Pub Date : 2024-05-15DOI: 10.21741/9781644903131-200
P. Prates
Abstract. This work focuses on predicting material parameters that describe the plastic behaviour of metallic sheets using the XGBoost machine learning algorithm, with a dual focus on the influence of data filtering and data noise. A dataset was populated with finite element simulation results of cruciform tensile tests, including strain field data during the test. Different noise levels were added to the strain-related features of the dataset; additionally, a feature importance study was carried out to identify and select the most relevant features of the dataset. A systematic analysis shows how feature noise and selection individually and simultaneously influence the predictive performance of machine learning models. The results show that feature selection will greatly accelerate model training, without losing its predictive performance. Also, adding noise to the features does not have significant impact on model performance, highlighting the robustness of the models.
{"title":"Influence of data filtering and noise on the calibration of constitutive models using machine learning techniques","authors":"P. Prates","doi":"10.21741/9781644903131-200","DOIUrl":"https://doi.org/10.21741/9781644903131-200","url":null,"abstract":"Abstract. This work focuses on predicting material parameters that describe the plastic behaviour of metallic sheets using the XGBoost machine learning algorithm, with a dual focus on the influence of data filtering and data noise. A dataset was populated with finite element simulation results of cruciform tensile tests, including strain field data during the test. Different noise levels were added to the strain-related features of the dataset; additionally, a feature importance study was carried out to identify and select the most relevant features of the dataset. A systematic analysis shows how feature noise and selection individually and simultaneously influence the predictive performance of machine learning models. The results show that feature selection will greatly accelerate model training, without losing its predictive performance. Also, adding noise to the features does not have significant impact on model performance, highlighting the robustness of the models.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"117 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-155
Robert Oliver Jung
Abstract. In the context of increasing resource efficiency and profitability, deep drawing can be improved using a digital twin and closed-loop process control. Cyber-physical production systems (CPPS) enable data capture and analysis for an autonomous optimization of the manufacturing process. In this work reference sensor signals are used to control the force and material flow with hydraulic actuators between the blank holder and the die. A novel model-based optimization method is proposed to determine the best sensor location, allowing for standardized evaluation and reduced integration time. FE simulations and forming trials are conducted for validation. The findings indicate time and resource savings through an efficient sensor implementation in deep drawing tools for process control.
摘要在提高资源利用效率和盈利能力的背景下，可以利用数字孪生和闭环过程控制来改进拉深工艺。网络物理生产系统（CPPS）可进行数据采集和分析，从而自主优化生产过程。在这项工作中，参考传感器信号被用于控制坯料支架和模具之间液压致动器的力和材料流。提出了一种基于模型的新型优化方法来确定最佳传感器位置，从而实现标准化评估并缩短集成时间。为进行验证，还进行了 FE 模拟和成型试验。研究结果表明，通过在深拉工具中有效安装传感器来实现过程控制，可以节省时间和资源。
{"title":"Sensor integration for process control in deep drawing","authors":"Robert Oliver Jung","doi":"10.21741/9781644903131-155","DOIUrl":"https://doi.org/10.21741/9781644903131-155","url":null,"abstract":"Abstract. In the context of increasing resource efficiency and profitability, deep drawing can be improved using a digital twin and closed-loop process control. Cyber-physical production systems (CPPS) enable data capture and analysis for an autonomous optimization of the manufacturing process. In this work reference sensor signals are used to control the force and material flow with hydraulic actuators between the blank holder and the die. A novel model-based optimization method is proposed to determine the best sensor location, allowing for standardized evaluation and reduced integration time. FE simulations and forming trials are conducted for validation. The findings indicate time and resource savings through an efficient sensor implementation in deep drawing tools for process control.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"129 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-137
João P. Brito
Abstract. The development of more sophisticated constitutive models is essential for improving the reliability of metal forming process simulations. The main objective of this work is to employ a Gurson-type [1] porous criterion to assess the ductile damage distribution of a strongly textured AA5042-H2 sheet during a single-stage cup-drawing process. The anisotropy of the dense phase is described with the non-quadratic form of the CPB06ex2 [2] criterion using two linear transformations. In line with Gurson’s homogenization theory, the plastic behavior of the porous solid is described by an approximate macroscopic strain-rate potential (SRP) using the classical Rice and Tracey trial fields. The particularity of this implementation is that the macroscopic potentials are not evaluated via analytical functions, but by numerical integration of the local fields [3]. It is shown that such approach is viable from the computational standpoint and opens the door for materials with intricate plastic behavior to be modeled within the framework of porous media.
