Mario Javier Cabello

The accurate detection and quantification of martensitic transformation in steel during quenching are essential for controlling the resulting material properties. Numerous studies have investigated this phenomenon using Acoustic Emission (AE) techniques, owing to the significant energy release associated with the transformation. However, no model based on acoustic emission currently exists that can estimate the martensite volume formed during induction hardening. In this work, a novel model is proposed to estimate the transformed martensite volume in induction hardening treatment, focused on the material, geometry, and AE settings used. By integrating acoustic emission data with conventional Vickers hardness measurements, the model parameters can be calibrated. Induction quenching experiments were carried out on cylindrical 42CrMo4 (AISI 4140) steel bars equipped with acoustic emission sensors to capture transformation-related events during heat treatment. The martensite volume after quenching was estimated from hardness values. Model calibration using the experimental acoustic emission data and martensite volume demonstrated strong agreement between predictions and experimental observations. The proposed model offers the potential for in-process monitoring of induction quenching, thereby reducing reliance on conventional characterization techniques.

La aparición de holguras por desgaste en la maquinaria industrial tiene un impacto importante ensu rendimiento y vida útil. Una holgura reduce la eficiencia de la máquina y provoca incrementosde temperatura, ruidos y vibraciones, lo que puede dañar ciertos componentes o causar el falloprematuro del equipo. Para reducir estos efectos, es necesario efectuar tareas de mantenimiento deforma regular. El proceso de caracterización de las holguras y su simulación ha sido objeto de estudio de lacomunidad de dinámica multicuerpo. Los métodos propuestos requieren una descripción precisa dela dinámica de los sólidos unidos por las juntas con holgura. La representación de este fenómenoen la simulación depende de la selección de varios parámetros que caracterizan la geometría de laholgura. En la práctica, estos parámetros son complejos de obtener y pueden variar durante eltiempo debido, por ejemplo, a cambios en las condiciones de operación de la máquina. Además,muchos de estos parámetros no se pueden medir directamente mediante sensores. Los observadores de estados, como los basados en el filtro de Kalman, se emplean para estimarvariables o parámetros que no se conocen con certeza, lo que los convierte en una alternativa paradetectar la presencia de holguras en un mecanismo. Los filtros de Kalman se han empleado conmodelos multicuerpo para estimar fuerzas y parámetros de configuración de modelos, además delpropio estado del sistema. Sin embargo, aún no se han aplicado al ámbito de las holguras de formasistemática, especialmente en el contexto de montajes experimentales. Los primeros pasos haciauna solución siguiendo el enfoque de estimadores implica la selección de los métodos másadecuados para cuantificar el problema de la holgura de forma directa, incluyendo la selección desensores, tipo de filtro y la identificación de las magnitudes más relevantes del problema. En este trabajo, la máquina objeto de estudio se basa en un mecanismo de biela-manivela. Se hacombinado un modelo ideal del mecanismo con un filtro de Kalman extendido consiguiendo estimarlas fuerzas en la holgura. El siguiente paso es conseguir estimar el tamaño de la holgura, para loque se están estudiando diferentes alternativas, como la estimación de parámetros mediante unfiltro de Kalman “unscented”. De esta forma, será posible monitorizar la aparición y evolución deholguras con el fin de evitar daños críticos en la maquinaria.

Induction hardening is a heat treatment that has been increasingly employed in the industry in recent years. It is a complex, highly coupled, and multiphysical process involving electromagnetism, thermal, mechanical, and metallurgical physics. One of the main quality requirements of the process is the hardened case depth generated in the workpiece. The usual method to measure the hardened case and ensure the quality of the parts is to use destructive techniques, which generate material and energy waste and production inefficiencies. Additionally, selecting process parameters such as current, frequency, or scanning speed typically requires several trial-and-error iterations. The goal of this work is to provide a hybrid digital twin (DT) that acts as a nondestructive test technique, predicting the resulting hardened case in real-time and enabling the correction of process parameters during the induction hardening process, ultimately achieving a zero-waste manufacturing scheme. For this purpose, a DT based on an artificial neural network (ANN) model is developed, predicting the hardened case depth in real-time using four monitored input variables: induction frequency, current, and two temperature measurements on the surface of the hardened part. The required data for DT development and training are obtained using a finite element model. Several ANN architectures are evaluated, and the configuration with the best regression results is chosen for implementation in an industrial induction hardening machine. The hardened case predictions obtained from the developed DT demonstrate high accuracy within the analyzed frequency and current range.

