Using RAM method for optimal selection of flame retardant nanocomposite material fabrication solution

This study aimed to optimize the selection of manufacturing solutions for flame retardant nanocomposite materials based on polyvinyl chloride (PVC). A total of eight different options were considered. The first option utilized PVC as the base material, and the subsequent options were carried out by adding specific amounts of reinforcing agents, including aluminum hydroxide (ATH) and zinc borate (ZB). The seven following options were denoted by their respective symbols: 5ATH/PVC, 10ATH/PVC, 15ATH/PVC, 5ZB/PVC, 10ZB/PVC, 15ZB/PVC, and 5ATH/5ZB/PVC. The number preceding the symbol of the reinforcing agent represents the percentage of the reinforcing agent added to the PVC material. For example, 5ATH/PVC signifies the addition of 5% of ATH reinforcing agent to the PVC material. To evaluate each option, five different indices were employed. The weight for each index was determined using four different methods, including the Equal method, Entropy method, MEREC method, and LOPCOW method. The RAM method was used to select the best option. The combination of the RAM method and the four weight determination methods generated four different datasets of option rankings. In all four of these datasets, the best and worst options consistently matched. The results indicated that the 15ATH/PVC option was deemed the best, while the pure PVC option was the worst

Study on model for cutting force when milling SCM440 steel

This article presents empirical study results when milling SCM440 steel. The cutting insert to be used was a TiN coated cutting insert with tool tip radius of 0.5 mm. Experimental process was carried out with 18 experiments according to Box-Behnken matrix, in which cutting speed, feed rate and cutting depth were selected as the input parameters of each experiment. In addition, cutting force was selected as the output parameter. Analysis of experimental results has determined the influence of the input parameters as well as the interaction between them on the output parameters. From the experimental results, a regression model showing the relationship between cutting force and input parameters was built. Box-Cox and Johnson data transformations were applied to construct two other models of cutting force. These three regression models were used to predict cutting force and compare with experimental results. Using parameters including coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)) and percentage mean absolute error (% MAE) between the results predicted by the models and the experimental results are the criteria to compare the accuracy of the cutting force models. The results have determined that the two models using two data transformations have higher accuracy than model not using two data transformations. A comparison of the model using the Box-Cox transformation and the model using the Johnson transformation was made with a t-test. The results confirmed that these two models have equal accuracy. Finally, the development direction for the next study is mentioned in this articl

Research on selection of abrasive grain size and cutting parameters when grinding of interrupted surface using aluminum oxide grinding wheel with ceramic binder

In this article, a study on intermittent surface grinding using aluminum oxide grinding wheel with ceramic binder is presented. The testing material is 20XH3A steel (GOST standard – Russian Federation). The testing sample has been sawn 6 grooves, with the width of each groove of 10 mm, the grooves are evenly distributed on the circumference of sample. The testing sample resembles a splined shaft. An experimental matrix of nine experiments has been built by Taguchi method, in which abrasive grain size, workpiece speed, feed rate and depth of cut were selected as input variables. At each experiment, surface roughness (Ra) and roundness error (RE) have been measured. Experimental results show that the aluminum oxide and ceramic binder grinding wheels are perfectly suitable for grinding intermittent surface of 20XH3A steel. Data Envelopment Analysis based Ranking (DEAR) method has been used to solve the multi-objective optimization problem. The results also showed that in order to simultaneously ensure minimum surface roughness and RE, abrasive grain size is 80 mesh, workpiece speed is 910 rpm, feed rate is 0.05 mm/rev and depth of cut is 0.01 mm. If evaluating the grinding process through two criteria including surface roughness and RE, depth of cut is the parameter having the greatest effect on the grinding process, followed by the influence of feed rate, workpiece speed, and abrasive grain is the parameter having the least effect on the grinding process. In addition, the effect of each input parameter on each output parameter has also been analyzed, and orientations for further works have also been recommended in this articl