{"title":"Modelling ductile damage of a textured aluminum alloy based on a non-quadratic yield function","authors":"João P. Brito","doi":"10.21741/9781644903131-137","DOIUrl":"https://doi.org/10.21741/9781644903131-137","url":null,"abstract":"Abstract. The development of more sophisticated constitutive models is essential for improving the reliability of metal forming process simulations. The main objective of this work is to employ a Gurson-type [1] porous criterion to assess the ductile damage distribution of a strongly textured AA5042-H2 sheet during a single-stage cup-drawing process. The anisotropy of the dense phase is described with the non-quadratic form of the CPB06ex2 [2] criterion using two linear transformations. In line with Gurson’s homogenization theory, the plastic behavior of the porous solid is described by an approximate macroscopic strain-rate potential (SRP) using the classical Rice and Tracey trial fields. The particularity of this implementation is that the macroscopic potentials are not evaluated via analytical functions, but by numerical integration of the local fields [3]. It is shown that such approach is viable from the computational standpoint and opens the door for materials with intricate plastic behavior to be modeled within the framework of porous media.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"127 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-128
Hervé Laurent
Abstract. Warm forming at 200°C is an interesting way of improving the formability of AA7075-T6. The T6 condition offers the highest ultimate and yield strengths of this aluminium alloy. It is therefore important to maintain these excellent mechanical properties at the end of the forming process. With the hot forming process at temperature above 450°C, it is necessary to add a heat recovery treatment obtained during paint baking to keep this T6 state, which is costly in terms of time and energy. In warm forming process, it is possible to maintain the T6 condition by controlling the heating time to avoid precipitation changes. The objective of this study is to find these optimal heating time conditions to maintain the T6 state during warm forming multi-step process. Different heating times were reproduced using a Gleeble 3500 machine. Electrical conductivity and hardness were measured after these different conditions to make the link with the evolutions of precipitates of AA7075-T6. Tensile tests were also performed to characterize the mechanical behavior at the end of these heating cycles. A holding time of less than 10 seconds is determined to maintain the T6 state at 200◦C. Two multi-step warm forming devices (a cylindrical cup in two steps and a U-channel part) were finally tested to validate these optimal time forming conditions.
{"title":"Warm forming of AA7075-T6: optimizing the heating time to maintain T6 condition","authors":"Hervé Laurent","doi":"10.21741/9781644903131-128","DOIUrl":"https://doi.org/10.21741/9781644903131-128","url":null,"abstract":"Abstract. Warm forming at 200°C is an interesting way of improving the formability of AA7075-T6. The T6 condition offers the highest ultimate and yield strengths of this aluminium alloy. It is therefore important to maintain these excellent mechanical properties at the end of the forming process. With the hot forming process at temperature above 450°C, it is necessary to add a heat recovery treatment obtained during paint baking to keep this T6 state, which is costly in terms of time and energy. In warm forming process, it is possible to maintain the T6 condition by controlling the heating time to avoid precipitation changes. The objective of this study is to find these optimal heating time conditions to maintain the T6 state during warm forming multi-step process. Different heating times were reproduced using a Gleeble 3500 machine. Electrical conductivity and hardness were measured after these different conditions to make the link with the evolutions of precipitates of AA7075-T6. Tensile tests were also performed to characterize the mechanical behavior at the end of these heating cycles. A holding time of less than 10 seconds is determined to maintain the T6 state at 200◦C. Two multi-step warm forming devices (a cylindrical cup in two steps and a U-channel part) were finally tested to validate these optimal time forming conditions.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"43 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-3
J. Szyndler
Abstract. To improve understanding of the material behavior of additive-produced components, this paper focuses on the development of a numerical model that reproduces a Wire Arc Additive Manufacturing (WAAM) process, with particular attention given to the evolution of the microstructure. In this study, a finite element model in Simufact Welding software is developed, that replicates a real wire arc welding process of building a multilayer straight wall. Microscopy analysis of the weld wall cut in the middle of its length gave information about the expected microstructure morphology at different levels of the build wall. The whole experimental setup is reproduced in the software Simufact Welding. Simulation results in the form of temperature-time and temperature gradient-time history are then used as superimposed thermal conditions to simulate the microstructure evolution at different areas of the welded part by using MICRESS software.
