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Advanced Applications of Artificial Intelligence in Metallic Materials Processing

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A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 18248

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Dr. Panorios Benardos

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School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: modelling and optimization of manufacturing processes and systems; industrial robotics in manufacturing; machine learning; machine vision; industrial internet of things (IIoT)
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Special Issue Information

Dear Colleagues,

For the last 50 years, manufacturing processes have been relying on automation and information technologies, thereby exhibiting an inter-disciplinary nature, to solve the ongoing challenge of optimizing productivity, quality and cost. However, a major digital transformation is currently taking place, as part of the 4^th Industrial Revolution or Industry 4.0, at a rate and on a scale never before experienced. This is due to scientific and technological advances in several fields that, when combined, enable the collection and efficient processing of unprecedented amounts of data. Artificial intelligence techniques have re-emerged and can now be found at the core of the latter due to their abilities to reveal underlying interactions and patterns as well as to support optimal decision-making strategies.

This Special Issue aims to highlight such advanced applications of artificial intelligence in metallic materials processing covering process modeling and simulation, process planning, real-time process monitoring and fault detection, in-process quality control, automated part handling and inspection. Real-world case studies that provide insights into the associated challenges, implementations and achieved benefits are especially welcome.

Dr. Panorios Benardos
Guest Editor

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Keywords

Manufacturing process modeling and simulation
Real-time process monitoring and optimization
In-process quality control
Automated part handling
Automated part inspection
Digital twins
Decision support systems
Artificial neural networks
Evolutionary algorithms
Machine vision
Machine learning

Published Papers (10 papers)

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Research

18 pages, 4895 KiB

Open AccessArticle

Inverse Design of Aluminium Alloys Using Genetic Algorithm: A Class-Based Workflow

by Ninad Bhat, Amanda S. Barnard and Nick Birbilis

Metals 2024, 14(2), 239; https://doi.org/10.3390/met14020239 - 16 Feb 2024

Viewed by 929

Abstract

The design of aluminium alloys often encounters a trade-off between strength and ductility, making it challenging to achieve desired properties. Adding to this challenge is the broad range of alloying elements, their varying concentrations, and the different processing conditions (features) available for alloy production. Traditionally, the inverse design of alloys using machine learning involves combining a trained regression model for the prediction of properties with a multi-objective genetic algorithm to search for optimal features. This paper presents an enhancement in this approach by integrating data-driven classes to train class-specific regressors. These models are then used individually with genetic algorithms to search for alloys with high strength and elongation. The results demonstrate that this improved workflow can surpass traditional class-agnostic optimisation in predicting alloys with higher tensile strength and elongation. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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15 pages, 2127 KiB

Open AccessArticle

Hybrid-Input FCN-CNN-SE for Industrial Applications: Classification of Longitudinal Cracks during Continuous Casting

by Davi Alberto Sala, Andy Van Yperen-De Deyne, Erik Mannens and Azarakhsh Jalalvand

Metals 2023, 13(10), 1699; https://doi.org/10.3390/met13101699 - 6 Oct 2023

Cited by 1 | Viewed by 795

Abstract

In the presented research, machine learning methods were applied to the prediction of longitudinal cracks in steel slabs during continuous casting. We employ a deep learning approach to process 68 thermocouple signals as a multivariate time series (MTS) along with 32 static features, which encompass both chemical composition and process information. Our deep learning approach integrates two distinct parallel modules, followed by an aggregation block; a Convolutional Neural Network (CNN) processes the thermocouple MTS, while in parallel, the static data undergo processing via a Fully Connected Network (FCN). To enhance the performance of the CNN, we incorporate two Squeeze and Excitation (SE) blocks, which act as an attention mechanism across different channels. By integrating chemical information with MTS in the detection system, we improve the performance of defect detection by 15% relatively. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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27 pages, 14716 KiB

Open AccessArticle

Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning

by Björn-Ivo Bachmann, Martin Müller, Dominik Britz, Thorsten Staudt and Frank Mücklich

