Search Results (33)

Additively manufactured (AM) aluminum (Al) alloys are very useful in sectors like automotive, manufacturing, and aerospace because they have unique mechanical properties, such as their light weight, etc. AlSi10Mg made by laser powder bed fusion (LPBF) is one of the most promising materials because it has a high strength-to-weight ratio, good thermal resistance, and good corrosion resistance. But machining AlSi10Mg parts is still hard because they have unique microstructural properties from the way they were produced. This research investigates the machining efficacy of the AM-AlSi10Mg alloy in distinct cutting conditions (dry, flood, chilled air, and minimal quantity lubrication with castor oil). The study assesses how different cooling conditions affect important performance metrics such as cutting temperature, surface roughness, and tool wear. Due to castor oil’s superior lubricating and film-forming properties, MQL (Minimal Quantity Lubrication) reduces heat generation between 80 °C and 98 °C for the distinct speed–feed combinations. The Multi-Objective Optimization by Ratio Analysis (MOORA) approach is used to determine the ideal cooling and machining conditions (MQL, Vc of 90 m/min, and fr of 0.05 mm/rev). The relative closeness values derived from the MOORA approach were used to predict machining results using machine learning (ML) models (MLP, GPR, and RF). The MLP showed the strongest relationship between the measured and predicted values, with R values of 0.9995 in training and 0.9993 in testing. Full article

A significant component of making intelligent decisions is optimizing algorithms. In this context, it is imperative to develop algorithms that are more efficient in order to efficiently and accurately process large quantities of intricate data. In addition, the main contribution of this study lies in the integration of optimization theory with swarm intelligence through multicriteria decision-making methods (MCDMs). This study indicates that combining dimensional analysis (DA) with particle swarm optimization (PSO) can smartly and efficiently improve analysis and decision making, resolving PSO’s shortcomings. A convergence investigation between the bat algorithm (BA), MOORA-PSO, TOPSIS-PSO, DA-PSO, and PSO is carried out to substantiate this assertion. Additionally, the ANOVA method is used to validate data dependability in order to evaluate the algorithms’ correctness. Full article

(1) Background: Educational systems across the world experienced significant changes during 2020–2022, with potential implications for progress toward Sustainable Development Goal 4 (SDG 4: Quality Education), which aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all across European Union member states. Understanding how educational sustainability performance evolved during the pre-pandemic period (2017–2019) and the pandemic-affected period (2020–2022) is essential for developing effective educational policies. (2) Objective: This quantitative comparative study aimed to (i) assess and rank sustainable education developments across EU-27 countries in two periods, Period 1—the pre-pandemic period (2017–2019)—and Period 2—the pandemic-affected period (2020–2022); (ii) identify performance changes between these periods; and (iii) classify countries into performance groups to guide targeted interventions. (3) Methods: Using data from the Eurostat database, we evaluated six key SDG 4 indicators: low-achieving students in reading, mathematics, and science; participation in early childhood education; early school leavers; tertiary educational attainment; adult participation in learning; and adults with basic digital skills. The Multiobjective Optimization based on Ratio Analysis (MOORA) method was used to rank countries and assess sustainable education development. (4) Results: Sweden maintained the highest educational sustainability performance across both periods, while Romania and Bulgaria consistently ranked lowest. Nine countries improved their rankings during the pandemic-affected period, while others maintained stable positions or experienced declines in their rankings. Adult participation in learning showed the greatest variation among the indicators, with top performers, such as Sweden, scoring 0.445 compared to Romania’s 0.051 in Period 2. The proportion of early school leavers decreased from an EU average of 9.0% in Period 1 to 8.3% in Period 2, indicating a positive trend across the study periods. While differences were observed across countries and periods, these should not be interpreted as causally linked to the pandemic alone (5). Conclusions: The performance of educational sustainability varied across EU member states between the two periods, with some countries demonstrating remarkable resilience or improvement, while others declined. These findings underscore the need for targeted educational policies that address specific sustainability weaknesses in individual countries, particularly those in the warning and danger categories. Sweden’s consistent performance offers valuable lessons for educational sustainability, especially during and after major disruptions. Full article

