Abstract. Warm stamping techniques have been employed to solve the formability problem in forming aluminium alloy panels. The formability of sheet metal is a crucial measure of its ability for forming complex-shaped panel components and is often evaluated by forming limit diagram (FLD). Although the forming limit is a simple tool to predict the formability of material, determining FLD experimentally at warm/hot forming condition is quite difficult. This paper presents the artificial neural network (ANN) modelling process to predict FLDs based on some experimental results (different temperature, 20°C-300°C and different forming rates, 5-300 mm.s -1 ). It is shown that the ANN can be trained to predict the FLDs and there is a good agreement between experimental and neural network results
This paper tends to discuss the relation between welding parameters (Voltage, Arc Current and Travel Speed) and the deposited lines shape (width, height and penetration) in metal deposition process. Due to the high nonlinearity of the process ANN (artificial neural network) was found to be the best chose for representing it. ANN is trained off-line under different operating conditions then used for prediction of the system model for further optimization of the process. The results show the capability of the developed ANNs to represent the process properly. Supervised learning with back propagation technique was used to make the networks. Best network for width consisted of tansig functions with an output layer of linear function. Traingd function gave the best result for width. Best network for penetration consisted of tansig functions with an output layer of linear function. Traingd function gave the best result for penetration. Best network for height consisted of mixture of radial basis, tansig functions with an output layer of linear function. Traingd function gave the best result for height.
This chapter summarizes a PhD thesis introducing a methodology for optimizing robotic MIG (metal inert gas) to perform WAAM (wire and arc additive manufacturing) without using machines equipped with CMT (cold metal transfer) technology. It tries to find the optimal MIG parameters to make WAAM using a welding robot feasible production technique capable of making functional products with proper mechanical properties. Some experiments were performed first to collect data. Then NN (neural network) models were created to simulate the MIG process. Then different optimization techniques were used to find the optimal parameters to be used for deposition. These results were practically tested, and the best one was selected to be used in the third stage. In the third stage, a block of metal was deposited. Then samples were cut from deposited blocks in two directions and tested for tension stress. These samples were successful. They showed behavior close to base alloy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.