{"title":"Prediction of the microstructure morphology after the WAAM process based on the FEM simulation results","authors":"J. Szyndler","doi":"10.21741/9781644903131-3","DOIUrl":"https://doi.org/10.21741/9781644903131-3","url":null,"abstract":"Abstract. To improve understanding of the material behavior of additive-produced components, this paper focuses on the development of a numerical model that reproduces a Wire Arc Additive Manufacturing (WAAM) process, with particular attention given to the evolution of the microstructure. In this study, a finite element model in Simufact Welding software is developed, that replicates a real wire arc welding process of building a multilayer straight wall. Microscopy analysis of the weld wall cut in the middle of its length gave information about the expected microstructure morphology at different levels of the build wall. The whole experimental setup is reproduced in the software Simufact Welding. Simulation results in the form of temperature-time and temperature gradient-time history are then used as superimposed thermal conditions to simulate the microstructure evolution at different areas of the welded part by using MICRESS software.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"76 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-292
Rita Moussallem
Abstract. Controlling the quality of industrial products requires an accurate comprehension of the material’s behavior during the several transformation phases. An accurate estimation of the heat transfers taking place throughout the production phases necessitates the exact knowledge of the thermophysical properties. These properties are well known in the solid state, however they are less mastered in the liquid state and during transformation. The main objective of this research project is to estimate the evolution of the thermal conductivity during transformation by solving an inverse heat conduction problem. The calculation outputs ought to describe the evolution of the thermal conductivity function of two coupled fields: the temperature and the transformation degree. The inverse method relies on a finite difference numerical model and a hybrid optimization algorithm, combining a stochastic method with a deterministic method. The temperature evolution within a thermoplastic undergoing transformation is measured with the help of an instrumented mold. The thermal conductivity values are identified by minimizing the discrepancy between the experimentally measured temperature profile and the one numerically simulated. The acquired results are compared with the mixing law, classically used to take into account the phase change of a material. It is observed that the values acquired by the established inverse method reproduce the measured temperature profiles more accurately than the mixing law.
{"title":"Identification of the thermal conductivity of polymer materials during their crystallization","authors":"Rita Moussallem","doi":"10.21741/9781644903131-292","DOIUrl":"https://doi.org/10.21741/9781644903131-292","url":null,"abstract":"Abstract. Controlling the quality of industrial products requires an accurate comprehension of the material’s behavior during the several transformation phases. An accurate estimation of the heat transfers taking place throughout the production phases necessitates the exact knowledge of the thermophysical properties. These properties are well known in the solid state, however they are less mastered in the liquid state and during transformation. The main objective of this research project is to estimate the evolution of the thermal conductivity during transformation by solving an inverse heat conduction problem. The calculation outputs ought to describe the evolution of the thermal conductivity function of two coupled fields: the temperature and the transformation degree. The inverse method relies on a finite difference numerical model and a hybrid optimization algorithm, combining a stochastic method with a deterministic method. The temperature evolution within a thermoplastic undergoing transformation is measured with the help of an instrumented mold. The thermal conductivity values are identified by minimizing the discrepancy between the experimentally measured temperature profile and the one numerically simulated. The acquired results are compared with the mixing law, classically used to take into account the phase change of a material. It is observed that the values acquired by the established inverse method reproduce the measured temperature profiles more accurately than the mixing law.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"45 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-201
William Han
Abstract. To manufacture plastic bottles with an increased ratio of rPET (recycled Polyethylene terephthalate), the ISBM (Injection Stretch Blow Moulding) process must be controlled to account for the variable mechanical and thermal properties. Calibration and optimization of the process have been successfully realized in past works but cannot be used for real-time applications. To address this, a gaussian process regression model of the free blowing step is created. It can calibrate itself using the pressure curve from a previous blowing to obtain near instantaneous predictions of key properties of the bottle. To create the model, the process’ characteristics are studied. Finite element simulations of the blowing where the properties follow a multivariate gaussian distribution are used to train the artificial intelligence. Then, an example is shown using the artificial intelligence predictions to optimize the thickness distribution of a bottle after blowing.
