The single weld bead geometry has critical effects on the layer thickness, surface quality, and dimensional accuracy of metallic parts in layered deposition process. The present study highlights application of a neural network and a second-order regression analysis for predicting bead geometry in robotic gas metal arc welding for rapid manufacturing. A series of experiments were carried out by applying a central composite rotatable design. The results demonstrate that not only the proposed models can predict the bead width and height with reasonable accuracy, but also the neural network model has a better performance than the second-order regression model due to its great capacity of approximating any nonlinear processes. The neural network model can efficiently be used to predict the desired bead geometry with high precision for the adaptive slicing principle in layer additive manufacturing.

Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis

http://www.ysxbcn.com/down/2014/09_en/09-p2805.pdf

Optimization of machining parameters in turning of Al−SiC−Gr hybrid metal matrix composites using grey-fuzzy algorithm

In this paper, an intelligent system to determine welding parameters for each pass and welding position in pipeline welding based on one database and FEM model, two BP neural network models and a C-NN model was developed and validated. The preliminary test of the system has indicated that the developed system could determine welding parameters for pipeline welding quickly, from which good weldments can be produced without experienced welding personnel. Experiments using the predicted welding parameters from the developed system proved the feasibility of interface standards and intelligent control technology to increase productivity, improve quality, and reduce the cost of system integration.

Prediction of Welding Parameters for Pipeline Welding Using an Intelligent System

A multi criteria decision making approach for process improvement in friction stir welding of aluminium alloy

In robotic welding, the planning and generation of the motion of the welding torch are crucial in welding complex joints. This paper presents an approach for optimal robot path planning of the centroid pass in welding a Y-joint. Relevant data associated with the welding passes is defined and managed to facilitate robot path planning and generation. With properly selected welding torch inclination angles and welding pass segmentation techniques, a solution space containing a number of robot paths is generated to account for functional redundancy and different welding robot configurations. A solution with minimum joint movement is determined using a beam search algorithm as the optimal robot path for each welding pass segment. A case study of robot path planning of the centroid pass of a scaled-down Y-joint is simulated and discussed.

Robot path planning optimization for welding complex joints

The objective of this paper is to present an integrated approach using the Taguchi method (TM), grey relational analysis (GRA) and a neural network (NN) to optimize the weld bead geometry in a novel gas metal arc (GMA) welding process. The TM is first used to construct a database for the NN. The GRA is adopted to solve the problem of multiple performance characteristics in a GMA welding process using activating flux. The grey relational grade obtained from the GRA is used as the output of the back-propagation (BP) NN. Then, a NN with the Levenberg-Marquardt BP (LMBP) algorithm is used to provide the nonlinear relationship between welding parameters and grey relational grade of each weldment. The optimal parameters of the novel GMA welding process were determined by simulating parameters using a well-trained BPNN model. Experimental results illustrate the proposed approach.

The use of the Taguchi method with grey relational analysis and a neural network to optimize a novel GMA welding process

This work describes an application of an integrated approach using the Taguchi method (TM), neural network (NN) and genetic algorithm (GA) for optimizing the lap joint quality of aluminum pipe and flange in automotive industry. The proposed approach (Taguchi-Neural-Genetic approach) consists of two phases. In first phase, the TM was adopted to collect training data samples for the NN. In second phase, a NN with a Levenberg-Marquardt back-propagation (LMBP) algorithm was adopted to develop the relationship between factors and the response. Then, a GA based on a well-trained NN model was applied to determine the optimal factor settings. Experimental results illustrated the Taguchi-Neural-Genetic approach.

Hsuan-Liang Lin

Papers

The use of the Taguchi method with grey relational analysis and a neural network to optimize a novel GMA welding process

Optimizing the auto-brazing process quality of aluminum pipe and flange via a Taguchi-Neural-Genetic approach

Effects of plasma-MIG hybrid welding parameters on performance and hot cracking susceptibility of 5083 Al alloy butt-joint welds

Multi-Response Optimization and Investigations of Al-Steel Lap-Joint Performance Using a Novel MIG Weld-Brazing Technique