Comparison of two methods in multi-criteria decision-making: application in transmission rod material selection

Transmission rod is an indispensable part in diesel and gasoline engines. Its job is to convert rotation into translational motion or vice versa. The transmission rod material selection plays a very important role, affecting its working function and durability. This study was conducted to compare two Multi Criteria Decision Making (MCDM) methods in transmission rod material selection. They are PIV (Proximity Indexed Value) method, and FUCA (Faire Un Choi Adéquat) method. Seven types of steel commonly used in transmission rods were reviewed for ranking, inclusive of: 20 steel, 40 steel, 45 steel, 18Cr2Ni4WA steel, 30 CrMoA steel, 45Mn2 steel and 40CrNi steel. Nine parameters were used as criteria to evaluate each steel including minimum yield strength, ultimate tensile strength, minimum elongation ratio, contraction ratio, modulus of elasticity, mean coefficient of thermal expansion, thermal conductivity, specific thermal capacity, and density. The weights of the criteria were calculated using three methods inclusive of MEAN weight method, Entropy weight method and MEREC weight method (Method based on the Removal Effects of Criteria). Each MCDM method was combined with the three weight methods mentioned above to rank the alternatives. The obtained results show that when using both PIV and FUCA methods to rank the alternatives, the best and worst alternatives are found regardless of the weight of the criteria. The best alternative determined using the PIV method is also the best alternative determined using the FUCA method. It means that the two PIV and FUCA methods have been shown to be equally effective. Among the seven transmission rod materials reviewed, 20 steel was identified as the best, and 40CrNi steel was identified as the wors

A research on application of the measurement of alternatives and ranking according to compromise solution method for multi-criteria decision making in the grinding process

The efficiency of cutting methods in general and the grinding method in particular is evaluated through many parameters such as surface roughness, machining productivity, system vibrations, etc. The machining process is considered highly efficient when it meets the set requirements for these parameters such as ensuring the small surface roughness, small vibrations, and high productivity, etc. However, for each specific machining condition, sometimes the set criteria for the output criteria are opposite. In these cases, it is required to solve the Multi-Criteria Decision Making (MCDM) which means making the decision to ensure the harmonization of all criteria. In this study, a study on multi-criteria decision making in the grinding process of 9CrSi steel using CBN grinding wheels is presented. The experimental process was carried out with sixteen experiments according to an orthogonal matrix that designed by the Taguchi method. The workpiece velocity, feed rate, and depth of cut were changed in each experiment. At each experiment, the responses were determined including surface roughness (Ra), the vibration of the grinding wheel shaft in the three directions, corresponding to Ax, Ay, and Az, and material removal yield (Q). Four determination methods of weights for criteria were used. The Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was applied for multi-criteria decision making. The objective of this study is to identify an experiment that simultaneously ensures the small values of Ra, Ax, Ay, and Az and large value

An Efficient Spectral Leakage Filtering for IEEE 802.11af in TV White Space

Orthogonal frequency division multiplexing (OFDM) has been widely adopted for modern wireless standards and become a key enabling technology for cognitive radios. However, one of its main drawbacks is significant spectral leakage due to the accumulation of multiple sinc-shaped subcarriers. In this paper, we present a novel pulse shaping scheme for efficient spectral leakage suppression in OFDM based physical layer of IEEE 802.11af standard. With conventional pulse shaping filters such as a raised-cosine filter, vestigial symmetry can be used to reduce spectral leakage very effectively. However, these pulse shaping filters require long guard interval, i.e., cyclic prefix in an OFDM system, to avoid inter-symbol interference (ISI), resulting in a loss of spectral efficiency. The proposed pulse shaping method based on asymmetric pulse shaping achieves better spectral leakage suppression and decreases ISI caused by filtering as compared to conventional pulse shaping filters

