Process synthesis of laser forming by genetic algorithm

Cheng, Gary J.; Yao, Y. Lawrence

doi:10.1016/j.ijmachtools.2004.06.002

Cited by 35 publications

(16 citation statements)

References 10 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Liu et al [31] and Cheng and Yao [32] presented optimal approaches to obtain laser scanning paths and heating condition for laser forming of doubly curved shapes. Cheng and Yao [33] developed a process design methodology for sheet laser forming based on genetic algorithms and experimentally validated it through cases with circular and parabolic profiles that involve many decision variables. Moreover, the effect of scanning schemes on laser tube bending has been numerically simulated and experimentally validated by Li and Yao [34], Zhang et al [35], and Safdar et al [36].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Analysis of Low Output Power Laser Bending of Thin Steel Sheets

Stevens¹,

Celentano²,

Ramos‐Grez

et al. 2012

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

This work presents an experimental and numerical analysis of a low output power singlepass laser forming process applied to thin stainless steel sheets. To this end, the proposed methodology consists in four stages respectively devoted to material characterization via tensile testing, estimation of thermal boundary conditions present in laser forming, realization of laser bending tests for two sets of operating variables, and finally, numerical simulation of this process carried otit with a coupled thermomechanical finite element formulation accounting for large plastic strains, temperature-dependent material properties and convection-radiation phenomena. The numerical analysis, focused on the description of the evolution of the thermomechanical material response, is found to provide a satisfactory experimental validation of the final benditig angle for two laser forming cases with different operating variables. In both cases, the predicted high temperature gradients occurring across the sample thickness show that the deformation process is mainly governed by the thermal gradient mechanism.

show abstract

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Analysis of Low Output Power Laser Bending of Thin Steel Sheets

Stevens¹,

Celentano²,

Ramos‐Grez

et al. 2012

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The proposed method was found to be feasible and effective for producing some shapes. Cheng and Yao (2004) presented a process synthesis methodology for laser forming of a class of shapes based on genetic algorithm (GA). The effects of GA control parameters and the types of fitness function on the synthesis process were discussed.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system

2012

View full text Add to dashboard Cite

To apply laser forming process in reality, it is required to know the relationships between the deformed shape and scanning paths along with heating conditions. The deformation due to laser scanning depends on various factors, namely laser power, scan speed, spot diameter, scan position, number of scans, and many others. This article presents soft computing-based methods to predict deformations for a set of heating conditions, and also to determine the heating lines and heat conditions, in order to get a desired shape (i.e.

show abstract

“…GA has become popular due to its lesser tendency to get trapped in local minima and is generally expected to find a global solution because it works with a population instead of a single point as in traditional optimization methods. Researchers have already employed GA with a user-defined objective function for optimization of the process parameters in laser cutting [24], laser welding [25], laser forming [26], and in other advanced machining processes including ultrasonic machining (USM), abrasive jet machining (AJM), water jet machining (WJM) and abrasive-water jet machining (AWJM) [27]. Ghosal and Chaki [28] have employed an ANN-Quasi Newton hybrid model for estimation and optimization of depth of penetration in hybrid CO 2 laser-MIG welding.…”

Section: Introductionmentioning

confidence: 99%

A Ga–ann Hybrid Model for Prediction and Optimization of Co2 Laser-Mig Hybrid Welding Process

Chaki¹,

Ghosal²

2015

Int. J. Automot. Mech. Eng.

View full text Add to dashboard Cite

The paper presents a hybrid model of an Artificial Neural Network (ANN) and Genetic Algorithm (GA) for modeling of a hybrid laser welding process. This model is employed for the prediction and optimization of penetration depth with corresponding process parameters. A single program developed for the purpose initially establishes an optimized ANN architecture using a Back-Propagation Neural Network (BPNN), with Bayesian regularization (BR). This trained ANN is then used in conjunction with GA to find optimum parameters for the process. An experimental dataset obtained from published literature has been employed to study the effect of process input parameters (arc power, focal distance from the workpiece surface, torch angle and the distance between the laser and the welding torch.) in CO 2 laser-MIG hybrid welding on the depth of penetration for 5005 Al-Mg alloy, and has been used in the present work for the purpose of training, testing and optimization of the GA-ANN model. The results indicate that the GA-ANN model can predict the output with reasonably good accuracy (mean absolute % errors of 0.7198%) and can optimize the process parameters with a negligible computational time of 100.09 s. The proposed approach is envisaged for application in a multi-variable complex problem with reasonable accuracy for prediction and optimization of operational parameters. A 4-7-1 network trained using BPNN with the BR method has been found to show the best prediction capability and a maximum penetration depth of 3.84 mm has been obtained during optimization.

show abstract

Process synthesis of laser forming by genetic algorithm

Cited by 35 publications

References 10 publications

Experimental and Numerical Analysis of Low Output Power Laser Bending of Thin Steel Sheets

Experimental and Numerical Analysis of Low Output Power Laser Bending of Thin Steel Sheets

Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system

A Ga–ann Hybrid Model for Prediction and Optimization of Co2 Laser-Mig Hybrid Welding Process

Contact Info

Product

Resources

About