This study explores the machinability of Incoloy 800HT (high temperature) under a sustainable lubrication approach, employing a twin-nozzle minimum quantity lubrication (MQL) system with groundnut oil as a green cutting fluid. The evaluation focuses on key performance indicators, including surface roughness, tool flank wear, power consumption, carbon emissions, and chip morphology. Groundnut oil, a biodegradable and nontoxic lubricant, was chosen to enhance environmental compatibility while maintaining effective cutting performance. The Taguchi L₁₆ orthogonal array (three factors and four levels) was utilized to conduct experimental trials to analyze machining characteristics. The best surface quality (surface roughness, Ra = 0.514 µm) was obtained at the lowest depth of cut (0.2 mm), modest feed (0.1 mm/rev), and moderate cutting speed (160 m/min). The higher ranges of flank wear are found under higher cutting speed conditions (320 and 240 m/min), while lower wear values (<0.09 mm) were observed under lower speed conditions (80 and 160 m/min). An entropy-integrated multi-response optimization using the MOORA (multi-objective optimization based on ratio analysis) method was employed to identify optimal machining parameters, considering the trade-offs among multiple conflicting objectives. The entropy method was used to assign weights to each response. The obtained optimal conditions are as follows: cutting speed = 160 m/min, feed = 0.1 mm/rev, and depth of cut = 0.2 mm. Optimized outcomes suggest that this green machining strategy offers a viable alternative for sustainable manufacturing of difficult-to-machine alloys like Incoloy 800 HT. Full article

AISI 4340 steel has applications in gun barrels, where the surface quality of the barrel is the prime factor. This study explores the application of a machine learning (ML) approach to optimize the precision turning of an AISI 4340 steel alloy using both conventional and wiper tool nose inserts under varying cutting parameters, such as cutting speed, depth of cut, and feed rate. The analytical framework integrates experimental machining data with computational algorithms to predict key output parameters: surface roughness (Ra) and material removal rate (MRR). A Multi-Objective Optimization based on Ratio Analysis (MOORA) method is used for data normalization. Particle swarm optimization (PSO) further refines the process by optimizing the input parameters to achieve superior machining performance. Results show that under optimized conditions, a 118 m/min cutting speed, 0.22 mm depth of cut, and 0.2 mm/rev feed, wiper inserts provide a 50% improvement in Ra compared to conventional inserts, highlighting their potential for enhancing both productivity and efficiency. At the suggested setting, the surface roughness values are 0.59 µm for wiper inserts and 1.30 µm for conventional inserts, with a material removal rate of 4996.96 mm³/min. The developed empirical model serves as a powerful tool for improving precision hard-turning processes across manufacturing sectors. The present work employs the XGBoost model of ML along with MOORA and PSO to predict and optimize machining outcomes, advancing hard-turning practices by delivering quantifiable improvements in surface quality, material removal rate, and operational efficiency. Full article

Electric discharge machining (EDM) stands out for its ability to perform no-contact machining of materials with desired forms by multi-pulse discharges. In this investigation, the machinability of drilling on Ti₅₆Zr₁₈Cu₁₂, metallic glass, for micro-hole is investigated with renewable dielectrics in the EDM process. Machinability is investigated by examining performance indicators including material removal rate (MRR), overcut, edge deviation, and energy efficiency per volume (EEV) in relation to the process parameters, such as electrical and non-electrical parameters. The edges of the drilled holes are examined to investigate the micro-structural changes that occur in metallic glass as a result of micro-machining. The experimental results show that the maximal value of MRR of 0.0103 mm³/min is achieved when the pulse-on time of 30 μs and sunflower oil renewable dielectric is selected, and the minimum overcut and edge deviation of micro-hole drilling in Ti₅₆Zr₁₈Cu₁₂ is 39.99 and 9.41 μm, respectively. Minimum overcut and edge deviation are obtained for colza oil, optimized by 38% and 70%, respectively, over the worst-case conditions. Multi-objective optimization on the basis of ratio analysis (MOORA) results in a 70% reduction in energy consumption of EEV compared to the conventional paraffin media process. In addition, a range of pulse-on time, pulse duty cycle, and renewable dielectric are identified using the MOORA technique while EDM drilling in metallic glass Ti₅₆Zr₁₈Cu₁₂. Full article