Uncertainty Quantification in the Directed Energy Deposition Process Using Deep Learning-Based Probabilistic Approach

peer reviewedThis study quantifies the effects of uncertainty raised from process parameters, material properties, and boundary conditions in the directed energy deposition (DED) process of M4 High-Speed Steel using deep learning (DL)-based probabilistic approach. A DL-based surrogate model is first constructed using the data obtained from a finite element (FE) model, which was validated against experiment. Then, sources of uncertainty are characterized by the probabilistic method and are propagated by the Monte-Carlo (MC) method. Lastly, the sensitivity analysis (SA) using the variance-based method is performed to identify the parameters inducing the most uncertainty to the melting pool depth. Using the DL-based surrogate model instead of solely FE model significantly reduces the computational time in the MC simulation. The results indicate that all sources of uncertainty contribute to a substantial variation on the final printed product quality. Moreover, we find that the laser power, the convection, the scanning speed, and the thermal conductivity contribute the most uncertainties on the melting pool depth based on the SA results. These findings can be used as insights for the process parameter optimization of the DED process.EDPOM

Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning

Typical computer-based parameter optimization and uncertainty quantification of the additive manufacturing process usually requires significant computational cost for performing high-fidelity heat transfer finite element (FE) models with different process settings. This work develops a simple surrogate model using a feedforward neural network (FFNN) for a fast and accurate prediction of the temperature evolutions and the melting pool sizes in a metal bulk sample (3D horizontal layers) manufactured by the DED process. Our surrogate model is trained using high-fidelity data obtained from the FE model, which was validated by experiments. The temperature evolutions and the melting pool sizes predicted by the FFNN model exhibit accuracy of 99% and 98%, respectively, compared with the FE model for unseen process settings in the studied range. Moreover, to evaluate the importance of the input features and explain the achieved accuracy of the FFNN model, a sensitivity analysis (SA) is carried out using the SHapley Additive exPlanation (SHAP) method. The SA shows that the most critical enriched features impacting the predictive capability of the FFNN model are the vertical distance from the laser head position to the material point and the laser head position.VINIF.2020.DA15 EDPOMP projec

Functional-Antioxidant Food

Nowadays, people face many different dangers, such as stress, unsafety food, and environmental pollution, but not everyone suffers. Meanwhile, free radicals are the biggest threat for humans because they lead to over 80 different diseases composed of aging. Free radicals can only be eliminated or minimized with antioxidant foods or antioxidants. The chapter on the functional-antioxidant food presents the antioxidant functional food concept, the classification, the structure, and the extraction process of antioxidant ingredients. Various antioxidant substances such as protein (collagen), polysaccharides (fucoidans, alginates, glucosamines, inulins, laminarins, ulvans, and pectins), and secondary metabolites (polyphenols (phlorotannins, lignins, polyphenols), alkaloids, and flavonoids) also present. The production technology, the mechanism, the opportunity, and the challenge of antioxidants functional food also present in the current chapter. The current chapter also gives the production process of functional-antioxidant food composed of the capsule, the tablet, tube, the pills, the powder, and the effervescent tablet

Modelling of surface roughness and tool wear when finish milling process of the circular bevel gear

An experimental process to build the models of surface roughness and tool wear in the finish milling of the Gleason circular bevel gears was carried out in this study. The experiments were conducted according to a Box-Behnken matrix. Three cutting parameters were adjusted in each experiment including cutting speed, feed rate, and depth of cut. From the experimental results, the influences of cutting parameters on the surface roughness and tool wear were analysed in detail. Two models of surface roughness and tool wear were established with high accuracy. The optimal values of the cutting parameters were also determined to simultaneously ensure the minimum values of two output parameters. The further research directions were also suggested at the end of this study

