The use of teaching-learning based optimization technique for optimizing weld bead geometry as well as power consumption in additive manufacturing

2021 ◽  
Vol 279 ◽  
pp. 123891 ◽  
Author(s):  
K. Venkatarao
Keyword(s):  
Additive Manufacturing ◽  
Power Consumption ◽  
Optimization Technique ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Weld Bead Geometry Prediction of Additive Manufacturing Based on Neural Network

2018 ◽  
Author(s):  
Qiankun Xue ◽  
Shiyue Ma ◽  
Yaxin Liang ◽  
Jiaxin Wang ◽  
Yipu Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry

A modified version of MATLAB application window for predicting the weld bead profile and stress–strain plot of AA5052 CMT weldment using ER4043

SIMULATION ◽  
2021 ◽  
pp. 003754972110315
Author(s):  
B Girinath ◽  
N Siva Shanmugam
Keyword(s):  
Strain Curve ◽  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Hybrid Technique ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Weld Bead Geometry ◽  
Inference System ◽  
Engineering Stress

The present study deals with the extended version of our previous research work. In this article, for predicting the entire weld bead geometry and engineering stress–strain curve of the cold metal transfer (CMT) weldment, a MATLAB based application window (second version) is developed with certain modifications. In the first version, for predicting the entire weld bead geometry, apart from weld bead characteristics, x and y coordinates (24 from each) of the extracted points are considered. Finally, in the first version, 53 output values (five for weld bead characteristics and 48 for x and y coordinates) are predicted using both multiple regression analysis (MRA) and adaptive neuro fuzzy inference system (ANFIS) technique to get an idea related to the complete weld bead geometry without performing the actual welding process. The obtained weld bead shapes using both the techniques are compared with the experimentally obtained bead shapes. Based on the results obtained from the first version and the knowledge acquired from literature, the complete shape of weld bead obtained using ANFIS is in good agreement with the experimentally obtained weld bead shape. This motivated us to adopt a hybrid technique known as ANFIS (combined artificial neural network and fuzzy features) alone in this paper for predicting the weld bead shape and engineering stress–strain curve of the welded joint. In the present study, an attempt is made to evaluate the accuracy of the prediction when the number of trials is reduced to half and increasing the number of data points from the macrograph to twice. Complete weld bead geometry and the engineering stress–strain curves were predicted against the input welding parameters (welding current and welding speed), fed by the user in the MATLAB application window. Finally, the entire weld bead geometries were predicted by both the first and the second version are compared and validated with the experimentally obtained weld bead shapes. The similar procedure was followed for predicting the engineering stress–strain curve to compare with experimental outcomes.

Electric arc shape and weld bead geometry analysis under the electromagnetic constriction and expansion effect

2021 ◽  
Author(s):  
Miguel Guilherme Antonello ◽  
Alexandre Queiroz Bracarense ◽  
Régis Henrique Gonçalves e Silva ◽  
Ivan Olszanski Pigozzo ◽  
Marcelo Pompermaier Okuyama
Keyword(s):  
Electric Arc ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Arc Shape ◽  
Geometry Analysis ◽  
Expansion Effect

scholarly journals Evaluation of the Effect of the Water in the Contact Tip on Arc Stability and Weld Bead Geometry in Underwater Wet FCAW

2017 ◽  
Vol 22 (4) ◽  
pp. 401-412 ◽  
Author(s):  
Marcelo Teodoro Assunção ◽  
Alexandre Queiroz Bracarense
Keyword(s):  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Arc Stability

A Model for In-Process Control of Thermal Properties During Welding

1989 ◽  
Vol 111 (1) ◽  
pp. 40-50 ◽  
Author(s):  
C. C. Doumanidis ◽  
D. E. Hardt
Keyword(s):  
Process Control ◽  
Thermal Response ◽  
Weld Bead ◽  
Distributed Parameter ◽  
Bead Geometry ◽  
Actual Process ◽  
Weld Bead Geometry ◽  
Thermally Activated ◽  
Real Time Tracking ◽  
Welding Processes

The control of welding processes has received much attention in the past decade, with most attention placed on real-time tracking of weld seams. The actual process control has been investigated primarily in the context of weld bead geometry regulation, ignoring for the most part the metallurgical properties of the weld. This paper addresses the latter problem through development of a model for in-process control of thermally activated material properties of weld. In particular, a causal model relating accessible inputs to the outputs of weld bead area, heat affected zone width, and centerline cooling rate at a critical temperature is developed. Since the thermal system is a distributed parameter, nonlinear one, it is modelled numerically to provide a baseline of simulation information. Experiments are performed that measure the thermal response of actual weldments and are used to calibrate the simulation and then to verify the basic dynamics predicted. Simulation results are then used to derive a locally linear transfer function matrix relating inputs and outputs. These are shown to be nonstationary, depending strongly upon the operating point and the boundary conditions.

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