Metals 2023, 13(8), 1395; https://doi.org/10.3390/met13081395 - 3 Aug 2023

Cited by 1 | Viewed by 1266

Abstract

Current conventional methods of evaluating microstructures are characterized by a high degree of subjectivity and a lack of reproducibility. Modern machine learning (ML) approaches have already shown great potential in overcoming these challenges. Once trained with representative data in combination with objective ground truth, the ML model is able to perform a task properly in a reproducible and automated manner. However, in highly complex use cases, it is often not possible to create a definite ground truth. This study addresses this problem using the underlying showcase of microstructures of highly complex quenched and quenched and tempered (Q/QT) steels. A patch-wise classification approach combined with a sliding window technique provides a solution for segmenting entire microphotographs where pixel-wise segmentation is not applicable since it is hardly feasible to create reproducible training masks. Using correlative microscopy, consisting of light optical microscope (LOM) and scanning electron microscope (SEM) micrographs, as well as corresponding data from electron backscatter diffraction (EBSD), a training dataset of reference states that covers a wide range of microstructures was acquired in order to train accurate and robust ML models in order to classify LOM or SEM images. Despite the enormous complexity associated with the steels treated here, classification accuracies of 88.8% in the case of LOM images and 93.7% for high-resolution SEM images were achieved. These high accuracies are close to super-human performance, especially in consideration of the reproducibility of the automated ML approaches compared to conventional methods based on subjective evaluations through experts. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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20 pages, 26252 KiB

Open AccessArticle

Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks

by Juan C. Buitrago Diaz, Carolina Ortega-Portilla, Claudia L. Mambuscay, Jeferson Fernando Piamba and Manuel G. Forero

Metals 2023, 13(8), 1391; https://doi.org/10.3390/met13081391 - 2 Aug 2023

Viewed by 1286

Abstract

The study of material hardness is crucial for determining its quality, potential failures, and appropriate applications, as well as minimizing losses incurred during the production process. To achieve this, certain criteria must be met to ensure high quality. This process is typically performed manually or using techniques based on analyzing indentation image patterns produced through the Vickers hardness technique. However, these techniques require that the indentation pattern is not aligned with the image edges. Therefore, this paper presents a technique based on convolutional neural networks (CNNs), specifically, a YOLO v3 network connected to a Dense Darknet-53 network. This technique enables the detection of indentation corner positions, measurement of diagonals, and calculation of the Vickers hardness value of D2 steel treated thermally and coated with Titanium Niobium Nitride (TiNbN), regardless of their position within the image. By implementing this architecture, an accuracy of

92 %

was achieved in accurately detecting the corner positions, with an average execution time of 6 seconds. The developed technique utilizes the network to detect the regions containing the corners and subsequently accurately determines the pixel coordinates of these corners, achieving an approximate relative percentage error between

0.17 %

5.98 %

in the hardness results. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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23 pages, 5745 KiB

Open AccessArticle

Defect Recognition in High-Pressure Die-Casting Parts Using Neural Networks and Transfer Learning

by Georgia Andriosopoulou, Andreas Mastakouris, Dimosthenis Masouros, Panorios Benardos, George-Christopher Vosniakos and Dimitrios Soudris

Metals 2023, 13(6), 1104; https://doi.org/10.3390/met13061104 - 12 Jun 2023

Cited by 2 | Viewed by 1693

Abstract

The quality control of discretely manufactured parts typically involves defect recognition activities, which are time-consuming, repetitive tasks that must be performed by highly trained and/or experienced personnel. However, in the context of the fourth industrial revolution, the pertinent goal is to automate such procedures in order to improve their accuracy and consistency, while at the same time enabling their application in near real-time. In this light, the present paper examines the applicability of popular deep neural network types, which are widely employed for object detection tasks, in recognizing surface defects of parts that are produced through a die-casting process. The data used to train the networks belong to two different datasets consisting of images that contain various types of surface defects and for two different types of parts. The first dataset is freely available and concerns pump impellers, while the second dataset has been created during the present study and concerns an automotive part. For the first dataset, Faster R-CNN and YOLOv5 detection networks were employed yielding satisfactory detection of the various surface defects, with mean average precision (mAP) equal to 0.77 and 0.65, respectively. Subsequently, using transfer learning, two additional detection networks of the same type were trained for application on the second dataset, which included considerably fewer images, achieving sufficient detection capabilities. Specifically, Faster R-CNN achieved mAP equal to 0.70, outperforming the corresponding mAP of YOLOv5 that equalled 0.60. At the same time, experiments were carried out on four different computational resources so as to investigate their performance in terms of inference times and consumed power and draw conclusions regarding the feasibility of making predictions in real time. The results show that total inference time varied from 0.82 to 6.61 s per image, depending on the computational resource used, indicating that this methodology can be integrated in a real-life industrial manufacturing system. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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13 pages, 1807 KiB