Comparison of the

Multi-criteria decision-making (MCDM) methods are used in many fields so as to rank alternatives and find the best one. However, rank reversal after adding or removing an alternative can occur in using some of the methods. In this study, two methods RAFSI and PIV were compared for application of making multi-criteria decisions. They are known to be capable of avoiding rank reversal problems. Sixteen 9XC steel turning tests were performed for the experiment. Tool holder length, spindle speed, feed rate and depth of cut are parameters that vary in each test. Three criteria for evaluating the turning process consist of MRR, RE and Ra. Four methods including MEREC, ROC, RS and EQUAL were used for determining weights of the criteria. The blend of two multi-criteria decision making methods (RAFSI and PIV) with four weight-determining methods resulted in eight ranking options. This is a new approach of the study. A positive outcome was reached that all eight ranking options identified the same best test. The best experiment must ensure to have maximum MRR and minimum RE and Ra simultaneously. A detailed discussion of the ranking results in each case was also carried out. Finally, the directions and issues that need to be studied further were pointed out in this paper as well

Застосування методів багатокритеріального прийняття рішень при виборі мастильно-охолоджуючої емульсії

Many machining processes would not be possible without the presence of cutting oils. There are many diffe­rent types of cutting oils on the market today, each with different properties. The difference of oils is manifested in many parameters such as viscosity, combustion temperature, recyclability, pollution tendency, stability, price, etc. Choosing the best oil is a difficult and tedious task for customers. In this work, we present the results of a study on the selection of cutting oil using multi­criteria decision­making (MCDM) methods. The selection of the best oil is made on the basis of ranking of seven different types. Two MCDM methods used in this study are Proximity Indexed Value (PIV) and Collaborative Unbiased Rank List Integration (CURLI). This two methods have been used to rank cutting oils. These are two methods with completely different characteristics. When using the PIV method, it is necessary to standardize the data and determine the weights for the criteria. Meanwhile, if using the CURLI method, these two tasks are not needed. In addition, three different weight methods were also used to calculate the weights for the criteria including EQUAL, Rank Order Centroid weight (ROC weight) and Rank Sum weight (RS weight). These three methods have been used to determine the weights for the criteria of cutting oil. The PIV method was used three times corresponding to three different weight methods. The results showed that out of the four ranking results (three using the PIV method and one using the CURLI method), the same best oil was unani­mously identified. It is recommended that the CURLI method should be used if weighting of criteria and data normalization are not desiredБагато процесів механічної обробки були б неможливі без наявності мастильно­охолоджуючих емульсій. Сьогодні на ринку представлено безліч різних видів мастильно­охолоджуючих емульсій, кожна з яких володіє різними властивостями. Різниця між емульсіями проявляється у багатьох параметрах, таких як в’язкість, температура горіння, можливість переробки, схильність до забруднення, стійкість, ціна тощо. Вибір кращої емульсії є складним та стомлюючим завданням для покупців. У даній роботі представлені результати дослідження щодо вибору мастильно­охолоджуючої емульсії з використанням методів багатокритеріального прийняття рішень (MCDM). Вибір кращої емульсії проводиться на основі ранжування семи різних видів. У дослідженні використовувалися два методи MCDM: індексоване за близькістю значення (PIV) та спільна інтеграція незміщеного списку рангів (CURLI). Ці два методи використовуються для ранжування мастильно­охолоджуючих емульсій і мають абсолютно різні характеристики. При використанні методу PIV необхідно стандартизувати дані та визначити ваги критеріїв. Тим часом, при використанні методу CURLI ці два завдання не потрібні. Крім того, для розрахунку ваг критеріїв також використовувались три різні вагові методи, включаючи рівний (EQUAL), вагу центроїда порядку ранжування (вага ROC) та вагу суми рангів (вага RS). Дані три методи також використовувалися для визначення ваг критеріїв мастильно­охолоджуючої емульсії. Метод PIV застосовувався тричі відповідно до трьох різних вагових методів. Результати показали, що з чотирьох результатів ранжування (три з використанням методу PIV і один з використанням методу CURLI) була одноголосно визначена одна і та ж краща емульсія. Метод CURLI рекомендується використовувати, якщо не потрібне зважування критеріїв та нормалізація дани