Sub-watershed prioritization using morphometric analysis and multi-criteria decision-making (MCDM) techniques is a systematic approach to identifying and ranking sub-watersheds based on their susceptibility to soil erosion. This helps in implementing targeted soil conservation measures. In this study, sub-watersheds in the Narangi basin are prioritized by employing morphometric analysis integrated with advanced MCDM techniques, including additive ratio assessment (ARAS), complicated proportional assessment (COPRAS), multi-objective optimization by ratio analysis (MOORA), and the technique for order preference by similarity to ideal solution (TOPSIS). Weights for various MCDM methods are determined using the criteria importance through an inter-criteria correlation approach (CRITIC: criteria importance through inter-criteria correlation method), while geospatial techniques ensure precise spatial analysis. The results provide a unified ranking of sub-watersheds, revealing that sub-watershed 3 (SW3) and SW9 are in the high-priority soil erosion category; SW1, SW2, SW5, and SW8 are medium-priority; and SW4, SW6, SW7, and SW10 are low-priority. This comprehensive and sustainability-oriented approach equips decision-makers with robust tools to identify and manage sub-watersheds at risk of soil erosion, ensuring the long-term sustainability of land and water resources. This study aligns with sustainable development goal 15 (life on land) and promotes sustainable land use practices to combat soil degradation. Full article

Finding optimal locations for wind farms requires a delicate balance between maximizing energy generation potential and addressing the socio-economic implications for local communities, particularly in regions facing socio-economic challenges. While existing research often focuses on technical and economic aspects of wind farm siting, this study addresses a crucial research gap by integrating sustainable supply-chain-management principles into a comprehensive site-selection framework. We present a novel approach that combines Geographic-Information-System-based spatial analysis, the Fuzzy Analytic Hierarchy Process, and multi-criteria decision-making techniques to identify and prioritize optimal wind farm locations in Tunisia. Our framework considers not only traditional factors, like wind speed, terrain slope, and road and grid infrastructure, but also crucial socio-economic indicators, such as unemployment rates, population density, skilled workforce availability, and land cost. Based on the spatial analysis, it was revealed that 33,138 km² was appropriate for deploying large-scale wind systems, of which 6912 km² (4.39% of the total available area) was categorized as “most suitable”. Considering the SSCM evaluation criteria, despite the minor variations, the ARAS, COPRAS, EDAS, MOORA, VIKOR, and WASPAS techniques showcased that Kasserine, Kebili, and Bizerte stood as ideal locations for hosting large-scale wind systems. These rankings were further validated by the Averaging, Borda, and Copeland methods. By incorporating this framework, the study identifies locations where wind energy development can be a catalyst for economic growth, social upliftment, and improved livelihoods. This holistic approach facilitates informed decision making for policymakers and investors, thus ensuring that wind energy projects contribute to a more sustainable and equitable future for all stakeholders. Full article

