This study examines the impacts of copper and boron in parts per million (ppm) on the microstructure and mechanical properties of spheroidal graphite cast iron (SCI). Boron’s inclusion increases the ferrite content whereas copper augments the stability of pearlite. The interaction between the two significantly influences the ferrite content. Differential scanning calorimetry (DSC) analysis indicates that boron alters the enthalpy change of the α + Fe3C → γ conversion and the α → γ conversion. Scanning electron microscope (SEM) analysis confirms the locations of copper and boron. Mechanical property assessments using a universal testing machine show that the inclusion of boron and copper decreases the tensile strength and yield strength of SCI, but simultaneously enhances elongation. Additionally, in SCI production, the utilization of copper-bearing scrap and trace amounts of boron-containing scrap metal, especially in the casting of ferritic nodular cast iron, offers potential for resource recycling. This highlights the importance of resource conservation and recycling in advancing sustainable manufacturing practices. These findings provide critical insights into the effects of boron and copper on SCI’s behavior, contributing to the design and development of high-performance SCI materials.

One of the factors that influence the dynamic characteristics of machining systems is the cutting tool. Cutting tools are very diverse, and receptance coupling substructure analysis (RCSA) is essential for analyzing the dynamic characteristics of each tool. For RCSA, a full receptance matrix of the equipment and tools is essential. In this study, rotational degree-of-freedom receptance was estimated and analyzed using translational receptance. Displacement/moment receptance was analyzed according to the distance of the response point using the first-and second-order finite difference methods. The rotation/moment receptance was estimated according to the distance of the response point. Rotation/moment receptance was analyzed using Schmitz’s method and compensation strategies. The limitations of these strategies were analyzed, and the rotation/moment receptance for the beam under free-free boundary conditions was predicted using the second compensation strategy.

For an engineering feasibility study, we studied a simple design to improve NCSS (nanocarbon composite strain sensor) sensitivity by using its geometric pattern at a macro scale. We fabricated bulk- and grid-type sensors with different filler content weights (wt.%) and different sensor lengths and investigated their sensitivity characteristics. We also proposed a unit gauge factor model of NCSS to find a correlation between sensor length and its sensitivity. NCSS sensitivity was improved proportional to its length incremental ratio and we were able to achieve better linear and consistent data from the grid type than the bulk type one. We conclude that the longer sensor length results in a larger change of resistance due to its piezoresistive unit summation and that sensor geometric pattern design is one of the important issues for axial load and deformation measurement.

In modern industries, enhancing the efficiency and performance of electric motors is a critical requirement. Pulse-width modulation (PWM) inverters utilized to enhance the energy efficiency of electric motors generate complex shaft voltages and bearing currents, leading to bearing electrical erosion. This study proposes a new method for detecting high-frequency (HF) circulating current and electric discharge machining (EDM) current signals in bearings for electric motors. The proposed method utilizes common mode voltage (CMV) and bearing current data, analyzing the relationship between these signals. Subsequently, it applies filtering techniques and differentiation for signal preprocessing. The interquartile range (IQR) method is used to detect outliers, classify HF circulating current and EDM current signals, and perform time-series data clustering to determine the occurrence frequency and timing of EDM signals. Finally, the implementation outcomes of the proposed method are validated, and its classification efficacy is evaluated and benchmarked against established methodologies through a comprehensive performance analysis. The proposed technique is anticipated to be applicable to the maintenance and prediction of modern electric motors in future developments, contributing to enhanced durability and reliability.

The spring-in phenomenon of the composite parts can affect the assembly process. This study aims to predict the spring-in phenomenon of a carbon fiber reinforced plastic (CFRP) part. Here, we predict the spring-in of the CFRP part using a coupled analysis of the forming and cooling processes during the stamping process. First, a simulation of the entire forming process, such as the transfer of the composite laminate, gravity analysis, and forming was performed to obtain the temperature distribution of the CFRP part. Subsequently, a finite-element (FE) simulation of the cooling process was conducted to predict the spring-in phenomenon of the shaped CFRP part using the temperature data obtained in the forming simulation. Finally, a CFRP part was manufactured and compared with the results of the FE simulation.