The design, fabrication, and testing of an epoxy-glass reinforced polymer composite pressure vessel suitable for high-pressure gas storage have been reported. In this study composite pressure vessels are made up of aluminum alloy 6063 seamless liner and glass/epoxy composite reinforcement. The aluminum liner, which is developed by extruded aluminum tube of internal diameter 141 mm and wall thickness 4 mm, is subjected to super plastic deformation. Continuous glass fibers impregnated in epoxy resin are wound on the seamless liner by filament winding process. In this work, 10 pressure vessels were manufactured for 3.5 MPa service pressure with marginal safety of 3. Four pressure vessels were subjected to cyclic and burst test; the burst pressures were 10.9, 11.0, 11.0, and 13.0 MPa.KEY WORDS: glass fiber, epoxy, super plastic deformation, design of composite pressure vessels, burst test, filament winding.

Nowadays, to reach progressive growth although being competitive in the market, the manufacturing industries are using advanced technologies such as cloud computing, the Internet of things (IoT), artificial intelligence, 3D printer, nanotechnology, cryogenics, robotics, and automation in smart manufacturing sectors. One such subclass of artificial intelligence is machine learning, which uses a computer system for making predictions and performing definite tasks without any use of specific instructions to enhance the quality of the product, and rate of production, and to optimize the processes and parameters in machining operations. A broad category of manufacturing that is technology-driven utilizes internet-connected machines to monitor the performances of manufacturing processes referring as smart manufacturing. The current paper presents a comprehensive survey and summary of different machine learning algorithms which are being employed in various traditional and nontraditional machining processes, and also, an outlook of the manufacturing paradigm is presented. Subsequently, future directions in the machining industry were proposed based on trends and challenges that are accompanying machine learning.

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Roughness is a prime parameter in any process/operation as it aids in confirming the quality status of the product. The insert and workpiece would develop a lot of friction and as a result, it generates heat in the cutting zone, which affects the machined surface. The speed, feed, and depth of cut were chosen as processing factors. L27 Orthogonal array is used based on the Taguchi technique. The regression analysis is used to develop an equation to predict the roughness. The impact of the processing factors on the machined surface is studied with help of ANOVA (Analysis of Variance). Furthermore, the estimation of surface roughness is carried out using a machine learning-based model-feed forward (nonlinear autoregressive network) NARX network, and the evaluated surface roughness is compared with the values predicted by the regression model and experimental results. The average percentage error observed with the predicted values by NARX is observed as 3.01%, which is lower than the average percentage error observed by the regression model 5.131%. Thus, this work provides the best machine learning approach to the prognosis of the roughness in dry turning of Inconel 625, which would save a lot of time and unnecessary wastage of the work material.

In this paper, the influence of process components on surface roughness in turning of Inconel 625 using cubic boron nitride (CBN) is studied. A predictive model is developed to forecast the surface roughness using the cascade forward neural network (CFNN). The experiments are designed based on Taguchi. L27 orthogonal array (OA) is used to perform the experimental trails by considering speed, feed, and depth of cut as input factors. Out of 27 experimental trails, 18 experiments are used for training and 9 experimental trails are used for testing. The developed predictive model by the CFNN is compared with regression model values. The average prediction error for surface roughness is 2.94% with R2 = 99.99% by the CFNN. The CFNN is known to be superior to predict the response with minimum of percentage error. The minimum and maximum roughness observed at trail 8 and trail 20 is noted, respectively, and the increases in roughness at experimental trail 8 is equal to 3.384 times higher than the roughness observed at experimental trail.20. The feed rate dominates effectively on the roughness rather than other factors. The consequences of process factors on surface roughness are studied with the help of ANOVA. This experimental study and developed model would be used for aero parts manufacturing to forecast the roughness accurately before to the actual experiment so that actual machining and material cost could be avoided.