Successful fuel filler tube hydroforming largely depends on proper loading paths, that is, application of internal pressure and axial feeding during the forming time duration. Generally, two part quality criteria are considered in selecting the feasible loading paths: (a) minimum part wall thinning and (b) part wrinkle free. Due to the highly nonlinear nature of the tube hydroforming process, iterative finite element analyses with adjustments based on forming experience are typically conducted to design the loading paths. In this research, genetic algorithm was integrated into the finite element analysis–based optimization, resulting in enhanced determination of the feasible loading paths. Genetic algorithm is a heuristic search based on mechanics of natural selection. A pair of pressure and axial feeding profiles was represented by connecting genes making up to be a chromosome. In each search, mutation and crossover operations generated a new generation of chromosomes. Fitness functions were formulated to assess performance of the chromosomes reflecting the part quality. Generations after generations, the optimal chromosomes are found only when the evaluated fitness function value falls within a user-defined tolerance. Unlike the typical iterative finite element analysis approach, it was shown that the iterative finite element analysis augmented with genetic algorithm was able to determine feasible pressure and axial feeding paths autonomously. The current approach still requires a lot of simulation runs, which must be offset by high-performance computing resources.

Chicken egg products increased by 60% worldwide resulting in the farmers or traders egg industry. The double yolk (DY) eggs are priced higher than single yolk (SY) eggs around 35% at the same size. Although, separating DY from SY will increase more revenue but it has to be replaced at the higher cost from skilled labor for sorting. Normally, the separation of double yolk eggs required the expertise person by weigh and shape of egg but it is still high error. The purpose of this research is to detect double-yolked (DY) chicken eggs with weight and ratio of the egg’s size using fuzzy logic and developing a low cost prototype to reduce the cost of separation. The K-means clustering is used for separating DY and SY, firstly. However, the error from this technique is still high as 15.05% because of its hard clustering. Therefore, the intersection zone scattering from using the weight and ratio of the egg’s size to input of DY and SY is taken into consider with fuzzy logic algorithm, to improve the error. The results of errors from fuzzy logic are depended with input membership functions (MF). This research selects triangular MF of weight as low = 65 g, medium = 75 g and high = 85 g, while ratio of the egg is triangular MF as low = 1.30, medium = 1.40 and high = 1.50. This algorithm is not provide the minimum total error but it gives the low error to detect a double yolk while the real egg is SY as 1.43% of total eggs. This algorithm is applied to develop a double yolk egg detection prototype with Mbed platform by a load cell and OpenMV CAM, to measure the weight and ratio of the egg respectively.

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Fused Deposition Modelling (FDM) has been extensively used in low-cost printers. However, the fundamental working principle (layered manufacturing) is resulted in the poor quality of the surface texture, the dimensional inaccuracy of fabricated parts, the limits its domain all issues often take place in precision industrial applications. In this paper, initially FDM based acrylonitrile butadiene styrene (ABS) model have been fabricated. In the post-processing stage, the vapor of acetone has been applied to the specimen. Then the changes in the surface finish and surface roughness have been investigated. The study highlighted that the post-processing of ABS specimen with acetone vapor treatment resulted in dramatic improvement of surface finish. Finally, parameter setting that gave the acceptable results while considering all the responses simultaneously.