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

COMPARISON R AND CURLI METHODS FOR MULTI-CRITERIA DECISION MAKING

When multi-criteria decision making, decision makers will expend significant effort in selecting a data normalization method and a weighting method. If a mistake is made in those choices, it will result in decisions that do not find the best solution. Furthermore, with qualitative criteria, it is impossible to standardize the data. Similarly, determining the weights of criteria will be difficult if the criteria are in qualitative form. R and CURLI are two multi-criteria decision-making methods that do not require data normalization or the use of additional weighting methods for the criteria. They are therefore well suited for ranking alternatives when the criteria are both quantitative and qualitative. This study compares the two methods through three examples from different fields. The results show that these two methods jointly determine the best solution in all three fields and are also suitable when using other decision-making methods that require data normalization and have high requirements using the method of determining the weights for the criteria

Development of surface roughness model in turning process of 3X13 steel using TiAlN coated carbide insert

Surface roughness that is one of the most important parameters is used to evaluate the quality of a machining process. Improving the accuracy of the surface roughness model will contribute to ensure an accurate assessment of the machining quality. This study aims to improve the accuracy of the surface roughness model in a machnining process. In this study, Johnson and Box-Cox transformations were successfully applied to improve the accuracy of surface roughness model when turning 3X13 steel using TiAlN insert. Four input parameters that were used in experimental process were cutting velocity, feed rate, depth of cut, and insert-nose radius. The experimental matrix was designed using Central Composite Design (CCD) with 29 experiments. By analyzing the experimental data, the influence of input parameters on surface roughness was investigated. A quadratic model was built to explain the relationship of surface roughness and the input parameters. Box-Cox and Johnson transformations were applied to develop two new models of surface roughness. The accuracy of three surface roughness models showed that the surface roughness model using Johnson transformation had the highest accuracy. The second one model of surface roughness is the model using Box-Cox transformation. And surface roughness model without transformation had the smallest accuracy. Using the Johnson transformation, the determination coefficient of surface roughness model increased from 80.43 % to 84.09 %, and mean absolute error reduced from 19.94 % to 16.64 %. Johnson and Box-Cox transformations could be applied to improve the acuaracy of the surface roughness prediction in turning process of 3X13 steel and can be extended with other materials and other machining processe

STUDY ON MULTI-OBJECTIVE OPTIMIZATION OF THE TURNING PROCESS OF EN 10503 STEEL BY COMBINATION OF TAGUCHI METHOD AND MOORA TECHNIQUE

In this study, the multi-objective optimization problem of turning process was successfully solved by a Taguchi combination method and MOORA techniques. In external turning process of EN 10503 steel, surface grinding process, the orthogonal Taguchi L9 matrix was selected to design the experimental matrix with four input parameters namely insert nose radius, cutting velocity, feed rate, and depth of cut. The parameters that were chosen as the evaluation criteria of the machining process were the surface roughness (Ra), the cutting force amplitudes in X, Y, Z directions, and the material removal rate (MRR). Using Taguchi method and MOORA technique, the optimized results of the cutting parameters were determined to obtain the minimum values of surface roughness and cutting force amplitudes in X, Y, Z directions, and maximum value of MRR. These optimal values of insert nose radius, cutting velocity, feed rate, and cutting depth were 1.2 mm, 76.82 m/min, 0.194 mm/rev, and 0.15 mm, respectively. Corresponding to these optimal values of the input parameters, the surface roughness, cutting force amplitudes in X, Y, Z directions, and material removal rate were 0.675 µm, 124.969 N, 40.545 N, 164.206 N, and 38.130 mm3/s, respectively. The proposed method in this study can be applied to improve the quality and effectiveness of turning processes by improving the surface quality, reducing the cutting force amplitudes, and increasing the material removal rate. Finally, the research direction was also proposed in this stud

