Application of Machine Learning Modeling in Establishing the Process, Structure, and Property Relationships of the Cast-Forged AZ80 Magnesium Alloy
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Date
2024-05-27
Authors
Azqadan, Erfan
Journal Title
Journal ISSN
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Publisher
University of Waterloo
Abstract
The cast-forging process is a novel hybrid manufacturing paradigm that leverages the cost effectiveness of cast product while alleviating its structural and durability weaknesses through forging. Therefore, the cast-forging process is a promising candidate for production of AZ80 magnesium alloy structural components with potential use in automotive and aerospace industries. In this novel method, the low formability of magnesium alloys at room temperature for near net shape forming is removed by elevated temperature forging of magnesium products. Also, the flexibility of casting in producing complex shapes is leveraged, and its low mechanical properties is enhanced through considerable deformation of AZ80 alloy offered by forging. The cast-forging manufacturing method takes advantage of possible microstructure variations induced in the material via different cast geometries and/or processing parameters. Therefore, this novel method can produce reliable lightweight magnesium structural components. Currently, there is limited knowledge of the effects of initial cast microstructure on the hot deformation behavior of AZ80 alloy. The current study aims to establish a link between casting process parameters that controls the microstructure of cast material and their effects on forging process. Due to the complexity of the relationship between process parameters, microstructure, and properties of cast-forged AZ80 magnesium alloys, advanced characterization methods and data-driven models are used to establish this link.
In this work, it is shown that casting cooling rate controls the matrix grain size and the morphology and distribution of intermetallic particles formed during and after solidification. These microstructural features influence dynamic recrystallization (DRX) during the forging process that affects further formability of the material. Also, the x-ray computed tomography (XCT) analysis of cast material shows the role of casting process parameters on the formation of porosities and their effect on mechanical properties. Moreover, several different morphologies of the Mg17Al12 intermetallic compound forms during the casting and forging processes. The evolution of the Mg17Al12 intermetallic during casting, pre-forging heat treatment, and forging process occurs due to breakage, dissolution, and precipitation of this phase. Different Mg17Al12 intermetallic morphologies affect DRX phenomenon. Since the final microstructure and mechanical properties of the cast-forged component is controlled by occurrence of DRX, a detailed investigation of the interactions between the Mg17Al12 intermetallic and DRX is conducted. This study shows, as previously suggested by the literature, the eutectic, lamellar, and discontinuous morphologies of the Mg17Al12 phase promote DRX through particle stimulated nucleation (PSN) mechanism. However, in contrast to the literature, this study finds that the continuous Mg17Al12 morphology when broken as a result of severe plastic deformation can also promote DRX occurrence.
The combination of casting and forging process parameters can result in a wide range of deformation behaviors and consequently varied microstructure and final mechanical properties. An informed search for optimal manufacturing route based on desired final mechanical properties requires modeling of materials evolution to accelerate research. Therefore, the application of data-driven modeling methods to establish the process-structure-property relationships of this system is studied. In this regard, an artificial neural network (ANN)-based screening tool is developed using more than 800 hardness measurements conducted on 12 different cast-forged components. This process-to-property model takes casting cooling rate and forging temperature to predict the hardness distribution of the cast-forged components. The hardness of materials correlates with several other properties such as tensile strength and resistance to deformation. This model, which requires no characterization of the material, can be used to find the most optimal combination of the process parameters that might satisfy the mechanical properties requirements. The application of this model for unseen combination of the process parameters is investigated and shows the robustness of the model as a screening tool.
In order to develop a mesoscale microstructure model predicting the microstructure evolution based on process parameters, image generative machine learning models, namely generative adversarial network (GAN) and denoising diffusion probabilistic model (DDPM) are implemented. Capitalizing on the enhanced data distribution capturing characteristic of DDPM, it is utilized for the final model. This process-to-microstructure model, trained on 434 high-resolution SEM images from 27 cast-forged samples, takes parameters like the casting geometry, casting cooling rate, pre-forging heat treatment, pre-forging soaking process, forging temperature, metallography extraction location, and image magnification, to produces convincing high-resolution synthesized SEM images for seen and unseen process parameter combinations. To evaluate the predictive capabilities of this proposed approach, computer vision and morphological feature metrics are analyzed for the real and synthesized images, revealing the model’s ability to capture underlying physical relationships, such as grain size, Mg17Al12 morphology and area fraction, distribution of morphological features, and DRX percentage within cast-forged AZ80 SEM images. To the best of our knowledge, this represents the most comprehensive study of machine learning image generative models aimed at producing high-resolution microstructure images.
The establishment of the relationship between process parameters, microstructure, and mechanical properties in this work aims to facilitate the search for optimum processing route for production of the AZ80 cast-forged front lower control arm (FLCA) component with superior mechanical properties compared to previous attempts and the aluminum alloy-based benchmark. In this regard, an Image-based machine learning model is also developed, based on 377 SEM images and tensile test results of 27 cast-forged components, to predict the yield strength, ultimate tensile strength, and elongation to failure of the cast-forged AZ80 alloy directly from the SEM microstructure images. The proposed process-to-microstructure and microstructure-to-property machine learning models provide an end-to-end framework to explore the possible microstructure and property spaces of this system. This framework is implemented using internally developed, custom-built Python scripts and leverages the PyTorch library.
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Keywords
machine learning, manufacturing, cast-forging, magnesium alloys, microstructure, mechanical properties, modeling, process-structure-property relationship