MODELING AND MULTI-RESPONSE OPTIMIZATION OF ABRASIVE WATER JET MACHINING USING ANN COUPLED WITH NSGA-II

This paper aims to develop a predictive model and optimize the performance of the abrasive water jet machining (AWJM) during machining of carbon fiber-reinforced plastic (CFRP) epoxy laminates composite through a unique approach of artificial neural network (ANN) linked with the nondominated sorting genetic algorithm-II (NSGA-II). Initially, 80 AWJM experimental runs were carried out to generate the data set to train and test the ANN model. During the experimentation, the stand-off distance (SOD), water pressure, traverse speed and abrasive mass flow rate (AMFR) were selected as input AWJM variables and the average surface roughness and kerf width were considered as response variables. The established ANN model predicted the response variable with mean square error of 0.0027. Finally, the ANN coupled NSGA-II algorithm was applied to determine the optimum AWJM input parameters combinations based on multiple objectives.

Comparative Studies on the Behaviour of Flat Panels Made of GRP Under Static and Dynamic Loads

Glass reinforced plastic, so called GRP, is a composite material made of glass strands called fibbers woven together to create a flexible fabric. GRP is a lightweight material with many and diverse applications ranging from the manufacture of reservoirs for different liquids to the manufacture of boats, yachts, chairs and even children playground furniture. The behaviour of this material under static and dynamic loads is still raising interest from the scientific community and a large number of researchers. This continued interest is due to the material versatility for different applications depending on its manufacture process that has a significant weigh-in in the material mechanical properties. These resulting mechanical properties need to be carefully analysed and benchmarked prior to using the obtained material in commercial applications. The scope of this research study is to analyse the behaviour of glass reinforced plastic plate panel with reinforcements on one and two directions under static and dynamic loads employing both experimental and numerical methods for results validation. The methods used in this research study for the dynamic loads can also be applied successfully to other composite materials. Additionally, the stress plots have been analysed in iteration in order to ensure the most optimal reinforcement pattern.

Full-scale Test on Emergency Repair Pavement under Airplane Loading

In order to reveal structural response law of emergency repair pavement under the airplane loading and verify the backfill material and structural applicability, two craters (Crater 1 composed of 2.4 m thick flying objects (FO) + 0.4 m thick graded crushed rocks (GCR) + 0.2 m thick roller compacted concrete + fibre reinforced plastic (FRP) course, and Crater 2 composed of 2.4 m thick FO + 0.6 m thick GCR + FRP course) were backfilled. Static and dynamic loads were applied using two airplanes. Results show that, laying FRP pavement layers reduced the maximum deflection of Crater 2 by 21%. Crater 1 and concrete pavement were both slightly rigid structures with a strong load transfer ability. The dynamic deflection basin curves of Crater 2 could be fit using a Gaussian function; while the curves of Crater 1 and concrete pavement could be fit using a quartic polynomial. Under static loading, the earth pressures of Crater 2 at −0.6 m, −0.4 m, and −0.2 m sites were 4.3, 9, and 9.6 times of those of Crater 1, respectively. At the −0.2 m site, the earth pressure of Crater 1 was 0.11 MPa, while that of Crater 2 reached 1.06 MPa. The research results can guide the rapid quality inspection and optimization design of emergency repair pavement structure and material.

Sensitivity Analysis of GFRP Composite Drilling Parameters and Genetic Algorithm-Based Optimisation

In this article, a genetic algorithm (GA) is used for optimizing a metamodel of surface roughness (R_a ) in drilling glass-fibre reinforced plastic (GFRP) composites. A response surface methodology (RSM) based three levels (-1, 0, 1) design of experiments is used for developing the metamodel. Analysis of variance (ANOVA) is undertaken to determine the importance of each process parameter in the developed metamodel. Subsequently, after detailed metamodel adequacy checks, the insignificant terms are dropped to make the established metamodel more rigorous and make accurate predictions. A sensitivity analysis of the independent variables on the output response helps in determining the most influential parameters. It is observed that f is the most crucial parameter, followed by the t and D. The optimization results depict that the R_a increases as the f increases and a minor value of drill diameter is the most appropriate to attain minimum surface roughness. Finally, a robustness test of the predicted GA solution is carried out.

Designing a Robot for Manufacturing Fiberglass Reinforced Plastic (FRP) Molded Grating

The use of robotics in the industrial environment has, in general, very similar goals. Because of productivity requirements, or due to reliability, industries have been constantly equipping their floor with robots. In that sense, the chapter observed—in a fiberglass company—the chance of using a robot to execute a boring and repetitive task. The task mentioned is, actually, the manufacturing of fiberglass reinforced plastic (FRP) molded grating. To confirm the possibility of using a robot to this job, a cost and time analysis was made about the whole molded gratings manufacturing process. Afterward, research about robotics was taken in parallel with the conception of the robot (named “roving-robot”). Calculations were made to the mechanical project of the robot. Applying computer-aided design (CAD), technical drawing and bill of materials were generated to permit the robot assembling. All of these project steps are presented in this chapter.