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Determining weights for criteria is an extremely crucial step in the process of selecting an option based on multiple criteria, also known as Multi-Criteria Decision Making (MCDM). This article presents the combination of five objective weighting methods for criteria with three MCDM methods in the context of material selection. The five objective weighting methods considered are Entropy, MEREC (Method based on the Removal Effects of Criteria), LOPCOW (Logarithmic Percentage Change-driven Objective Weighting), CRITIC (Criteria Importance Through Intercriteria Correlation), and MEAN. The three MCDM methods employed are MARA (Magnitude of the Area for the Ranking of Alternatives), RAM (Root Assessment Method), and PIV (Proximity Indexed Value). Material selection investigations were conducted in three different cases, including lubricant selection for two-stroke engines, material selection for manufacturing screw shafts, and material selection for manufacturing gears. The Spearman's rank correlation coefficient was calculated to assess the stability of ranking the alternatives using different MCDM methods. The combinations of objective weighting methods and MCDM methods were evaluated based on factors such as consistency in identifying the best material type, range, average value, and median of each set of Spearman's rank correlation coefficients. Two significant findings were identified. First, the weights of criteria calculated using LOPCOW method appear to be inversely related to those calculated using the Entropy method. Second, among the three MCDM methods used, MARA was identified as the most suiTable for lubricant selection for two-stroke engines, RAM was found to be the most suiTable for material selection for screw shafts and gears. The best material type in each case was also determine

Persistent Test-time Adaptation in Episodic Testing Scenarios

Current test-time adaptation (TTA) approaches aim to adapt to environments that change continuously. Yet, when the environments not only change but also recur in a correlated manner over time, such as in the case of day-night surveillance cameras, it is unclear whether the adaptability of these methods is sustained after a long run. This study aims to examine the error accumulation of TTA models when they are repeatedly exposed to previous testing environments, proposing a novel testing setting called episodic TTA. To study this phenomenon, we design a simulation of TTA process on a simple yet representative $\epsilon$-perturbed Gaussian Mixture Model Classifier and derive the theoretical findings revealing the dataset- and algorithm-dependent factors that contribute to the gradual degeneration of TTA methods through time. Our investigation has led us to propose a method, named persistent TTA (PeTTA). PeTTA senses the model divergence towards a collapsing and adjusts the adaptation strategy of TTA, striking a balance between two primary objectives: adaptation and preventing model collapse. The stability of PeTTA in the face of episodic TTA scenarios has been demonstrated through a set of comprehensive experiments on various benchmarks

Comparision of both methods psi and curli: applied in solving multi-objective optimization problem of turning process

Solving a multi-objective optimization problem involves finding the best solution to simultaneously satisfy multiple predefined objectives. Currently, various mathematical methods are available for solving optimization problems in general, and multi-objective optimization in particular. The comparison of mathematical methods when addressing the same problem has been explored in numerous studies. In this study, let’s conduct a comparison of two multi-objective optimization methods: the PSI method and the CURLI method. These two methods were applied collectively to tackle a multi-objective optimization problem related to a turning process. Experimental data were borrowed from a previous study, and a total of sixteen experiments were conducted. Roughness average (Ra), Roundness Error (RE), Tool Wear (VB), and Material Removal Rate (MRR) were the four output parameters measured in each experiment. The objective of solving the multi-objective optimization problem was to identify an experiment among the sixteen existing experiments that simultaneously minimized the three parameters of Ra, RE, and VB while maximizing MRR. The optimal results determined using the PSI and CURLI methods were also compared with the optimal results obtained through other methods (COCOSO, MABAC, MAIRCA, EAMR and TOPSIS) in published documents. The comparison results indicate that the optimal experiment found using the CURLI method consistently matches that of other methods. In contrast, the optimal results obtained through the PSI method differ significantly from those obtained through other methods. The Spearman correlation ranking coefficient between CURLI and the five methods COCOSO, MABAC, MAIRCA, EAMR, and TOPSIS is very high, ranging from 0.9 to 1. In contrast, this coefficient is very small when comparing PSI with the aforementioned five methods, falling within the range of –0.6088 to –0.3706 in this case. Ultimately, this study concludes that the CURLI method is suiTable for solving the multi-objective optimization problem in the turning process, whereas the PSI method is deemed unsuitabl