{"title":"Intelligent control of ISBM process for recycled PET bottles","authors":"William Han","doi":"10.21741/9781644903131-201","DOIUrl":"https://doi.org/10.21741/9781644903131-201","url":null,"abstract":"Abstract. To manufacture plastic bottles with an increased ratio of rPET (recycled Polyethylene terephthalate), the ISBM (Injection Stretch Blow Moulding) process must be controlled to account for the variable mechanical and thermal properties. Calibration and optimization of the process have been successfully realized in past works but cannot be used for real-time applications. To address this, a gaussian process regression model of the free blowing step is created. It can calibrate itself using the pressure curve from a previous blowing to obtain near instantaneous predictions of key properties of the bottle. To create the model, the process’ characteristics are studied. Finite element simulations of the blowing where the properties follow a multivariate gaussian distribution are used to train the artificial intelligence. Then, an example is shown using the artificial intelligence predictions to optimize the thickness distribution of a bottle after blowing.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"133 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-17
A. Jedynak
Abstract. This research presents a comprehensive study on the production of aluminum-matrix composite (AMC) powders using ultrasonic atomization for additive manufacturing (AM). The impact of different heat sources—plasma, arc, and induction melting—was evaluated on the processability and resultant properties of the AMC powders, including morphology, size, and composite structure. Additionally, induction melting was considered in terms of process parameters such as pressure difference, nozzle size, and frequency. The analysis of AMC powder processability revealed that the efficiency of the ultrasonic process depended on the selected heat source. The highest efficiency, nearly 50%, was attained with the induction system. All produced AMC powders exhibited high sphericity, with average sizes ranging from 88.2 to 120 µm. However, the desired composite structure was not achieved under tested conditions due to the decrease in SiC particle content from 20% in the feed material to approximately 3.5% in the final AMC powder. Based on these results, the research highlights the potential and limitations of ultrasonic atomization in AMC powder production, emphasizing the need for further optimization to improve powder quality and process efficiency for broader industrial application in AM.
{"title":"Processability of aluminum-matrix composite (AMC) by ultrasonic powder atomization","authors":"A. Jedynak","doi":"10.21741/9781644903131-17","DOIUrl":"https://doi.org/10.21741/9781644903131-17","url":null,"abstract":"Abstract. This research presents a comprehensive study on the production of aluminum-matrix composite (AMC) powders using ultrasonic atomization for additive manufacturing (AM). The impact of different heat sources—plasma, arc, and induction melting—was evaluated on the processability and resultant properties of the AMC powders, including morphology, size, and composite structure. Additionally, induction melting was considered in terms of process parameters such as pressure difference, nozzle size, and frequency. The analysis of AMC powder processability revealed that the efficiency of the ultrasonic process depended on the selected heat source. The highest efficiency, nearly 50%, was attained with the induction system. All produced AMC powders exhibited high sphericity, with average sizes ranging from 88.2 to 120 µm. However, the desired composite structure was not achieved under tested conditions due to the decrease in SiC particle content from 20% in the feed material to approximately 3.5% in the final AMC powder. Based on these results, the research highlights the potential and limitations of ultrasonic atomization in AMC powder production, emphasizing the need for further optimization to improve powder quality and process efficiency for broader industrial application in AM.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"83 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-36
A. Benarbia
Abstract. The FFF process is one of the most widely used additive manufacturing processes for shaping thermoplastic polymers. The recent development of industrial printers equipped with high-temperature ovens has made it possible to print high-performance thermoplastics from the PAEK family for applications in the aerospace, medical and other industries. Numerous studies have shown that thermal history is a key factor to improve the mechanical properties of printed parts. Nevertheless, the uniformity of mechanical properties of printed parts is generally poor and highly dependent on the homogeneity of the thermal oven used, which, to our knowledge, has never been properly characterized. For semi-crystalline polymers, the thermal driven crystallization process is also a key factor in adhesion. However, the coupling between phase transformation and heat transfer is often neglected in numerical modelling and its influence has not yet been clearly demonstrated. In this work, we will carry out a preliminary characterization of the printer by measuring air velocity and temperature gradients over the whole printing zone. Secondly, the comparison between simulation and experimental measurements will show the importance of correctly predicting crystallization kinetics to obtain more accurate predictions.
