A journal bearing is a machine element that is used to keep the shaft rotating about its axis. The increasing demand for journal bearing applications in high-speed machines that are efficient and economical has resulted in the need for improvements to the acoustic and tribological performance of journal bearings. In order to improve the tribological and acoustic performance, this study aims to propose a novel journal bearing design by introducing a roughness condition in a specific zone of the stationary bearing surface. In addition, the impact of the application of engineered roughness on the performance of Bingham-plastic-lubricated bearings is investigated in more detail. Considering the effect of cavitation, the analysis was conducted using a 3D computational fluid dynamics (CFD) model of a journal bearing. In comparison with the Reynolds equation—which is inertialess—for lubrication analysis, the use of a 3D CFD model based on Navier–Stokes equations reflects more detailed flow characteristics. Moreover, in this work, variations in the area of surface roughness were employed, resulting in various roughness patterns on the surface of the journal bearing, so that the acoustic and tribological performances of the journal bearing were anticipated to be enhanced. The findings of this study show that under non-Newtonian lubrication of the bearing, the engineered roughness has a strong effect in altering the tribological performance. Furthermore, the well-chosen roughened surface was proven to be more pronounced in enhancing the load support and reducing the friction force. The simulation results also show that using an engineered surface has little effect on the noise of the bearing.

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments.

Reverse engineering (RE) is essential in recreating 3D models of existing manufactured parts. It is widely used for repairing damaged components, improving used parts, and designing new items based on older models. One of the most common methods in RE is photogrammetry, which enables 3D reconstruction by capturing multiple images. Therefore, this study aimed to explore the application of mobile photogrammetry to obtain a 3D model of a train brake pad. The process started with capturing images of objects in a quick and professional manner to ensure visualization of data. This was followed by processing 2D images using Agisoft Metashape 2.2.1 software and Artificial Intelligence (AI) algorithms to create a precise 3D model. Subsequently, assessment was performed using feasibility in terms of cost, time, and accuracy. The results show that mobile photogrammetry provided an accessible and cost-effective method, alongside maximum contact stress after reducing optimization by approximately 28.42%, with maximum error value measured by the virtual model with the reference value of 0.30 mm (on Metashape) and 0.46 mm (on AI). This suggested that reverse parameterization significantly accelerated computer-aided design (CAD) model reconstruction and reduced the part redesign development cycle. By using photogrammetry and parametric modeling, engineers could accurately analyze and optimize train brake pads, ensuring safety as well as sustainability in railway operations. Additionally, RE and parametric modeling could assist in creating durable, cost-effective alternatives, and predicting appropriate replacements.

A crankshaft is a part of the main engine components and plays a critical role in car performance. The crankshaft component consists of journals, pins, and flange that experience induction hardening treatment to increase their hardness. Hardness case depth represents important hardness criteria achieved by the depth of induction hardening process. This paper aims to evaluate crankshaft hardness quality by analyzing hardness case depth measurement. The control chart shows the results, which presents process stability to indicate abnormal processes. The capability process is also performed to process consideration. Evaluation results show hardness case depth is not at its best performance even though process capability meets product requirements. Affected factors to process stability are manpower, method, material, measurement, machine and environment. Implemented improvements based on the problem root cause to eliminate influenced factors and achieve stable process are delivered.

This study evaluates the performance of a copper-based metal matrix composite (Cu-MMC) brake pad fabricated by powder metallurgy for high-speed railway braking applications. The material was produced via homogeneous powder mixing, compaction at 650 MPa, and sintering at 950 °C for 2 h to promote densification and metallurgical bonding. The fabricated Cu-MMC exhibited densities of 5.71–5.98 g/cm³, porosities of 5.85–10.1%, and hardness values of 62–73 HV, indicating effective microstructural control. Tribological performance was assessed using a brake dynamometer at an equivalent speed of 160 km/h and a contact pressure of 0.95 MPa. The composite demonstrated a low specific wear rate of 0.11–0.14 cm³/MJ, meeting the TJ/CL 307-2014 standard for high-energy braking. Surface analysis revealed stable frictional behavior dominated by oxidative–abrasive, adhesive, and delamination wear mechanisms. Thermal evaluation showed a maximum operating temperature of 225–235 °C, below the softening temperature of copper, confirming adequate thermal stability.