Mechanical Engineering Advances

Numerical investigation of induction hardening of stationary cylindrical steel pins with convective quenching

Mohammad Yaghoub Abdollahzadeh Jamalabadi — 2026-01-15

This paper presents a comprehensive, fully-coupled Multi-physics finite element model for simulating the induction hardening process of stationary cylindrical steel pins, including subsequent convective cooling. The model integrates three interacting physics domains—electromagnetic induction, transient heat transfer, and metallurgical phase transformations—within an efficient two-dimensional axisymmetric formulation. Temperature-dependent material properties for all steel phases (ferrite, pearlite, austenite, martensite) and the surrounding air are implemented, and the formulation accounts for latent heat effects during phase changes. The framework employs a segregated solver approach, ensuring robust convergence between the strongly coupled electromagnetic, thermal, and phase transformation modules. The stationary configuration simplifies the computational approach while retaining high fidelity for industrial applications. The simulation predicts critical process outcomes such as transient temperature distributions, phase evolution, and the resulting spatially-graded hardness profile. It further evaluates the resultant residual stress distribution, providing insight into potential distortion and component performance. Furthermore, it serves as a predictive tool for optimizing key operational parameters, including induction coil current frequency and magnitude, heating time, and forced convective cooling intensity. Model predictions for case depth versus applied power show strong agreement with experimental measurements, validating the framework. The validated model demonstrates its utility as a virtual design platform, reducing the need for costly experimental trials. This integrated model provides a complete and practical computational framework for designing, analyzing, and optimizing stationary induction hardening processes to achieve targeted hardness depths, improve energy efficiency, and ensure consistent product quality in manufacturing.

PDFP-enhanced physics-informed neural networks for solving higher-order PDEs: Application to engineering beam problems

Shahbaz Ahmad — 2026-01-27

This study introduces a modern technique for solving beam equations on elastic foundations, applied to Euler–Bernoulli and Timoshenko beams based on the Winkler foundation. Conventional Physics-Informed Neural Networks (PINNs) have faced major challenges in handling large space-time domains, even when closed-form solutions exist. To address these challenges, we introduced a preconditioned PINNs loss function that incorporates prior knowledge from similar problems, thereby enhancing both productivity and accuracy. This novel method improves the generalization capability of PINNs in structural engineering, specifically for beam dynamics on elastic foundations. The effectiveness of the method is shown through numerical simulations on Euler–Bernoulli beams and extended domains for Timoshenko beams. Comparing with state-of-the-art PINNs shows that our method speeds up the convergence and precisely describes the behavior of the system, surpassing current strategies under the L2-norm metric. Besides, we examine the weight and weight loss plots, and present 3D visualizations of the best weight configurations. Further, we describe the superior performance of the proposed method. The present study explores the application of Preconditioned Devidon–Fletcher–Powell (PDFP)-enhanced Physics-Informed Neural Networks (PINNs) for solving higher-order partial differential equations, with particular emphasis on their implementation in engineering beam problems. It provides an overview of the new approach for addressing complex beam dynamics, including visualizations of the neural network architectures, PINNs convergence behavior, and representative solutions for the beam problems.

Thermal confinement effects in laser-polished AM 316L slots: The role of geometry in internal surface response

Aswin Karkadakattil — 2026-02-06

Laser polishing (LP) is widely employed to enhance the surface quality of additively manufactured (AM) metals; however, its behaviour within deep or confined internal geometries remains insufficiently understood. Many high-performance AM components, such as biomedical implants, turbine cooling channels, and metal microfluidic systems, incorporate narrow internal features where heat-transfer conditions differ significantly from open surfaces. In this study, laser powder bed fusion (LPBF)-fabricated 316L stainless steel specimens containing ~10 mm deep slots with widths ranging from 1 to 5 mm were subjected to laser polishing using a continuous-wave fibre laser (power: 80–120 W, scan speed: 450–750 mm/s, spot size: ~80–100 µm, ~60–70% track overlap, single-pass strategy). The influence of internal geometric confinement on microstructural evolution and mechanical response was systematically investigated. A pronounced depth-dependent microhardness gradient was observed along the slot wall, with hardness decreasing from approximately 270 HV in the lower region to ~210 HV near the slot opening, with more significant gradients in narrower geometries. Quantitative grain-size analysis revealed finer grains (~8–12 µm) in the lower region and coarser grains (~18–25 µm) toward the upper region, indicating progressive grain coarsening with increasing height. These variations are attributed to geometry-dependent thermal boundary conditions, where enhanced conductive coupling to the bulk substrate in the lower region promotes higher cooling rates, while reduced thermal extraction near the slot opening results in slower solidification. The results provide clear experimental evidence that internal geometric confinement can significantly influence microstructure–property evolution during laser polishing, even under constant processing parameters. This study highlights the importance of incorporating geometric effects into post-processing strategies for AM components and offers practical insights for achieving more predictable and uniform mechanical performance in confined internal features.