. We also evaluate the generalizability of the best-performing model on a second MMC dataset from a different industrial setting that shows an overall accuracy of 93 % without fine-tuning. The proposed tool enables rapid quantification under 1 min, unlike the existing semi-automatic approach that takes 1–2 h of segmentation effort on single bead cross-sections. The tool is wrapped into a single executable file for industrial deployment. The work also contributes to two annotated datasets from a direct energy deposition (DED) AM process. The datasets can be used to develop and validate new semantic segmentation models of AMed microstructures, whereas the functionality of the tool can be expanded upstream to include data and modeling steps, enabling a no-code pipeline for the industry.

Association analysis between the distribution of surface physical characteristics and bonding performance of carbon fiber composites

AbstractEffects of plasma treatment distance on the adhesion properties of carbon fiber composites were investigated, and the association between the distribution of surface physical characteristics on the composites and their bonding properties was discussed. After plasma treatment at different distances, surface morphology of the bonding area was obtained and the roughness distribution model was established. The change in surface physical characteristics directly influences the wettability of the bonding surface and the tensile shear strength (TSS) of the bonded components. When the TSS showed an increase by 19.47% at the treatment distance of 15 mm, metallographic images indicated that the bonding layer was integrated with the composites and the joint in the component was dominated by cohesive failure. When the TSS showed a decrease by 30.72% at the treatment distance of 5 mm, metallographic images indicated that voids and delamination were introduced in the bonding layer and fiber tearing failure mode appeared in the plasma etched area. Moisture‐heat resistance of the bonded components was also considered and a noticeable increase in water absorption was observed in the case of excessive treatment at the distance of 5 mm, which was also related to the distribution of physical characteristics induced by the plasma treatment.Highlights Surface characteristic distribution of plasma‐modified composites was analyzed. State of resin and fibers on composite surface varied with the treatment distance. Semi‐annular plasma treatment traces appeared in the roughness distribution. The high roughness in the distribution played a key role in bonding properties. Performance degradation due to close treatment was also distribution‐dependent.

Headed coupling behavior of large diameter Cu-Al-Mn shape memory alloy bars: Mechanical testing and microstructural analyses

Cu-Al-Mn (CAM) superelastic alloy bars (SEAs) have received increasing attention in bridge applications. To take full advantage of their strain recovery and energy dissipation capacity, CAM SEAs are usually only applied in the plastic hinge regions of columns as part of the longitudinal reinforcement and coupled with steel rebar through mechanical splices. In this study, the feasibility of using headed coupling to connect large diameter CAM SEAs with steel rebar was investigated. Five large diameter CAM SEAs with headed ends were prepared: one 30 mm diameter and four 20 mm diameter samples. First, mechanical tests were performed on the five headed samples where each sample was coupled with one steel rebar at each end. Monotonic, incremental and constant strain cyclic loading was applied to simulate earthquake loading. The key mechanical properties were extracted and discussed. Second, microstructural analyses including electron backscatter diffraction (EBSD), metallographic imaging, Vickers hardness testing and fractographic evaluation were performed. The crystal orientation, phase composition and fracture surfaces were investigated to understand the effect of heading process on the stress induced martensitic transformation (SIMT), phase composition and failure of the headed CAM SEAs. It was found that the heading process only affects the near end portion of the specimen and it has no influence in the middle portion of the bars. Therefore, the strain recovery capacity of the CAM SEAs after heading was not reduced. Heading process led to high density of bainite phase precipitation at the end portions, which strengthened the sample. In order to uniformly strengthen the headed end of large diameter CAM SEAs, the key is to ensure a consistent cooling rate in the central and peripheral regions of the headed end after the heading process.

Study on the High-Temperature Interaction between Coke and Iron Ores with Different Layer Thicknesses.

Coke plays a key role as the skeleton of the charge column in BF. The gas path formed by the coke layer in the BF has a decisive influence on gas permeability. At high temperatures, the interface between coke and ore undergoes a melting reaction of coke and a reduction reaction of ore. The better the reducibility of the ore, the more conducive it is to the coupling reaction of ore and coke. The melting loss reaction of coke becomes more intense, and the corresponding strength of coke will decrease, which will affect the permeability of the blast furnace and is not conducive to the smooth operation of the blast furnace. Especially with a deterioration in iron ore quality, BF operation faces severe challenges, which makes it necessary to find an effective way to strengthen BF operation. In this study, a melting-dropping furnace was used to develop and clarify the high-temperature interaction between coke and iron ores with different layer thicknesses. The influencing factors were studied by establishing a gas permeability mathematical model and observing the metallographic microscope images of samples after the coke solution loss reaction. The relationships between coke layer thickness, distribution of gas flow, and pressure drop were obtained. The results showed that, under certain conditions, the gas permeability property of a furnace burden has been improved after the coke layer thickness increased. Upon observing the size of coke particles at the interface reaction site, the degree of melting loss reaction can be determined. A smaller particle size indicates more melting loss reaction. A dripping eigenvalue for molten metal was introduced to evaluate the dynamic changes in the comprehensive dripping properties of molten metal of furnace burden, which showed that the dripping eigenvalue for the molten metal could deteriorate because of the unruly thickness and the coke layer thickness should be limited through considering the operational indicators of the blast furnace.