{"title":"Experimental and numerical study of heat transfer on an industrial FFF printer: Application to PEEK","authors":"A. Benarbia","doi":"10.21741/9781644903131-36","DOIUrl":"https://doi.org/10.21741/9781644903131-36","url":null,"abstract":"Abstract. The FFF process is one of the most widely used additive manufacturing processes for shaping thermoplastic polymers. The recent development of industrial printers equipped with high-temperature ovens has made it possible to print high-performance thermoplastics from the PAEK family for applications in the aerospace, medical and other industries. Numerous studies have shown that thermal history is a key factor to improve the mechanical properties of printed parts. Nevertheless, the uniformity of mechanical properties of printed parts is generally poor and highly dependent on the homogeneity of the thermal oven used, which, to our knowledge, has never been properly characterized. For semi-crystalline polymers, the thermal driven crystallization process is also a key factor in adhesion. However, the coupling between phase transformation and heat transfer is often neglected in numerical modelling and its influence has not yet been clearly demonstrated. In this work, we will carry out a preliminary characterization of the printer by measuring air velocity and temperature gradients over the whole printing zone. Secondly, the comparison between simulation and experimental measurements will show the importance of correctly predicting crystallization kinetics to obtain more accurate predictions.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.21741/9781644903131-40
Abdul Herrim Seidou
Abstract. In this study, Al, Cr, Fe, Mn, and Ni are selected and pure elemental powders were used to prepare several medium entropy alloys (MEAs) and high entropy alloys (HEAs). Differential Thermal Analysis (DTA) is used as a tool for pre-screening of the compositions suitable to design corrosion-resistant alloys for Laser Powder Bed Fusion (LPBF). The advantage of DTA lies in the precise temperature control and in the small quantity of powder necessary to perform the test in near-equilibrium conditions. The powder mixtures were heated up to 1550°C, fully melted, and then cooled down to room temperature at 5°C/min. The results of DTA are used as reference to understand the complex microstructures obtained using LPBF. Microstructure analysis of DTA samples by combining Optical Microscopy (OM) and Scanning Electron Microscopy (SEM) helped to confirm the phase prediction theories. Most of the samples showed a heterogeneous structure with Ni-Al rich B2 phase, Fe-Cr rich BCC and FCC phases. The spinodal decomposition of the BCC phase was also observed in the equimolar AlCrFeMnNi sample. The Valence Electron Concentration (VEC) theory was verified and the partitioning of the elements between the phases was investigated.
{"title":"Differential thermal analysis to assist the design of corrosion-resistant high entropy alloys for laser powder bed fusion","authors":"Abdul Herrim Seidou","doi":"10.21741/9781644903131-40","DOIUrl":"https://doi.org/10.21741/9781644903131-40","url":null,"abstract":"Abstract. In this study, Al, Cr, Fe, Mn, and Ni are selected and pure elemental powders were used to prepare several medium entropy alloys (MEAs) and high entropy alloys (HEAs). Differential Thermal Analysis (DTA) is used as a tool for pre-screening of the compositions suitable to design corrosion-resistant alloys for Laser Powder Bed Fusion (LPBF). The advantage of DTA lies in the precise temperature control and in the small quantity of powder necessary to perform the test in near-equilibrium conditions. The powder mixtures were heated up to 1550°C, fully melted, and then cooled down to room temperature at 5°C/min. The results of DTA are used as reference to understand the complex microstructures obtained using LPBF. Microstructure analysis of DTA samples by combining Optical Microscopy (OM) and Scanning Electron Microscopy (SEM) helped to confirm the phase prediction theories. Most of the samples showed a heterogeneous structure with Ni-Al rich B2 phase, Fe-Cr rich BCC and FCC phases. The spinodal decomposition of the BCC phase was also observed in the equimolar AlCrFeMnNi sample. The Valence Electron Concentration (VEC) theory was verified and the partitioning of the elements between the phases was investigated.","PeriodicalId":515987,"journal":{"name":"Materials Research Proceedings","volume":"115 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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