Carmelo Javier Luis-Pérez

The characterization of high-temperature plastic deformation behavior in centrifugally cast alloys has historically been based on curve fitting of experimentally obtained creep data. Although this approach provides acceptable results within the range of experimental conditions used for curve fitting, performance predictions under conditions outside conventional tests—typically derived from constant temperature and stress—remain insufficiently accurate for petrochemical engineering applications. In this study, constant-load creep tests were performed across a stress range of 5–33 MPa at 950–1100 °C. The minimum creep rate was calculated from the creep curves and used to fit Norton-law parameters by log–log regression. A global Norton-law fit was first carried out for each temperature, and an additional segmented fit was then performed by separating the low- and high-stress domains. The results reveal a systematic variation in the apparent Norton stress exponent (n) with the stress range considered. This variation suggests that the creep response changes from one apparent stress domain to another, contradicting the assumption of a single-valued Norton exponent inherent in standard curve-fitting procedures. Although the experimental database was obtained from conventional constant-load and constant-temperature creep tests, the results are discussed in terms of their implications for creep modeling under non-stationary operating conditions, where local stress and temperature fields may evolve during service. Parameters fitted over a broad stress range may produce systematic local errors when applied to stress domains with different apparent sensitivities. Furthermore, it is well known that classical steady-state creep models may be insufficient when their fitted parameters are transferred to non-stationary loading conditions, where thermal transients during start-up and shutdown generate differential thermal strains and high local stress levels. Geometric constraints, combined with these peak stresses, may lead to values exceeding the yield strength, a condition under which classical methods fail to adequately describe material behavior and stress relaxation mechanisms. This manuscript directly addresses this limitation through the analysis of three heats of the same centrifugally cast alloy.

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.

Purpose: This paper aims to investigate the effect of fused filament fabrication (FFF) 3D printing parameters on surface roughness, dimensional error and density of glass fiber-filled polypropylene (GF/PP) parts. It is a promising material to be used to obtain surgical models for bones, due to its high thermal and mechanical properties. Design/methodology/approach: The experimental approach focuses on the manufacture of cuboid parts, measurement of surface roughness, dimensions and mass, and the use of adaptive neuro-fuzzy inference system (ANFIS) models to analyze the influence of 3D printing parameters such as printing temperature, print speed (PS), nozzle diameter (ND) and layer height (LH) on the responses. Multi-objective optimization using the desirability function is then applied to determine the optimal 3D printing parameters. In addition, the results of the ANFIS models were compared to other machine learning models such as genetic algorithm, support vector machine for regression and random forest for regression. Findings: The research identifies the optimal 3D printing parameters recommended to simultaneously minimize surface roughness and dimensional error, and to maximize density. Specifically, the optimal parameters were identified as a low temperature of 230°C, a moderate print speed of 20 mm/s, a large nozzle diameter of 0.8 mm and a low layer height of 0.1 mm. Originality/value: This study will help select appropriate 3D printing parameters for fabricating surgical models using GF/PP, a material that has only recently become commercially available for FFF processes, and has therefore been scarcely explored in the literature for such applications.

Surface roughness analysis is crucial for assessing machining quality, with most studies emphasizing amplitude parameters due to their industrial relevance. However, spacing roughness parameters, such as Sm (mean spacing of profile irregularities) and Pc (peak count), provide complementary insights into the horizontal distribution of surface features, which are often overlooked. This study investigates the effect of current intensity (I) and pulse time (t_i) on Sm and Pc during the die-sinking EDM of cobalt-bonded tungsten carbide (94WC-6Co), a material widely used in wear-intensive applications due to its exceptional hardness. A multilevel design of experiments (DOE) was performed, varying I from 2 to 6 A and t_i from 10 to 100 µs. Results demonstrated a strong relationship between machining conditions and both parameters, with higher current intensities increasing feature spacing (Sm) and reducing peak number (Pc). The optimal surface quality, defined by Sm = 30 µm and Pc = 337 cm⁻¹, was achieved at I = 2 A and t_i = 10 µs. Polynomial regression models provided predictive insights for process optimization. Scanning Electron Microscopy (SEM) analysis validated these results, revealing distinct surface morphology variations associated with the machining parameters. Finally, an attempt to define a criterion for predicting wear behaviour under the experimental conditions considered in this study will be made, where this will be based on Pc and two additional surface roughness parameters such as Rsk (skewness) and Rku (kurtosis).

