This study investigates the impact of ozone exposure on the hardness of synthetic rubber specimens (a blend of NR (natural rubber) and CR (chloroprene rubber)) through accelerated aging tests. Using a linear regression model, the research predicts the lifespan of rubber under real-world conditions and demonstrates how established experimental methods can yield novel insights when applied to synthetic rubber. The results show that ozone exposure significantly increases hardness within the first 10 days, stabilizing after day 12. Through analysis, this study calculates acceleration factors based on ozone concentration and temperature, estimating the practical lifespan of synthetic rubber under actual conditions to be approximately 25.76 years. These findings provide valuable indicators for evaluating the durability of synthetic rubber materials and predicting the longevity of rubber products in industrial applications. Furthermore, the research emphasizes the potential for improving lifespan prediction accuracy by incorporating non-linear models or machine learning approaches.

The impact of the Fourth Industrial Revolution has brought significant attention to Condition-based maintenance (CBM) for autonomous ships. This study aims to apply CBM to the fuel supply pump of a ship. Five major failures were identified through reliability analysis, and structural analysis was conducted to investigate the mechanisms by which one failure induces another, leading to the identification of three compound failure scenarios. Data were collected on a test bed under normal conditions, five single failure conditions, and three compound failure conditions. The acceleration data from the experiments were transformed into 2D arrays corresponding to a single pump rotation, and a method was proposed to compensate for the errors accumulated during the repeated array generation. The data were vectorized using a simplified CNN structure and applied to six multi-label learning methods, which were compared to identify the optimal approach. Among the six methods, the Label Powerset (LP) was found to be the most effective. Multi-label learning captures correlations between labels, similar to the failure-inducing mechanisms learned from structural analysis.

It is difficult to exchange parametric assembly models using conventional neutral formats such as the standard for the exchange of product model data or the initial graphics exchange specification. These formats only support the boundary representation information that leads to the inability to perform parametric re-evaluation, once a model is exchanged. In order to exchange parametric information along with the design intent, a design history-based macro-parametric approach was proposed. Our method is macro-parametrics approach, however, supported only the exchange of individual part models. As most of the products are manufactured in assemblies, where several components are connected with multiple constraints, it is necessary to exchange the assembly model data. To overcome the issue of post-exchange editability, a collection of neutral assembly commands was introduced to extend the capabilities of the macro-parametric approach. A set of neutral assembly constraints was defined and a system for exchanging the parametric assembly models was implemented. An assembly model consisting of coaxial and incidence constraints was successfully exchanged between two commercial computer-aided design systems: CATIA and NX. It was possible to re-evaluate the assembly model parametrically after the exchange. The method can be further extended to exchange the remaining constraint types in different commercial computer-aided design systems.

In this study, a kW-level high-power Nd:YAG nanosecond laser was adopted to eliminate a corrosion layer on a 304L stainless steel (SS304L) surface. Four different laser cleaning (LC) processes with various hatch distances and loop counts were adopted. The energy-dispersive X-ray spectroscopy (EDX) analysis revealed that the corrosion layer was successfully eliminated via the LC process. However, the electron probe X-ray microanalysis (EPMA) analysis confirmed that a Cr-based oxide layer with a thickness of a few micrometers had developed on the surface of SS304L by the LC process. Moreover, Cr-depleted regions were generated in the subsurface owing to the Cr consumption for oxide layer development. The surface temperature during the LC process strongly affected the thickness of oxide layers. The oxide layer and Cr-depletion formation can affect the subsequent manufacturing processes, including welding and molding. Moreover, those can influence the materials’ properties themselves, where the laser-cleaned workpieces may be used. Therefore, it is critical to characterize the relation between LC process parameters and microstructural alteration.

The fourth Industrial Revolution is driving the maritime and port logistics industry toward greater operational efficiency and reduced human intervention through automatic mooring systems. This study developed and applied a lab-based evaluation procedure to assess the performance and stability of vacuum suction pads in such systems. Test conditions were based on Cavotec’s specifications, with evaluation criteria tailored to the pads’ required performance. Relevant standards were reviewed to establish a vacuum pressure leakage criterion of 5% or less. Results showed that the pads maintained pressure reductions below this threshold, confirming their suitability for automatic mooring, though dynamic factors like heave and tides remain untested.