Optimizing Drilling Induced Delamination in GFRP Composites using Genetic Algorithm&amp; Particle Swarm Optimisation

Kalita, Kanak; Mallick, Pradeep Kumar; Bhoi, Akash Kumar; Ghadai, K.R.

doi:10.1177/096369351802700101

Cited by 23 publications

(13 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rate of latency was very high in this method, which is a disadvantage. A novel approach is proposed in ( 21 ) on the basis of experimental evaluation on polyster compounds reinforced with fiberglass. The PSO algorithm and genetic algorithm were used to predict global optimum, and it was inferred that the convergence of the PSO algorithm was very fast and needed less execution time.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Skewed Data Using Sampling-Based Preprocessing Approach

Mishra

Mallick

Jena

et al. 2020

Front. Public Health

View full text Add to dashboard Cite

In the past few years, classification has undergone some major evolution. With a constant surge of the amount of data gathered from different sources, efficient processing and analysis of data is becoming difficult. Due to the uneven distribution of data among classes, data classification with machine-learning techniques has become more tedious. While most algorithms focus on major data samples, they ignore the minor class data. Thus, the data-skewing issue is one of the critical problems that need attention of researchers. The paper stresses upon data preprocessing using sampling techniques to overcome the data-skewing problem. Here, three different sampling techniques such as Resampling, SpreadSubSampling, and SMOTE are implemented to reduce this uneven data distribution issue and classified with the K-nearest neighbor algorithm. The performance of classification is evaluated with various performance metrics to determine the efficiency of classification.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimization of Skewed Data Using Sampling-Based Preprocessing Approach

Mishra

Mallick

Jena

et al. 2020

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Genetic algorithms efficiently optimize processes in an evolutionary manner 25–27 and find many applications. 28–30 A flow chart of the genetic algorithm for the JC model is shown in Figure 3. The initial state features a group of random chromosomes representing combinations of JC parameters.…”

Section: Machining-based Plastic Behaviormentioning

confidence: 99%

Simulation of the round insert face milling process of AISI 316LN stainless steel with machining-based plastic behavior modeling

Kim

Kwon³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

A proper plastic behavioral model is required to simulate metal cutting. Here, we model the plastic behavior of AISI 316LN stainless steel during face milling. We used a numerical approach to derive a plasticity model appropriate for machining; a two-dimensional cutting force prediction and a genetic algorithm were conducted for that. The force prediction was performed considering a geometrical relationship between the work material and cutting tool. We used the Johnson-Cook (JC) constitutive material model, and initial model parameters were obtained via tension testing at low strain rates (0.001–1 s−1). The genetic algorithm optimized the model parameters; the predictive accuracy with respect to cutting force was high in the model with optimized parameters. We used the optimized JC model for finite element analysis and simulated face milling with a round insert. We measured the cutting forces to validate our modeling approach; the simulated and measured principal forces were in good agreement (error rate ≤ 3.9% under all machining conditions). Our model improved the accuracy of plastic behavior prediction by 93.0% versus the original model. The high accuracy was retained even when the machining environment changed.

show abstract

“…A metamodel can be used to find out the combination of all the input parameters to achieve the aimed response parameters. The most widely used metamodeling technique for the optimization of manufacturing /machining process is the Response Surface Methodology (RSM), which is essentially a polynomial regression approach [9][10][11][12][13]. Some authors have also used some advanced metamodeling techniques like artificial neural network (ANN) [14][15][16], ANFIS [17], gene expression programming (GEP) [3] in the machining operation.…”

Section: Introductionmentioning

confidence: 99%

Symbolic regression metamodel based multi-response optimization of EDM process

Kumar¹,

Kalita²,

Gao³

2020

FME Transactions

View full text Add to dashboard Cite

Electrical Discharge Machining (EDM) is a popular non-traditional machining process that is widely used due to its ability to machine hard and brittle materials. It does not require a cutting tool and can machine complex geometries easily. However, it suffers from drawbacks like a poor rate of machining and excessive tool wear. In this research, an attempt is made to address these issues by using a metamodel coupled with global optimization approach to predict suitable combinations of input parameters (current, pulse on-time and pulse off-time) that would effectively increase the material removal rate and minimize the tool wear. The metamodels are built by using a novel symbolic regression approach carried out using Genetic Programming (GP). On comparative evaluation against traditional response surface methodology (RSM) metamodels, the GP metamodels show much better and accurate estimation. GP metamodels are then coupled with a genetic algorithm to carry out multiobjective optimization of the EDM process.

show abstract

Optimizing Drilling Induced Delamination in GFRP Composites using Genetic Algorithm& Particle Swarm Optimisation

Cited by 23 publications

References 22 publications

Optimization of Skewed Data Using Sampling-Based Preprocessing Approach

Optimization of Skewed Data Using Sampling-Based Preprocessing Approach

Simulation of the round insert face milling process of AISI 316LN stainless steel with machining-based plastic behavior modeling

Symbolic regression metamodel based multi-response optimization of EDM process

Contact Info

Product

Resources

About