Cobalt-bonded tungsten carbide (WC-Co) is widely used in heavy-duty machining applications due to its exceptional hardness and wear resistance, and it is increasingly being adopted in industries such as aerospace and the automotive sector, among others. Its superior mechanical properties make it difficult to machine with conventional methods such as turning or milling. Electrical Discharge Machining (EDM) has emerged as an efficient alternative, as it allows for the machining of hard materials to be carried out without direct contact between the tool and the workpiece, provided that the material has sufficient electrical conductivity. In this study, a multilevel Design of Experiments (DOE) was conducted to analyze the influence of EDM parameters—specifically, the current intensity and pulse time—on the surface roughness (SR), electrode wear (EW), and material removal rate (MRR) for 94WC–6Co. The results indicate that the current intensity was the most significant factor across all responses, while the pulse time played a secondary role. Surface finishes as low as Ra = 0.47 μm were achieved at I = 2 A and t_i = 10 μs. For each outcome variable, mathematical models were obtained in order to improve the EDM processes and better understand the machining of WC-Co.

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.

Today, the high-precision manufacturing of small cavities in difficult-to-machine materials is still a challenge, even more so if they need to be threaded. The machining time, the wear suffered by the electrodes and the surface finish are determining factors in the efficiency of the threading process. However, there is scant literature on this subject so there is a need to study the process and the parameters involved. Thus, this study presents a novel machining strategy for the manufacture of nuts using die-sinking electrical discharge machining (EDM). Moreover, the novelty of this strategy is that it is carried out in a single stage and with a conventional EDM generator. To do so, a design of experiments (DOE) methodology has been followed. First, the optimal machining conditions are determined by studying the influence of EDM parameters on operation variables and mathematical models are developed using multiple linear regression. These models allow the behavior of the response variables under study to be predicted. Finally, this machining strategy developed from the previous experimental results is validated in the manufacturing process of a final part, specifically a square nut. It can be concluded that the mathematical model is good enough to predict the experimental results. Thus, the new method presented and described in this present study allowed a nut to be obtained with a real arithmetic mean deviation of the roughness profile (Ra) value of 1.27 μm whereas the predicted value from the model was 1.28 μm. To do so, the machining conditions selected were: 4 A (current intensity), 5 µs (pulse time) and 0.4 (duty cycle), which also gave a material removal rate (MRR) value of 0.5370 mm³/min. The machining strategy proposed here may be used for future research works related to the manufacturing of mechanical joints made of conductive ceramic materials.

The utilization of diamond-like carbon (DLC) coatings has emerged as a promising strategy to enhance the performance, durability, and functionality of industrial tools and components. Recognized for their exceptional attributes such as hardness, wear resistance, low friction, and biocompatibility, DLC coatings have achieved widespread acclaim for their potential to improve the capabilities of tool steels for different applications. This present study shows a comprehensive investigation into the application of DLC coatings on a diverse range of tool steel substrates, encompassing 1.2379, 1.2358, Caldie, K340, HWS, and Vanadis 4. The main aim is to show the effects of DLC coatings on these substrates and to provide an in-depth analysis of their properties during forming processes. Furthermore, this study explores the practical utilization of DLC-coated tool steel components, with a particular focus on their role in cold forming dies. Additionally, the study reviews the application of duplex treatments involving plasma nitriding to enhance DLC coating performance. To sum up, this study pursues a threefold objective: to investigate DLC coatings’ performance on diverse tool steel substrates; to assess the potential for improvement through nitriding; and to evaluate the behavior of DLC coatings in the cold stamping of S235 steel, which is of great technological and industrial interest to the cold forging sector.