In this paper, the effect of filler metal and type of welding on the strength and ductility of dissimilar welding of two different grades of stainless steel was investigated. One of the benefits of stainless steel is its corrosion resistance, which is often necessary for equipment longevity in these facilities. During shipbuilding, as required, stainless steel 316L needs to be welded to the shipbuilding-grade carbon steel A131. In these applications, welding between the two should demonstrate superior strength during vessel construction. To provide a clear illustration, experimental work was needed to allow a careful selection of the joining procedure and filler metal or electrode. The current research work includes a comparative experimental analysis of dissimilar-metal welding (SS-316L & A131 steel). The reasons for choosing these two materials are their greater corrosion resistance and high strength in humid environments. Furthermore, two different welding methods (SMAW & TIG) with varying filler metals were employed in the experiment. The ultimate tensile strength and yield strength of the SMAW welds using E308-16 filler metal were the highest among all, while the TIG welds with ER308L showed superior bending strength. Observations suggest that SMAW with the E308-16 electrode exhibits superior tensile strength, while TIG joints with ER 308L filler provide better bending strength for the welding of SS-316L and shipbuilding (SB) grade A131 steels.
Steel is an essential component used to build marine vessels due to its endurance of the sea’s harsh conditions, including corrosion and dynamic stresses, therefore, different grades of mild steel are used in shipbuilding. It provides the strength, ductility, and weldability necessary for structural integrity, consisting of carbon, manganese, etc., as alloying elements. In this research, different quenching media were employed to assess variations in mechanical properties. This process ultimately triggered alterations in the microstructure of the steel. Two types of media, such as vegetable oil (Canola) and Polyvinylpyrrolidone polymer (PVP), were studied in comparison with simple heat-treated steel. Mechanical characterization comprised of tensile testing, hardness and impact testing to evaluate major changes in strength and ductility. Furthermore, a microscope was used to interpret the microstructure. To guarantee consistency in testing, samples were prepared in accordance with ASTM guidelines. The yield strength of as-received steel was increased from 298 MPa to 358 MPa and 370 MPa because of rapid cooling action in PVP and oil, respectively. A significant increase in Ultimate tensile strength was achieved due to the variety of quenching media; however, ductility was seriously compromised because of the excessive hardness of the material. Impact energy analysis revealed a notable reduction, which is linked with degradation in toughness.
The integration of robotics into service environments is transforming how labor-intensive tasks are managed, particularly during peak hours with staff shortages and long wait times. This research presents a fully autonomous, modular food-delivery robot designed to enhance operational efficiency and improve service experience. The system combines artificial intelligence, facial recognition, smartphone-based order management, Arduino, ESP32, ESP32-CAM, and Python to navigate indoor environments and deliver food directly to recipients, supported by a secure handover mechanism. Experimental results indicate that the robot performs waiter-like delivery reliably, maintaining mobility and structural integrity across various surfaces by using lightweight materials and motors that have been optimized. Through the use of a motion coordination algorithm, responsive navigation can be achieved, while a simple user interface can be operated by anyone with minimal training. According to these results, automation reduces the need for manual labor, increases the speed of service, and ensures consistency in the delivery process. Additionally, the system provides a practical framework for future research and potential applications beyond food delivery, such as surveillance, environmental monitoring, and disaster response. Future work will focus on scaling for real-world deployment and integration advanced AI navigation to enhance autonomy, adaptability, and overall operational performance.
This study examines how efficiency improvements associated with Jevons’ Paradox and product-system maturation, as described by Vernon’s Product Life Cycle (PLC), jointly influence the long-term pricing relationship between primary and recycled copper and aluminium. Using author-provided nominal annual USD price series for 2002–2021, the analysis derives descriptive indicators most notably the recycled-to-primary (R/P) price ratio to characterize structural shifts consistent with PLC-driven secondary integration. Recent market conditions in 2024–2025, including tight physical availability, low inventories, regional premia, and recurrent episodes of backwardation, are incorporated as qualitative context without merging with the historical dataset. Results indicate a sustained narrowing of R/P discounts for both metals by 2021. The combined Jevons–PLC interpretation suggests that efficiency-driven service expansion and supply-side tightness increase the relative value of secondary material, supporting long-term convergence between primary and recycled streams.
The proliferation of Industry 4.0 technologies in manufacturing has created an unprecedented opportunity to leverage Big Data for process optimization and efficiency improvements. However, the sheer volume of data can also lead to critical information being overlooked, potentially hindering productivity and competitiveness. This paper presents a straightforward Industry 4.0-based data visualization framework designed to transform raw manufacturing data into actionable insights. Specifically, this work focuses on the analysis of Overall Equipment Effectiveness (OEE) data. The framework utilizes a practical dashboard tool to enable manufacturers to perform in-depth data analysis and identify areas for improvement in real-time. Such a framework enables prompt intervention when corrective actions are needed, ultimately increasing efficiency and reducing production downtime. The framework was successfully implemented at a tire manufacturing company on a single machine within a short period of time. The results highlighted the effectiveness of data visualization in identifying specific operational losses and informing strategic decision-making. This work emphasizes the critical role of technology and proper policies in leveraging data to optimize production processes and drive continuous improvement in Industry 4.0 environments.
The study focuses on identifying the specific mechanisms of the FR4 fiberglass composite milling process using tungsten carbide end mills with wear-resistant diamond-like and diamond coatings. The processing was carried out at cutting speeds from 115 to 300 m/min and feed of 0.075 and 0.15 mm/tooth. At the same time, the vibroacoustic signal was recorded in three formats: changes in the RMS value and the amplitude of the acoustic emission in the low-frequency and high-frequency ranges, as well as the parameter Kf, which is the ratio of the RMS amplitudes of the signals in the low-frequency and high-frequency ranges. It is shown that the coating material has a predominant effect on the surface roughness. The minimum roughness value was RA = 0.2 µm for the case of a diamond-coated tool. In addition, the coating improves processing performance by increasing the cutting speed for tools with DLC by 1.3 times and for tools with diamond coating by 1.7 times, provided that the RA increases slightly but does not exceed 0.36 µm. When processed with an uncoated instrument, the mill captures the fiber, bends it and breaks it into bundles, creating grooves. The mechanism of glass fiber destruction by a DLC mill is similar, with the difference that the length of the fragmented fiber sections is noticeably reduced due to reduced friction. The mechanism of cutting fiberglass with a diamond-coated milling cutter is significantly different. There are characteristic scratches on the worn sections of the fiber, and there are no signs of destruction of the composite between the matrix and the fiber. Studies of vibration signals have shown that frequency ranges up to 20 kHz and from 33 to 48 kHz are informative enough to diagnose the fiberglass milling process. The most significant values of the Kf parameter were observed at large amplitudes of low-frequency vibrations, typical for processing with uncoated and DLC milling cutters. The lowest Kf values were obtained using diamond-coated milling cutters. A correlation was found between the values of the Kf parameter and the roughness values of the treated end surface of the fiberglass plate.
