Combined PIPRECIA method and modified FUCA method for selection of lathe

Trung, Do Duc; Trưởng, Nguyễn Văn; Thinh, Hoang Xuan

doi:10.5937/jaes0-39335

Cited by 14 publications

(9 citation statements)

References 33 publications

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“…When qualitative criteria are introduced, calculating weights for these criteria using the Entropy, CRITIC, and SD methods can be challenging. In such cases, the PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method can be a viable alternative [20]. In the case where the number of lubricant types needs to be ranked either increased or decreased, combining the Design of Experiments (DOE) method with the COCOSO method can be applied to quickly rank the lubricant types without the need to redo the entire calculation process [21][22][23].…”

Section: Fig 1 Ranking Of Lubricant Typesmentioning

confidence: 99%

Selecting lubricating oil for two-stroke gasoline engines: a multi-criteria decision-making approach

Mai,

Thinh

2024

Eureka: PE

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The two-stroke engine boasts advantages in terms of simpler manufacturing and a smaller size when compared to the four-stroke engine. Vehicles powered by two-stroke engines can thus effortlessly overcome road obstacles compared to their four-stroke counterparts. However, the use of a two-stroke engine results in higher carbon monoxide and hydrocarbon emissions than that of a four-stroke engine. This discrepancy places greater demands on the selection of lubricating oil for two-stroke engines compared to four-stroke engines. In market, there exists a multitude of lubricating oil options tailored for two-stroke engines, each characterized by varying technical parameters. These disparities are expressed through factors such as density, viscosity index, viscosity, and combustion temperature, among others. Consequently, the task of choosing the optimal lubricant becomes a complex endeavor for consumers. In this study, an examination of lubricant selection is presented using a Multi-Criteria Decision-Making (MCDM) approach. The MCDM method employed in this article is the Combined Compromise Solution (COCOSO) method. The selection of the best lubricant is based on an evaluation of four distinct types. Each type of oil is described by four key parameters (criteria): density, viscosity index, viscosity at 100 °C, and viscosity at 40 °C. The weights for these four criteria are determined through three different methods, including the Entropy method, Criteria Importance Through Intercriteria Correlation (CRITIC) method, and Standard Deviation (SD) method. Thus, the ranking of lubricants is conducted three times, corresponding to these three weighting methods. The results indicate that the best oil choice remains consistent regardless of the weighting method applied

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Section: Fig 1 Ranking Of Lubricant Typesmentioning

confidence: 99%

Selecting lubricating oil for two-stroke gasoline engines: a multi-criteria decision-making approach

Mai,

Thinh

2024

Eureka: PE

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“…Similarly, the CURLI method has been applied to solve multi-objective optimization problems in diverse fields. This includes optimizing the selection of equipment such as air conditioners, washing machines, and drones [15], optimizing the selection of lathes [16], optimizing the selection of three other types of machine tools (grinders, drills, millers) [17], optimizing the selection of various materials (materials for making protective plates on automobiles, materials for making gears, materials for cutting tools) [18], optimizing the selection of cars in Vietnam [19], optimizing the selection of three types of woodworking machines (wood millers, wood saws, wood planers) [20], optimizing the selection of grinding wheels and suppliers [21], and more.…”

Section: Mathematicsmentioning

confidence: 99%

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

Trung,

Phuong Giang,

Son

2024

Eureka: PE

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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 unsuitable

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“…Criteria weights were determined with the analytic hierarchy process (AHP). Trung et al (2022) applied combination of PIPRECIA and FUCA methods for solving MCDM problems, i.e., selection of a new lathe and selection of used lathe. A comprehensive MCDM approach for evaluation of alternative CNC machine tools with respect to seven main and thirteen sub-criteria was proposed by Sahin and Aydemir (2022).…”

Section: Introductionmentioning

confidence: 99%

Selection of cutting insert for longitudinal turning of unalloyed steel using robust decision making

Madić,

Trifunović,

Petrović

2024

XIX International May Conference on Strategic Management – IMCSM24 Proceedings - Zbornik Radova

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Cutting tool is a very important element of machining production system. It is primarily responsible for material removal in the form of chips, but also significantly affects multiple machinability characteristics, surface finish, attainable dimensional accuracy, productivity and costs. As for a given machining operation there is a number of alternative cutting tools and inserts from many manufacturers, each characterized by a unique set of characteristics, the selection of a particular cutting tool can be very complex task, yet solvable within the framework of multi-criteria decision making (MCDM) methodology. This study is focused on the development of an MCDM model for selection of the most suitable cutting insert for medium machining of unalloyed structural steel. The model was developed by available information, catalogues of cutting tool manufacturers and machining estimations, and consisted of fourteen alternative cutting inserts from eight well-known cutting tool manufacturers and seven criteria. Initially, the assessment and ranking of alternative cutting inserts was derived by the six multi-criteria decision making (MCDM) methods, however, due to ranking inconsistency, the application of the robust decision making rule was adopted for solving the cutting insert MCDM problem.

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Combined PIPRECIA method and modified FUCA method for selection of lathe

Cited by 14 publications

References 33 publications

Selecting lubricating oil for two-stroke gasoline engines: a multi-criteria decision-making approach

Selecting lubricating oil for two-stroke gasoline engines: a multi-criteria decision-making approach

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

Selection of cutting insert for longitudinal turning of unalloyed steel using robust decision making

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