Open AccessArticle

Material Quality Filter Model: Machine Learning Integrated with Expert Experience for Process Optimization

by Xuandong Wang, Hao Li, Tao Pan, Hang Su and Huimin Meng

Metals 2023, 13(5), 898; https://doi.org/10.3390/met13050898 - 5 May 2023

Cited by 1 | Viewed by 1224

Abstract

In the process of material production, the mismatch between raw material parameters and manufacturing processing parameters may lead to fluctuations in product properties and ultimately to unstable or unqualified product quality. In this paper, we propose the concept of the Quality Filter model for process optimization. The Quality Filter model uses the property prediction model as a surrogate model and integrates expert experience and process window constraints to construct a loss function. When raw material parameters are supplied, the suitable processing parameters can be automatically matched, and the processing fluctuation can be used to hedge the fluctuations in raw material, thus stabilizing the product quality and improving overall product properties. A trial production data set of 128 samples of wind power steel from a steel plant was used to test the model. We selected the ellipsoid discriminant analysis model with a classification accuracy rate of 82.81% as the surrogate model, which gives a highly interpretable visualization result. Finally, the results show that the properties of the samples that underwent the optimized process are improved. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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17 pages, 4662 KiB

Open AccessArticle

Fabrication and Characterization of SiC-reinforced Aluminium Matrix Composite for Brake Pad Applications

by Arpita Chatterjee, Soumyadeep Sen, Subhodeep Paul, Pallab Roy, Asiful H. Seikh, Ibrahim A. Alnaser, Kalyan Das, Goutam Sutradhar and Manojit Ghosh

Metals 2023, 13(3), 584; https://doi.org/10.3390/met13030584 - 13 Mar 2023

Cited by 6 | Viewed by 2368

Abstract

The wear debris from conventional brake pads is a growing source of environmental contamination that often leads to life-threatening diseases for human beings. Though the emerging organic brake pads show potential to serve as an eco-friendly alternative, their mechanical and tribological properties are not adequate to withstand the demands of high-wear resistance of a functioning braking system under regular use. Metal matrix composites have served as an optimal solution with minimal environmental pollution and appreciable physical properties. Owing to the popularity of aluminium metal matrix composites, the present study is based on the fabrication and characterization of SiC-reinforced LM6 alloy through stir casting methodologies for evaluating its worthiness in application as a brake pad material. Microstructural, compositional, and phase characterizations were executed through optical micrography, X-ray diffraction, and energy-dispersive X-ray spectroscopy analysis. Although mechanical properties were evaluated through surface hardness investigation, parallel thermal properties were estimated through thermal conductivity evaluation. Finally, the execution of tribological analysis and precise microstructural observations of wear track at ambient and elevated temperatures helped in establishing the datum that the fabricated metal matrix composite (MMC) is a reliable brake pad material alternative. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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19 pages, 3803 KiB

Open AccessArticle

Intelligent Systems to Optimize and Predict Machining Performance of Inconel 825 Alloy

