Some applications of a novel desirability function in simultaneous optimization of multiple responses

Marinković, V.

doi:10.5937/fme2103534m

Cited by 8 publications

(7 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DF includes several parameters which result in decision-makers’ preference(s) (Lee et al. , 2018; Marinković, 2021). For simultaneous optimization of process parameters for FDM machines, an MRO problem could be formulated as equation (4): …”

Section: Resultsmentioning

confidence: 99%

“…Desirability Function (DF), which is explained by Lee et al (2018), is a dimensionality reduction approach, often used to optimize multiple response problems. DF includes several parameters which result in decision-makers' preference(s) (Lee et al, 2018;Marinkovi c, 2021). For simultaneous optimization of process parameters for FDM machines, an MRO problem could be formulated as equation ( 4):…”

Section: Subject To X2fxgmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Hashemi Baghi,

Mansour

2024

RPJ

View full text Add to dashboard Cite

Purpose Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters. Design/methodology/approach In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model. Findings The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value. Originality/value By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Subject To X2fxgmentioning

confidence: 99%

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Hashemi Baghi,

Mansour

2024

RPJ

View full text Add to dashboard Cite

show abstract

“…For more details on this DF, which was used in the present research, readers are referred to recent studies. 23,24 The overall desirability was defined by Harrington 17 and Derringer and Suich, 18 respectively, as the geometric mean and as the weighted geometric mean of the individual desirability functions:…”

Section: Desirability Function Approach (Dfa)mentioning

confidence: 99%

“…To this aim, a shape-based and/or bound-based interactive procedures can be implemented. 24,28 According to the initial solution and bounds given in Table 3, the bound-based interactive approach seems to be a logical choice in this example. Therefore,…”

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Multi-response optimization in laser processing technologies by applying desirability function approach and response surface methodology based on grey relation analysis

Madić,

Marinković

2024

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

Laser processing technologies are among the leading industry technologies for efficient and economical processing of a wide spectrum of engineering materials. The determination of optimal parameter settings with respect to multiple and opposite process performances is of great practical importance in laser processing technologies. This paper discusses, analyzes, and compares two main approaches for multi-response optimization (MRO) in engineering, that is, desirability function approach (DFA) and grey relation analysis (GRA), while solving five case studies, covering different laser processing technologies, such as cutting, drilling, welding, micro-channeling, and cladding. In each case study the MRO solutions obtained with two competitive approaches were assessed using the relative target deviation (RTD) criterion. Analysis of the obtained MRO solutions and comparative analysis with results from previous studies indicate that the integrated RSM-GRA provide similar and comparable solutions with the hybrid Taguchi method and GRA, while DFA proved to be a better and more promising approach for solving MRO in laser processing technologies. Some specific features, limitations, and possibilities of MRO approaches were also discussed.

show abstract

Normalization for the Case “Nominal Value the Best”

Mukhametzyanov¹

2023

International Series in Operations Research &Amp; Management Science

View full text Add to dashboard Cite

Some applications of a novel desirability function in simultaneous optimization of multiple responses

Cited by 8 publications

References 41 publications

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Multi-response optimization in laser processing technologies by applying desirability function approach and response surface methodology based on grey relation analysis

Normalization for the Case “Nominal Value the Best”

Contact Info

Product

Resources

About