A comprehensive MCDM-based approach using TOPSIS and EDAS as an auxiliary tool for pyrolysis material selection and its application

Dhanalakshmi, C. Sowmya; Madhu, P.; Karthick, Alagar; Mathew, Manoj; Kumar, Raman

doi:10.1007/s13399-020-01009-0

Cited by 60 publications

(25 citation statements)

References 75 publications

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“…TOPSIS is one of the multi-criteria decision-making methods. It is characterized by being simple, logical and computationally efficient, and is considered one of the best ranking methods [41]. It is based on an organized procedure that ranks the alternatives with respect to some attributes (criteria) in such a way that the best alternative would have the smallest distance from the ideal solution and the largest distance from the non-ideal solution [42].…”

Section: B Ranking Sensory Features Based On Topsismentioning

confidence: 99%

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Assafo

Langendoerfer

2021

IEEE Access

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Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approaching TCM solutions. This work mainly addresses the issues associated with multi-operating-condition TCM models, namely the varying discrimination ability of sensory features; the overlap between normal and anomalous data; and the complex structure of input data. A feature selection scheme is proposed in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is presented as a tool to aid the multi-objective selection of sensory features. In addition, four anomaly detection approaches based on Self-Organizing Map (SOM) are studied. To examine the stability of the four approaches, they are applied on different single-operating-condition models. Further, to examine their robustness when dealing with complex data structures, they are applied on multi-operating-condition models. The experimental results using the NASA Milling Data Set showed that all the studied anomaly detection approaches achieved a higher assessment accuracy with our feature selection scheme as compared to Principal Component Analysis (PCA), Laplacian Score (LS), and extended LS in which we added a final step to the original LS method in order to eliminate redundant features.

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Section: B Ranking Sensory Features Based On Topsismentioning

confidence: 99%

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Assafo

Langendoerfer

2021

IEEE Access

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“…Overall, nanofluids have less thermal resistance than deionized water, which is often representative of lower microchannel wall temperature vital to the heat dissipation of photovoltaic module concentration and electronics devices. Almost 32.5% and 26% of thermal resistance decrease in Al 2 O 3 /water by 0.25% and 0.1% compared to deionized water [31][32][33][34][35][36] Figure 11 shows the microchannel conductor pressure drop International Journal of Photoenergy concerning different Reynolds number conditions using Al 2 O 3 /water nanofluids (0.1%, 0.25%) and deionized water. When rising the Reynolds number, increases of the pressure drop of fluids were noticed.…”

Section: Influence Of Impact In Concentration Percentages Inmentioning

confidence: 99%

Investigation on Heat Transfer Enhancement in Microchannel Using Al2O3/Water Nanofluids

Munimathan¹,

Sathish

Mohanavel

et al. 2021

International Journal of Photoenergy

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Nowadays, reducing heat generation in electronic devices while using microchannel cooling is used to solve this problem. Because the trend is globally marching toward the compact size, the component’s dimensions get smaller, but the warmth involved within the component increases. Studies of heat transfer rate are conducted to determine the effect of a fully heated microchannel conductor’s heat transfer performance. Experiments are performed using nanofluid Al2O3/water through a concentration percentage of 0.1% and 0.25% and deionized water through a microchannel conductor with 25 rectangular microchannel numbers with a dimension of ( 0.42 × 0.42 × 100 ) mm3. This present work deals with the effect of nanofluids and their concentration percentages. Finally, it concluded that better heat transfer performance was seen in nanofluids compared to deionized water. The reason is the high viscosity of nanofluid Al2O3/water due to these nanoparticles is deposited on the wall surface of the microchannel and outcomes trendy improvement in the heat transfer. Finally, a high concentration percentage of nanofluids revealed a practical improvement in the transfer of microchannel. As a result, 0.25% of the concentration percentage achieved a satisfactory result compared to the remaining fluids and almost 32.5% and 26% of thermal resistance decrease.

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“…The correct material selection technique entails precisely describing the application requirement in terms of mechanical properties, primarily for the utility class defined in the proposed application. Various researchers have used MADM methods such as VIKOR, EDAS, WSM PROMETHE, and TOPSIS to select the best material for specific applications in a range of fields such as automotive [11][12][13][14][15][16], marine [17,18], medical [19], and agriculture [20][21][22]. The VIKOR method outperformed the other 10 most common methods for selecting suitable materials for a sailing boat mast, a flywheel, and a cryogenic storage tank, according to the author [23].…”

Section: Introductionmentioning

confidence: 99%

Selection of Optimal Material from Stir Cast Aluminum Graphene Nano Platelets Composites for Aerospace Applications

Palampalle¹,

Dharmalingam²,

Royal³

2022

Aluminium Alloys - Design and Development of Innovative Alloys, Manufacturing Processes and Applications

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Qualitative and quantitative requirements when selecting materials for different properties can be difficult and ambiguous. An insufficient variety of materials can lead to component malfunction and failure at any point during their service. Owing to the vast availability of dissimilar materials, material selection in the engineering design phase is difficult and elusive. This study presents an EDAS (Evaluation based on Distance from Average Solution) and VIKOR (VIse Kriterijumska Optimizacijakompromisno Resenje) techniques for effective material selection for aviation applications. In this research, the selection index value was calculated using the EDAS and VIKOR entropy-based weight techniques. The MADM (multi-attribute decision making) procedure also selects the best weight per cent combination among pure aluminum reinforced with GNPs (graphene nanoplatelets) for aircraft applications based on its physical and mechanical properties. The results demonstrate that 0.5 wt% GNPs reinforced in pure aluminum has the best combination of both physical and mechanical qualities, according to the EDAS and VIKOR multi-criteria decision-making methodologies. The composites were made using the stir casting technique. MATLAB R2020a is used to grade and compare the composite materials.

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A comprehensive MCDM-based approach using TOPSIS and EDAS as an auxiliary tool for pyrolysis material selection and its application

Cited by 60 publications

References 75 publications

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Investigation on Heat Transfer Enhancement in Microchannel Using Al2O3/Water Nanofluids

Selection of Optimal Material from Stir Cast Aluminum Graphene Nano Platelets Composites for Aerospace Applications

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