In today’s world, air transport has become a favored choice for enhancing the value of a national economy, driven by advancing technology, escalating volumes of national and international trade, and population growth. The proliferation of airport transfer centers, particularly within air transport, plays a pivotal role in fostering the advancement of the aviation sector. Therefore, the selection of these hubs is of great importance. This study evaluated the New Çukurova, Antalya, Sivas Nuri Demirağ, Erzurum and Muğla Airports in Türkiye for the selection of a new airport transfer center in terms of criteria such as airport costs, airport terminal and apron facilities, airport passenger transportation services, airport operating capacity, airport location, demand factors in the service region and other factors. The study employed three methods for evaluating alternative international airports: AHP (Analytic Hierarchy Process), MOORA (Multi-Objective Optimization by Ratio Analysis) and ELECTRE (Elimination and Choice Translating Reality). In the initial phase, the priority ranking of criteria was established based on expert opinions. Subsequently, Antalya Airport was the most suitable airport transfer center according to the ELECTRE method, while New Çukurova Airport emerged as the preferred choice according to the MOORA method. Both airports secured top rankings in both evaluation methods. Full article

The prediction of heating and cooling loads using machine learning algorithms has been considered frequently in the research literature. However, many of the studies considered the default values of the hyperparameters. This manuscript addresses both the selection of the best regressor and the tuning of the hyperparameter values using a novel nature-inspired algorithm, namely, the Multi-Objective Plum Tree Algorithm. The two objectives that were optimized were the averages of the heating and cooling predictions. The three algorithms that were compared were the Extra Trees Regressor, the Gradient Boosting Regressor, and the Random Forest Regressor of the sklearn machine learning Python library. We considered five hyperparameters which were configurable for each of the three regressors. The solutions were ranked using the MOORA method. The Multi-Objective Plum Tree Algorithm returned a root mean square error value for heating equal to 0.035719 and a root mean square error for cooling equal to 0.076197. The results are comparable to the ones obtained using standard multi-objective algorithms such as the Multi-Objective Grey Wolf Optimizer, Multi-Objective Particle Swarm Optimization, and NSGA-II. The results are also performant concerning the previous studies, which considered the same experimental dataset. Full article

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country’s GDP is very high. These circumstances pose serious challenges for long-term energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors. The objective of this paper is to develop models that allow the selection of the most appropriate scenario for renewable energy transition in an oil- and gas-producing country. The distinguishing feature of the proposed model is that alternatives in the decision matrix are presented as scenarios, composed of a set of energy resources and the level of their use. Linguistic descriptions of the alternative scenarios are formalized in the form of fuzzy statements. For the problem solution, four different Multiple-Criteria Decision-Making (MCDM) methods were used: the fuzzy simple additive weighting (F-SAW) method, the distance-based fuzzy TOPSIS method (Technique of Order Preference Similarity to the Ideal Solution), the ratio-analysis-based fuzzy MOORA method (Multi-Objective Optimization Model Based on the Ratio Analysis), and the fuzzy multi-criteria optimization and compromise solution method VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje). This approach is illustrated using the example of the energy sector of Azerbaijan. The recommended solution for the country involves increasing natural gas (NG) moderately, maintaining hydro, and increasing solar notably and wind moderately. Full article

Due to the variety of benefits over traditional cutting techniques, the usage of laser cutting technology has risen substantially in recent years. The attributes of laser technology for leather cutting include adaptability, mass production, capability of cutting complicated patterns, ease of producing tailored components, and reduction in leather waste. In the present study, vegetable chrome-tanned buffalo leather specimens were cut using a 20 W laser diode with conventional and pulse width control in a photodiode-assisted laser cutting process. Emission rate, kerf width, carbonization, and material removal rate were considered as quality indicators. The higher power density associated with the pulse width approach reduces the interaction with the specimen, which results in a better emission rate and material removal rate, along with a lesser kerf width and carbonization. Using the MOORA approach, the optimal parameters of the present study were found to be a stand-off distance of 22 mm, a feed rate of 200 mm/min, a duty cycle of 75%, and a frequency of 20 kHz. The duty cycle can effectively control the pulse width at which the energy has been dissipated across the cutting zone. Full article