by Abdulsalam Abdulaziz Al-Tamimi and Chintakindi Sanjay

Metals 2023, 13(2), 375; https://doi.org/10.3390/met13020375 - 12 Feb 2023

Cited by 1 | Viewed by 1536

Abstract

Intelligent models are showing an uprise in industry and academia to optimize the system’s outcome and adaptability to predict challenges. In machining, there is difficulty of unpredictability to the part performance especially in super alloys. The aim of this research is to propose an intelligent machining model using contemporary techniques, namely, combinative distance-based assessment (CODAS), artificial neural network (ANN), adaptive neuro-fuzzy inference systems, and particle swarm optimization (ANFIS-PSO) approach for minimizing resultant force, specific cutting energy, and maximizing metal removal rate. Resultant force response has shown to be affected by feed rate and cutting speed with a contribution of 54.72% and 41.67%, respectively. Feed rate and depth of cut were statistically significant on metal removal rate contributing with the same value of 38.88%. Specific cutting energy response resulted to be statistically significant toward feed rate with 43.04% contribution and 47.81% contribution by depth of cut. For the CODAS approach, the optimum parameters are cutting speed of 70 m/min, feed of 0.33 mm/rev, and depth of cut of 0.6 mm for the seventh experiment. The estimated values predicted by the ANN and ANFIS method were close to the measured values compared to the regression model. The ANFIS model performed better than the ANN model for predicting turning of the Inconel 825 alloy. As per quantitative analysis, these two models are reliable and robust, and their potential as better forecasting tools can be used for hard-to-machine materials. For hybrid ANFIS-PSO, the optimum parameters for minimizing resulting force were (82, 0.11, 0.15), for minimizing specific cutting energy (45, 0.44 and 0.6) and maximizing metal removal rate (101, 0.43, 0.54). The hybrid model ANFIS-PSO has proven to be a better approach and has good computational efficiency and a lower discrepancy in assessment. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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15 pages, 6651 KiB

Open AccessArticle

A Tool for Rapid Analysis Using Image Processing and Artificial Intelligence: Automated Interoperable Characterization Data of Metal Powder for Additive Manufacturing with SEM Case

by Georgios Bakas, Spyridon Dimitriadis, Stavros Deligiannis, Leonidas Gargalis, Ioannis Skaltsas, Kyriaki Bei, Evangelia Karaxi and Elias P. Koumoulos

Metals 2022, 12(11), 1816; https://doi.org/10.3390/met12111816 - 26 Oct 2022

Cited by 3 | Viewed by 3090

Abstract

A methodology for the automated analysis of metal powder scanning electron microscope (SEM) images towards material characterization is developed and presented. This software-based tool takes advantage of a combination of recent artificial intelligence advances (mask R-CNN), conventional image processing techniques, and SEM characterization domain knowledge to assess metal powder quality for additive manufacturing applications. SEM is being used for characterizing metal powder alloys, specifically by quantifying the diameter and number of spherical particles, which are key characteristics for assessing the quality of the analyzed powder. Usually, SEM images are manually analyzed using third-party analysis software, which can be time-consuming and often introduces user bias into the measurements. In addition, only a few non-statistically significant samples are taken into consideration for the material characterization. Thus, a method that can overcome the above challenges utilizing state-of-the-art instance segmentation models is introduced. The final proposed model achieved a total mask average precision (mAP50) 67.2 at an intersection over union of 0.5 and with prediction confidence threshold of 0.4. Finally, the predicted instance masks are further used to provide a statistical analysis that includes important metrics such as the particle size distinction. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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16 pages, 2703 KiB

Open AccessArticle

An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles

by Zhiyang Li, Bin Li, Hongjun Ni, Fuji Ren, Shuaishuai Lv and Xin Kang

Metals 2022, 12(11), 1809; https://doi.org/10.3390/met12111809 - 25 Oct 2022

Cited by 10 | Viewed by 1872

Abstract

The automatic classification of aluminum profile surface defects is of great significance in improving the surface quality of aluminum profiles in practical production. This classification is influenced by the small and unbalanced number of samples and lack of uniformity in the size and spatial distribution of aluminum profile surface defects. It is difficult to achieve high classification accuracy by directly using the current advanced classification algorithms. In this paper, digital image processing methods such as rotation, flipping, contrast, and luminance transformation were used to augment the number of samples and imitate the complex imaging environment in actual practice. A RepVGG with CBAM attention mechanism (RepVGG-CBAM) model was proposed and applied to classify ten types of aluminum profile surface defects. The classification accuracy reached 99.41%, in particular, the proposed method can perfectly classify six types of defects: concave line (cl), exposed bottom (eb), exposed corner bottom (ecb), mixed color (mc), non-conductivity (nc) and orange peel (op), with 100% precision, recall, and F1. Compared with the existing advanced classification algorithms VGG16, VGG19, ResNet34, ResNet50, ShuffleNet_v2, and basic RepVGG, our model is the best in terms of accuracy, macro precision, macro recall and macro F1, and the accuracy was improved by 4.85% over basic RepVGG. Finally, an ablation experiment proved that the classification ability was strongest when the CBAM attention mechanism was added following Stage 1 to Stage 4 of RepVGG. Overall, the method we proposed in this paper has a significant reference value for classifying aluminum profile surface defects. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Metallic Materials Processing)

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