Prediction of chemical composition and mechanical properties in powder metallurgical steels using multi-electromagnetic nondestructive methods and a data fusion system

Akhlaghi, Iman Ahadi; Haghighi, Mehdi Salkhordeh; Kahrobaee, Saeed; Hojati, Milad

doi:10.1016/j.jmmm.2019.166246

Cited by 8 publications

(4 citation statements)

References 48 publications

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“…𝐴𝐴 es el área, en 𝑚𝑚 2 . |∇𝑇𝑇| es el gradiente de temperatura en la dirección del flujo de calor, en 𝐾𝐾 Es ampliamente avalado en la bibliografía consultada y disponible que las propiedades de las aleaciones de aceros dependerán de la temperatura de servicio y de la microestructura del material, esta última condicionada por la composición química de la aleación [6][7][8]. Cada elemento de aleación en particular influye en la microestructura y propiedades del acero, modificando las características del diagrama Fe-C [9].…”

Section: Introductionunclassified

Modelación empírica de la conductividad térmica para un grupo de aceros

Roque-Villalonga

Camaraza-Medina

2022

DYNA

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La relación entre la composición química y la temperatura de trabajo del acero no son lineales con la conductividad térmica por lo que se proponen modelos empíricos para la predicción de esta. Se realizaron mediciones a32 marcaciones de acero AISI laminados y recocidos. Se utilizó el algoritmo de machine learning K- Nearest Neighbor, además se entrenó una red neuronal empleando el software RStudio, específicamente la librería caret, para obtener un modelo empírico que permitió predecir con un adecuado nivel de incertidumbre la conductividad térmica en el rango de temperaturas de 0−800℃. El modelo se probó con un grupo de valores reservados para este fin, obteniendo bajos niveles de incertidumbre. Los mejores resultados se obtienen al entrenar una red neuronal con 25 neuronas en la capa oculta y un valor de regularización de 0,001, obteniendo un error de 5,4%y un RMSE de 0,0228.

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Section: Introductionunclassified

Modelación empírica de la conductividad térmica para un grupo de aceros

Roque-Villalonga

Camaraza-Medina

2022

DYNA

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“…Recent investigations in this field have been focused on microstructural changes due to the various heat-treating processes. For instance, pearlite/ferrite fraction [2], prior austenite grain size [3], microstructural changes during recovery process [4], the thickness of induction [5][6][7], carburized [8], and *Corresponding Author Institutional Email: i_a_akhlaghi@sadjad.ac.ir (I. Ahadi Akhlaghi) decarburized [9][10][11] layers in mild carbon steels, retained austenite fraction [12], precipitation of alloy carbides [13] and secondary hardening occurrence [14] in tool steels as well as variations in mechanical properties of API X65 [15], powder metallurgical [16] and Hadfield [17] steels have been characterized using EC and MHL methods.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network and Multi-magnetic NDE Methods to Determine Mechanical Properties of Plain Carbon Steels Subjected to Tempering Treatment

Akhlaghi

Kahrobaee

Sekhavat

et al. 2021

IJE

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“…Froend et al 39 installed an infrared monitoring system on the laser cladding equipment to collect the infrared radiation value of furnace hearth during the processing and dynamically adjust the laser power to avoid the occurrence of abnormal state of furnace hearth. Akhlaghi et al 40 proposed an approach for online detection of possible cracks and incomplete fusion of specimens during processing by using high-frequency ultrasonic technology, and the experimental results showed the approach had a certain evaluation effect. Rieder et al 41 detected online the dynamic evolution of the thickness of cladding layer, sound velocity, and signal spectrum during the processing by installing an ultrasonic probe at the bottom of the laser cladding equipment.…”

Section: Introductionmentioning

confidence: 99%

Structural condition monitoring and identification of laser cladding metallic panels based on an acoustic emission signal feature optimization algorithm

Yang

2020

Structural Health Monitoring

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Laser cladding technology has become a central issue in industrial research and application recently. Due to the complexity of the processing and the difficulty of quantitative analysis, it is particularly important to effectively and reliably detect the forming quality of the cladding layer. Therefore, the laser cladding processing condition of metallic panels is monitored and identified based on acoustic emission technology in this article. Consequently, laser cladding acoustic emission signal of 316L stainless steel plate is first collected, and a multi-domain acoustic emission signal feature extraction method based on time–frequency domain and waveform parameters is constructed by integrating time–frequency, empirical, and inter-relationship diagraph analysis. Moreover, a feature optimization approach based on t-distributed stochastic neighbor embedding algorithm is proposed, which combines with correlation analysis to realize the de-redundancy and optimization of acoustic emission signal features. In addition, on the basis of optimizing the characteristics of acoustic emission signal, laser cladding acoustic emission signals under three technological parameters are collected. Second, a laser cladding condition identification method, which is a least square support vector machine algorithm based on niche particle swarm optimization, is proposed. The optimal parameter combination of niche particle swarm optimization algorithm is mainly selected to improve the accuracy of identification and classification. The results demonstrate the proposed t-distributed stochastic neighbor embedding feature optimization method can effectively extract the sensitive information related to the processing condition in the feature space, and the optimization of least square support vector machine parameters through niche particle swarm optimization can significantly improve the identification rate of classification. Specifically, the feasibility of the proposed t-distributed stochastic neighbor embedding–niche particle swarm optimization–least square support vector machine model to analyze laser cladding acoustic emission signal characteristics is verified, and the effectiveness of acoustic emission–based structural condition monitoring and identification of laser cladding plate-like structures is also demonstrated.

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Prediction of chemical composition and mechanical properties in powder metallurgical steels using multi-electromagnetic nondestructive methods and a data fusion system

Cited by 8 publications

References 48 publications

Modelación empírica de la conductividad térmica para un grupo de aceros

Modelación empírica de la conductividad térmica para un grupo de aceros

Application of Artificial Neural Network and Multi-magnetic NDE Methods to Determine Mechanical Properties of Plain Carbon Steels Subjected to Tempering Treatment

Structural condition monitoring and identification of laser cladding metallic panels based on an acoustic emission signal feature optimization algorithm

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