The rising cost of carbide tools, which can account for up to 20% of manufacturing expenses, necessitates the development of cost-effective machining solutions. This study investigates the performance of dual interchangeable carbide inserts (DICI) applied in wheelset reprofiling. The research focuses on the relationship between cutting parameters (cutting speed, feed rate, and cutting depth) and surface quality indicators, including hardness and surface roughness. A dfigual interchangeable carbide inserts (DICI) configuration mounted on a specially designed tool holder is proposed to improve tool performance and durability. Experimental results demonstrate that using DICI increases surface hardness by up to 12% compared to conventional inserts while maintaining acceptable surface roughness. Microstructural analysis confirms the formation of compressive stresses and reveals a correlation between cutting regimes and thermal effects in the surface layer. The proposed approach improves machining efficiency, enhances tool life, and provides a cost-effective solution for wheelset maintenance in railway engineering.
This study provides a quantitative assessment of the reliability and availability of a serial butter-oil manufacturing line from a mechanical engineering perspective. The line comprises critical rotating and fluid-handling subsystems raw-milk receiving pumps, cream separators, heat exchangers, mechanical agitators, homogenizers, gearboxes, and conveying components whose mechanical integrity profoundly impacts throughput and quality. We characterize the production train using continuous-time Markov chains (CTMCs) based on a reliability block diagram (RBD) that depicts series dependencies and maintenance plans. Component-level failure (λ) and repair (μ) rates, derived from realistic duty cycles and mechanical failure modes (bearing wear, seal leakage, misalignment, lubrication starvation, cavitation, fouling, and thermomechanical fatigue), are conveyed to system-level metrics via state-transition analysis. Numerical simulations yield mean time between failures (MTBF), mean time to repair (MTTR), and steady-state availability for the entire system and for mechanically essential subsystems (pump-valve assemblies and homogenization units). Sensitivity analyses identify availability limitations and priorities mechanical factors bearing L10 lifespan, seal mean time between failures, lubricant replacement interval, alignment tolerance, and cooling-water temperature differential according to their impact on throughput decrease. The results suggest that minor improvements in repair logistics for high-criticality assets (such as the use of cartridge seals and quick-release couplings on feed pumps) might exceed substantial reductions in MTBF for low-criticality components. We demonstrate that the integration of mechanical condition monitoring (vibration and temperature trending) with SPC-based run charts and control limits stabilizes essential mechanical variables (overall vibration, RMS acceleration, and discharge pressure ripple), thereby reducing special-cause variation and unanticipated downtime. The study concludes with maintenance strategies for mechanical assets, encompassing spares pooling for prevalent failure categories, precision alignment, optimized lubrication practices, and threshold-based, condition-directed overhauls, yielding measurable improvements in line availability and energy efficiency while upholding butter-oil quality standards.
Pythagorean fuzzy set (PFS) and their modified frameworks deal with uncertainty more flexibly than the intuitionistic fuzzy set (IFS) frameworks. The existing frameworks address only partial aspects of real-life uncertainty, such as real-valued, interval-valued or complex-valued information. The existing models integrating interval-valued or complex-valued information lack of capable to represent linguistic uncertainty while the fuzzy set (FS) models based on linguistic term lack the capability to present complex-valued or higher order real-life uncertainty. Therefore, the existing techniques fail to simultaneously model interval imprecision, linguistic vagueness, and complex-valued cubic uncertainty within a unified framework. To overcome these limitations, the newly defined framework, called linguistic complex cubic Pythagorean fuzzy set (LCCuPFS) is demonstrated, integrating complex cubic Pythagorean fuzzy sets (CCuPFSs) with linguistic feature, providing a more realistic and robust representation of multifaceted uncertainty in daily real-life applications. Some basic operational laws and their corresponding aggregation operators (AOs) such as averaging AOs, geometric AOs, weighted averaging AOs, and weighted geometric AOs within the framework of LCCuPFSs are established. The key properties of the proposed AOs are investigated. Furthermore, a decision-making (DM) algorithm based on the newly defined approaches is developed. We employ the proposed techniques to establish a DM approach for solving real-world problems. To verify its practical utility, we apply the newly defined approaches in a real-world financial investment DM problem, where assessments are inherently imprecise, linguistically expressed, and influenced by interval-valued and complex-valued uncertainties. Finally, a comparative study between the proposed and existing approaches is investigated based on the ranking-wise performance and characteristic-wise evaluation.
Purpose: In the current competitive and changing business environment, it is important to focus on the factors that can improve Manufacturing Operational performance. Therefore, the purpose of this research was to examine the effect of manufacturing flexibility, E-learning system, and AI-technology Adoption on Manufacturing Operational performance through the mediation of mechanical organization learning capability. Design/ method/ approach: The research adopted a quantitative research methodology. Data was gathered from employees using convenience sampling. Data was collected through a questionnaire developed from past literature review. The usable response rate was 59.19%. The gathered data was analyzed using Smart PLS. Findings: Results of the study revealed that manufacturing flexibility, E-learning system, and AI-technology Adoption have an effect on mechanical organization learning capability. Moreover, mechanical organization learning capability also has a positive significant effect on Manufacturing Operational performance. Findings also supported the mediating role of mechanical organization learning capability. Originality: This study adds to the body of literature by discussing the mediating role of mechanical organization learning capability.
Present research work deals with the design of experiments and optimization of TIG welding parameters to improve the surface quality and mechanical properties of austenitic stainless-steel grade. The key process parameters (welding current, welding speed and shielding gas flow rate) were finalized by conducting systematic experiments on the welding characteristics and properties. Experiments were conducted by Response surface methodology (RSM) using Box-Behnken design to analyze the effect of the above parameters on the surface quality aspects such as weld bead width, surface smoothness, surface defect formation and mechanical properties like ultimate tensile strength (UTS) and hardness. The experimental results indicated that the surface quality is very sensitive to heat input, which is dominated by welding current and velocity. When welding current was 90 A, the fusion went insufficient, which result in the irregular surface profile and the presence of surface discontinuity. When the welding current increased to 115-120 A, the surface finished very well and the weld bead was uniform and smooth, with width of 5.0-5.5 mm. When welding current was increased to 130 A, the welding pool melted excessively, and the width of weld bead was increased to 7.0 mm. When the welding velocity increased from 100 to 200 mm/min, the surface was smooth, and the surface was irregular when the velocity was decreased; the surface was irregular. For the shielding gas flow rate of 15 L/min, good surface appeared, and the oxidation and plasma formation were prevented effectively. The hardness of the base metal decreased from 150 HV to 170 190 HV and the UTS increased to the maximum of 605-615 MPa under the optimal welding condition. The RSM models predicted the surface quality and mechanical properties with high accuracy with the R2 higher than 0.95. Desirability Function Approach (DFA), Teaching learning-based optimization (TLBO) and Grey Relational Analysis (GRA) were used to found optimized welding parameters combination of 115-120 A current, 140-150 mm/min of welding speed and 15 L/min of gases flow rate. These conditions produced performances of around 20% improvements in surface quality and overall weld performance. As a whole, the results give quantitative guidance on how to weld in high quality with great surface finish in industry.