Integration of Artificial Intelligence into Metallography: Area-wide Analysis of Microstructural Components of a Jominy Sample

Abstract Analysing the microstructure is an essential part of quality control in many steel manufacturing and processing operations. In this work, a promising method for autonomous analysis of microstructures in low-alloy steels based on artificial intelligence image analysis is presented. This study focuses on the classification of different microstructure components in metallographic images of steel microstructures using a Deep Convolutional Neural Network (DCNN) model. Since the accuracy of the model strongly depends on the size of the data set, a data set consisting of two million optical microscopy images was created to ensure the presence of different microstructure components and their combinations for training the system. The Jominy test was performed to verify the accuracy and capability of the microstructure analysis software. The AI makes it possible to analyse large amounts of image data with high precision and at the same time with less effort than conventional methods of microstructure components analysis.

Deep Learning-Based Automatic Defect Detection of Additive Manufactured Stainless Steel

Accumulating interest from academia and industry, the part of quality assurance in metal additive manufacturing (AM) is achieving incremental recognition owing to its distinct advantages over conventional manufacturing methods. In this paper, we introduced a convolutional neural network, YOLOv8 approach toward robust metallographic image quality inspection. Metallographic images accommodate key information relating to metal properties, such as structural strength, ductility, toughness, and defects, which are employed to select suitable materials for multiple engineering execution. Therefore, by comprehending the microstructures, one can understand insights into the behavior of a metal component and make predictive assessments of failure under specific conditions. Deep learning-based image segmentation is a robust technique for the detection of microstructural defects like cracks, inclusion, and gas porosity. Therefore, we improvise the YOLOv8 with dilated convolution mechanisms to acquire automatic micro-structure defect characterization. More specifically, for the first time, the YOLOv8 algorithm was proposed in the metallography dataset from additive manufacturing of steels (Metal DAM) to identify defects like cracks and porosity as a novel approach. A total of 414 images from ArcelorMittal engineers were used as an open-access database. The experimental results demonstrated that the YOLOv8 model successfully detected and identified cracks and porosity in the metal AM dataset, achieving an improved defect detection accuracy of up to 96% within just 0.5 h compared to previous automatic defect recognition processes.

The Influence of Sealing Processes and Machining Operations on the Scratch and Wear Resistance of Anodized AlSi9Cu3(Fe) Diecasting Alloy

The effects of hydrothermal and cold sealing processes on the scratch and wear resistance of the anodic layer have been studied. High-pressure diecast AlSi9Cu3(Fe) alloy plates were anodized in a sulfuric acid electrolyte at 16°C and further sealed in boiled water or in a NiF2 solution at 25°C. To analyze the influence of pre-anodizing machining operations, the plates were studied in the as-diecast condition and after milling. Metallographic investigations and image analysis techniques were carried out to study the morphology and thickness of the anodic layer. Hardness, wear, and scratch measurements were also performed to characterize the surface mechanical properties. The results showed that the sealing processes enhanced the wear and scratch resistance of the anodized surfaces because of the precipitation of hydrates that sealed the surface porosity. The thicker oxide layer formed on the milled substrate led to a greater wear resistance compared to the as-diecast surface, owing to reduced wear of the underlying aluminum substrate. Finally, a cracked mechanically mixed layer, which was enriched with fragmented intermetallics and anodic layer debris, was formed on the anodized surfaces at the end of the wear tests.

Wire melted mark metallographic image recognition and classification based on semantic segmentation

The recognition and classification of wire melted marks is crucial in modern fire investigation. The existing technology mainly uses physical or chemical methods to deal with wire melted marks and draws conclusions through manual observation, or manually extracts the features and train classification model, both of which consume excessive manpower and resources. The research on automatic feature extraction and recognition of wire weld marks by artificial intelligence technology is still blank. Based on the data set of wire melted mark metallographic images provided by a city fire research institute, we proposed an algorithm to recognize the type of wire melted mark metallographic images based on artificial intelligence which can help fire fighters efficiently speculate the cause of fire. In the algorithm, the TransUnet network is used to segment the melted zone by semantic segmentation to extract the melted zone containing the main features, and the mIOU reaches 92.2%. Then, the features of wire melted mark are extracted based on the melted zone image. Finally, XGBoost is used for feature modeling for classification. The F1 Score of model is 82.9%.

Automatic Measurement in Metallography

Quantitative analysis of the structure of metals and alloys is an important part of modern metal science. To obtain quantitative data and build dependencies, metallographic image processing programs are used, oriented both for scientific research and for use in industry. Programs capable of automatically performing metallographic analysis are of great interest to consumers. When advertising such programs, it is often claimed that they allow quantitative analysis of the structure with virtually no time. The purpose of this work was to determine the time spent on quantitative metallographic analysis in some image processing programs presented on the Belarusian market. Connected and unconnected metallographic objects were considered. It is shown that automatic quantitative analysis is possible for unconnected objects (powders, cast iron graphite). The time required is within a minute. For connected objects (structures of metals and alloys after metallographic etching), the time required to detect objects and obtain digital data is 10–40 min or more, depending on the complexity of the object, which is unacceptable for factory laboratories that analyze a large number of samples per shift. Therefore, it is recommended that potential users of metallographic image processing software always require a substantive demonstration of the automatic measurement capabilities of the proposed software.