This study proposes a structured decision-support framework for machining operations involving multiple and conflicting performance objectives. The approach integrates multi-objective optimization using the Varimax Rotated Factor-Normal Boundary Intersection method with a post-Pareto decision stage designed to identify the most appropriate solution among Pareto-optimal alternatives. Principal Component Analysis and Factor Analysis, combined with Varimax rotation, are employed to reorganize eight correlated response variables into three independent and interpretable performance dimensions representing Reliability, Economic Behavior, and Quality. This transformation enhances the stability of the search space and improves the robustness of the optimization process. In the post-optimization stage, the Pareto-optimal solutions are evaluated using the Shannon’s Entropy/Global Percentage Error, allowing a systematic and multi-criteria assessment of the solution set. A sensitivity analysis based on the variation of the weighting parameter indicates that the decision-making process is stable and robust with respect to preference changes. In addition, a comparative analysis was conducted with well-established multi-objective optimization methods, including the Non-dominated Sorting Genetic Algorithm II, the Multi-Objective Evolutionary Algorithm based on Decomposition, the Weighted Sum Method, and the Multi-Objective Lichtenberg Algorithm, using the Hypervolume, Inverted Generational Distance and Spacing metrics. The results show that, while traditional methods exhibit strong capability in exploring the Pareto front, the proposed framework provides more uniformly distributed solutions, along with a structured mechanism for selecting the final solution, in addition to lower computational time and cost. A case study on the hard turning of AISI H13 steel using PCBN 7025W inserts confirms the applicability of the proposed framework, optimizing cutting speed, feed rate, and depth of cut with respect to tool life, economic performance, and surface integrity. The proposed method identifies balanced parameter configurations while reducing reliance on subjective decision-making, thereby supporting decision-making in multi-objective contexts within manufacturing environments.
The microstructure and mechanical properties of an Mg-4.8Y-2.8Gd-0.7Zr (wt.
Abstract This study presents a comprehensive risk management analytical suite for interdependent assets, applying production engineering methodologies to cryptocurrency portfolio optimization. The proposal integrates principal component analysis, K-means clustering, hidden Markov model regime detection, structural shock decomposition, network causality analysis, GARCH volatility modeling, and stationarity testing to provide a multifaceted approach to risk assessment and decision support. Analysis of fourteen cryptocurrency assets over a multi-year period reveals extreme risk concentration with 67.89% of portfolio variance explained by the first systematic factor, six distinct operational clusters, and five market regimes with volatility ratios reaching 6.91-fold. The study identifies supply chain disruption events as the primary source of mean cumulative abnormal return of negative 10.27%, while network analysis reveals hierarchical information transmission structures with Ethereum and Bitcoin as dominant hubs. GARCH modeling demonstrates mean volatility persistence of 0.938 with half-lives averaging 20.8 days. These findings validate return-based methodologies and provide actionable insights for resource allocation, position sizing, and dynamic hedging strategies in high-volatility environments. The proposal establishes a guide for financial risk management under extreme uncertainty.
This manuscript presents a novel, comprehensive study focused on predicting the complex interactions between rail wheels and tracks under wear conditions. The study introduces a unique combination of advanced regression techniques — Gaussian Process Regression (GPR), Support Vector Machines (SVM), Decision Tree (DT), Linear Regression (LR), Non-linear Regression (NLR), and Artificial Neural Networks (ANN) — integrated with a Finite Element (FE) model. The FE model, meticulously developed to simulate the intricate dynamics of wheel-rail interactions, allows for a detailed analysis of wear-related parameters, such as contact pressure, stress distribution, and contact area. A key innovation of this work is the integration of multiple machine learning techniques with FE analysis to predict wear evolution in rail-wheel systems. By validating the FE model against established references and generating an extensive dataset, this study provides a rigorous comparison of each regression method’s predictive accuracy. The findings offer crucial insights into the strengths and limitations of each technique, enhancing the applicability of predictive modeling for real-world railway maintenance. The combination of FE analysis with machine learning techniques constitutes a significant advancement in railway engineering, offering an efficient and reliable predictive framework for managing wear in rail-wheel interactions, with direct implications for optimizing maintenance strategies.
Understanding modal parameters is essential for analyzing the vibrational behavior of structures. This study examines the impact of temperature variations and delamination characteristics (damage position and length) on the modal properties of composite sandwich beams. Experimental modal analysis was conducted in a temperature-controlled environment ranging from − 40 °C to 80 °C. Results indicate significant effects of both extreme temperatures and delamination on modal responses. The natural frequency of the first mode decreased from 70.31 Hz at −40 °C to 65.92 Hz at 80 °C, and the second mode’s natural frequency reduced from 460 Hz at −40 °C to 429.2 Hz at 80 °C. Delamination also influenced modal parameters, with damping factor variations from 2.189
