Experimental Investigation, Modeling and Optimization of Circularity, Cylindricity and Surface Roughness in Drilling of  PMMA Using ANN and ANOVA

Susac, Florin; Stan, Felicia

doi:10.37358/mp.20.1.5312

“…In order to develop a relationship between input and output parameters, they utilized a feed-forward backpropagation neural network consisting of two hidden layers. The results showed that the developed models have a maximum absolute mean relative error of 7% [ 36 ]. Karthik et al incorporated the CCDOE to study the effect of spindle speed, feed rate, and depth of cut on surface roughness AISI 316 under face milling operation.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Grinding Parameters of Tool Steel by the Soft Computing Technique

Hatami

¹

,

Sayadi

²

,

Razbin

³

et al. 2022

Computational Intelligence and Neuroscience

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Grinding is one of the most complex and accurate machining processes, and the efficiency of the grinding wheel depends significantly on its surface properties. This work aims to propose an algorithmic manner that reduces the cost and time to conduct grinding of an optimized DIN 1.2080 tool steel (SPK) using a soft computing technique to obtain the best combination of input parameters including depth of cut (20, 40, 60 μ m ), wheel speed (15, 20, 25 m / s ), feed rate (100, 300, 500 m m / s ), and incidence angle (0, 30, 45 d e g r e e s ) with respect to output parameters consisting of average surface roughness and specific grinding energy. According to the input parameters and their levels, an experiment using fractional factorial design of experiment (RFDOE) was designed. Later on, two parallel feed-forward backpropagation (FFBPNN) networks with similar topology made up of 4, 11, and 1 units in their input, hidden, and output layers are trained, respectively. After sensitivity analyses of networks for determination of the relative importance of input variables, a genetic algorithm (GA) adopting linear programming (LP) based on Euclidean distance is coupled to networks to seek out the best combinations of input parameters that result in minimum average surface roughness and minimum specific grinding energy. The findings revealed that RFDOE provides valid data for training FFBP networks with a total goodness value of more than 1.99 in both cases. The sensitivity analyses showed that feed rate (38.97%) and incidence angle (33.94%) contribute the most in the case of average surface roughness and specific grinding energy networks, respectively. Despite the similar surface quality based on scanning electron microscopy (SEM), the optimization resulted in an optimized condition of the depth of cut of 25.23 μ m , wheel speed of 15.02 m m / s , feed rate of 369.45 m m / s , and incidence angle of 44.98 d e g r e e s , which had a lower cost value (0.0146) than the optimum one (0.0953). Thus, this study highlights that RFDOE with a hybrid optimization using FFBP networks-GA/LP can effectively minimize both average surface roughness and specific grinding energy of SPK.

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“…Around 410 research articles were reported on experimental work on different spindle speed and feed rate on google scholar and 153 out of it were published in science directly. (Susac and Stan 2020) in this study an experimental investigation on the aluminum 6061 to optimize of the circularity,cylindricity and the surface roughness of the drilling done with different spindle speed and feed rate.and observe at which speed the surface roughness and cylindricity tolerance are optimize with the nearest values. (Zaidi 2016) this research is about knowing the physical properties of the aluminum 6061chips using the hot extrusion process.how the metal chips are removed during the drilling process.based on the spindle speed.…”

Section: Introductionmentioning

confidence: 95%

A Novel Optimization of Feed and Spindle Speed Levels for Surface Roughness and Cylindricity Tolerance of the Drilled Holes in Liquid Stir Casted Al6061 Hybrid Metal Matrix Composite Reinforced with 15% Weight Fraction of B4C+Fly ash and Comparing with th

2022

pnr

0

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Aim: Our main aim of the research is to Optimization of feed and spindle speed levels for surface roughness and cylindricity tolerance of the drilled holes in liquid stir casted Al6061 hybrid metal matrix composite reinforced with 15% weight fraction of B4C+Fly ash and comparing with that of as-cast Al6061. Materials and Methods: In this study, Al6061 aluminum casting plate with a thickness of 10 mm was used. Al6061 aluminum was normalized at 750 degrees Celsius using a muffle furnace.and optimized with different spindle speed and feed rate. And taking the surface roughness and cylindricity tolerance. .Test samples (N = 20) were taken from each plate of experimental group ( B4C,Fly ash) and control group (Al6061) G-Power:82%. Results: To measure the surface roughness and cylindricity tolerance of the drilled holes at different spindle speed and various feed rate and to optimize with the output values.with insignificance value of 0.4485 (p>0.05). Conclusion: The surface roughness and cylindricity tolerance tolerance will be compared between the control and experimental groups based on spindle speed and feed rate, as well as knowing the material properties.

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“…A three-layer ANN model with a sigmoid transfer function with back-propagation algorithm between the input and the hidden layer and a linear transfer function between the hidden and the output layer was used in this study [5,20,22,23,42,43]. The architecture of the ANN model used to predict the flow length is shown in Figure 11 The input layer has four neurons corresponding to the design variables, such as melt temperature, spiral thickness, injection pressure, and mold temperature, whereas the output layer have two nodes, i.e., flow length and spiral weight.…”

Section: Architecture and Training Of The Ann Modelmentioning

confidence: 99%

Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network

Sandu¹,

Susac²,

Stan³

et al. 2020

Self Cite

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In this study, computer-aided engineering (CAE) simulation software and the design of experiments (DOE) method were used to simulate the injection molding process in terms of the melt flow length, using a spiral part. Process parameters such as melt temperature, mold temperature, injection pressure and mold cavity thickness were considered as injection molding variables. A predictive model for the flow length was created using a three-layer artificial neural network (ANN). The ANN model was trained with both simulation and experimental data, and the predictive performances were compared in terms of correlation coefficient, root mean square error and mean relative error. The cavity thickness and melt temperature were found to be the most significant factors for both the simulation and the experiment, while the injection pressure and the mold temperature had little effect on the flow length. The ANN model trained with Moldex3D data shows a significantly higher prediction capacity than the ANN model trained with experimental data. However, the melt flow lengths predicted by the ANN model for both Moldex3D and Moldflow simulation data are statistically significant, indicating that the proposed prediction methodology, which combines the ANN model, DOE method and the CAE simulation technology, can effectively predict the flow length of injection molded parts, with a small number of data.

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Experimental Investigation, Modeling and Optimization of Circularity, Cylindricity and Surface Roughness in Drilling of PMMA Using ANN and ANOVA

Cited by 14 publications

References 15 publications

Optimization of Grinding Parameters of Tool Steel by the Soft Computing Technique

Optimization of Grinding Parameters of Tool Steel by the Soft Computing Technique

A Novel Optimization of Feed and Spindle Speed Levels for Surface Roughness and Cylindricity Tolerance of the Drilled Holes in Liquid Stir Casted Al6061 Hybrid Metal Matrix Composite Reinforced with 15% Weight Fraction of B4C+Fly ash and Comparing with th

Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network

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