Induction hardening is a heat treatment process that enhances the mechanical properties of materials, improving their resistance to fatigue, fracture, and wear. Unlike conventional methods, induction hardening selectively heats the surface of the material, forming a high-hardness layer while leaving the core relatively unaffected. In addition, it generates a compressive residual stress layer in the surface, which is beneficial for the component service behaviour. This compressive layer progressively decreases, turning tensile at the boundary of the hardened layer, and gradually decreases in the untreated core. This study first focused on the numerical simulation model to analyze the physical mechanisms involved in the process and select the ideal calculation method. Subsequently, the effects of the material’s carbon content and the quenching severity on hardness and residual stress formation after the induction hardening of a cylindrical 42CrMo4 steel specimen are examined. For this purpose, a coupled thermo-metallurgical-mechanical finite element model in ANSYS^® is employed. The findings underscore the importance of accounting for all factors contributing to stress generation to accurately predict the material’s behaviour. Additionally, the results highlight the significant impact of carbon content on the hardness of the hardened layer, as well as the substantial role of quenching medium severity in shaping the axial residual stress profile within the material.

Laser welding has become increasingly popular in recent decades due to its high processing speed and minimal heat-affected zone, which contribute to extended component lifetimes. However, the adoption of this advanced technique is often hindered by a lack of skilled personnel associated with traditional welding and limited awareness of its potential. This study proposes a straightforward methodology for developing a finite element-based thermal model for laser welding, incorporating a two-step experimental calibration process. Using temperature measurements from thermocouples and data from a welding spot test, the numerical model was calibrated to optimize combinations of welding speed and power. The study focuses on S275JR structural steel, involving initial spot weld followed by a linear welding trajectory. The results demonstrate the precision of the proposed calibration approach, as numerical predictions closely align with experimental data from all three thermocouples. The findings validate the effectiveness and accuracy of this methodology, highlighting its ability to adapt spot calibration results to more complex linear welding scenarios. Moreover, the simplicity of the process ensures it is accessible and broadly applicable across different materials and welding configurations. This work offers a reliable and resource-efficient framework for laser welding process optimization in industrial applications.

Non-Destructive Testing (NDT) technology known as Acoustic Emission (AE) has been used to investigate the phase transformation of 42CrMo4 steel in a cylinder during an induction hardening process. The objective of this study is to characterize the martensitic transformation of the material using the data obtained by the AE sensor during the process. The heating process of the cylinder is carried out statically, focusing the heat on the area to be treated and then analyzing it. After quenching, destructive tests are carried out to determine the hardness and three-dimensional geometry of the steel microstructure after the process (hardness and X-ray diffraction tests). Once the microstructure of the material is known, it is possible to relate the results with the signals obtained by AE, making possible in future induction hardening processes the estimation of martensite by non-destructive techniques.

Overspeed governors are the crucial safety elements that have allowed the popularization of elevators for the last two centuries, being their main function to securely activate the safety breaks of the cab in an overspeed situation such as a failure in the machine or a rope break. The design of these elements has barely changed in the industry and still relays on a cam follower design that stops rotating when centrifugal forces reach a critical point. Such a design of multiple parts with relative motion induces wear, produces noise, and can even lead to availability reduction. In this paper, the design process and the multibody dynamic (MBD) analysis of a novel overspeed governor concept are presented, with the objective to reduce moving parts and improve the performance and robustness of the solution. A group of partitioners and experienced designers along with industrial partners have analyzed new creative designs, and a new governor typology is presented. Owing to the standard required sensitivity of the governor, a deep MBD analysis is performed to precisely define optimal design parameters. The proposed design has fewer moving parts and therefore is more resilient, reducing the possibilities of failure and the maintenance needs and therefore being a more cost-efficient solution. Optimization techniques have been implemented to fit design parameters and evaluate robustness. Additionally, the effect of manufacturing- or wear-induced clearances is analyzed for the traditional and the novel governor concepts, proving that the new concept is safer in the long term. The adopted design and analysis methodology can also be used for the improvement or definition of new overspeed governors.