We know that in today’s advanced world, artificial intelligence (AI) and machine learning (ML)-grounded methodologies are playing a very optimistic role in performing difficult and time-consuming activities very conveniently and quickly. However, for the training and testing of these procedures, the main factor is the availability of a huge amount of data, called big data. With the emerging techniques of the Internet of Everything (IoE) and the Internet of Things (IoT), it is very feasible to collect a large volume of data with the help of smart and intelligent sensors. Based on these smart sensing devices, very innovative and intelligent hardware components can be made for prediction and recognition purposes. A detailed discussion was carried out on the development and employment of various detectors for providing people with effective services, especially in the case of smart cities. With these devices, a very healthy and intelligent environment can be created for people to live in safely and happily. With the use of modern technologies in integration with smart sensors, it is possible to use energy resources very productively. Smart vehicles can be developed to sense any emergency, to avoid injuries and fatal accidents. These sensors can be very helpful in management and monitoring activities for the enhancement of productivity. Several significant aspects are obtained from the available literature, and significant articles are selected from the literature to properly examine the uses of sensor technology for the development of smart infrastructure. The analytical hierarchy process (AHP) is used to give these attributes weights. Finally, the weights are used with the multi-objective optimization on the basis of ratio analysis (MOORA) technique to provide the different options in their order of importance. Full article

Decision making is the process of making choices by organizing relevant information and evaluating alternatives. MCDMs (Multi-Criteria Decision Methods) help to select and prioritize alternatives step by step. These tools can help in many engineering fields where the problem is complex and advanced. However, there are some limitations of the different MCDMs that reduce the reliability of the decision that needs to be improved and highlighted. In this study, Elimination and Choice Expressing Reality (ELECTRE) NI (Net Inferior), NS (Net Superior), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), VIekriterijumsko KOmpromisno Rangiranje (VIKOR), Multi-Objective Optimization Ratio Analysis (MOORA), Weight Sum Method (WSM) and Weighted Product Method (WPM) are applied for the selection of urban heat mitigation measurements under certain criteria. The models were applied using weighting criteria determined by two ways, (i) the direct weighting method and (ii) the Analytic Hierarchy Process (AHP), for precise weighting factoring through pairwise comparison. This numerical research evaluated the reliability of MCDMs using the same decision matrix under different normalization techniques and shows the impact of AHP on the decision. The results show that WSM and PROMETHEE provided reliable and consistent results for all normalization techniques. The combination of AHP with applied MCDMs improved the frequency of consistent ranking, except with ELECTRE-NS. Full article

The present study aims to create groups of symmetrical autonomous metro lines that are united by common features. An integrated six-step methodology which proposes a new aggregated approach for multi-criteria evaluation of fully autonomous metro systems was proposed. The first step determines the criteria to assess the autonomous metro system. Eight criteria connected to the safety, infrastructural and technological development of the autonomous metro system were chosen. In the second step, 20 fully autonomous metro systems in European countries were selected as alternatives. The determination of the criteria weights was performed in the third step based on objective, subjective and combined approaches. For this purpose, the Shannon Entropy method and BWM (Best Worst method) were applied. The fourth step presents the ranking of the autonomous metro system by using multi-criteria methods. Three approaches were studied: distance-based, utility-based and outranking approaches. The distance-based approach includes the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and EDAS (Evaluation Based on Distance from Average Solution) methods; the utility-based approach includes MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) and COPRAS (COmplex PRoportional Assessment) methods; the outranking approach includes the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) method. The final ranking based on the new aggregative approach was carried out in the fifth step. Thus, Laplace’s criterion was applied to the final ranking. The Hurwitz’s criterion was used to verify the results. In the sixth step, the verification of the results was performed by applying cluster analysis. In was found that Line 1 in Paris is the best. Line 14 in Paris and Line D in Lyon were ranking in the second and third position, respectively. The autonomous metro in Brescia, Line C in Rome, and Line M2 in Lausanne were placed at the end of the ranking. Finally, four clearly formed groups of autonomous metro were proposed. The novelty of this study and its main advantage entails the elaboration of a new aggregated approach of multi-criteria methods, evaluation of the autonomous metro systems’ performance and determination for the groups of symmetrical autonomous lines in European countries. Full article