scholarly journals Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network

Coatings ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1402
Author(s):  
Yutao Li ◽  
Kaiming Wang ◽  
Hanguang Fu ◽  
Xiaohui Zhi ◽  
Xingye Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Dilution Rate ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Orthogonal Experiment ◽  
Scanning Speed ◽  
Good Prediction ◽  
Optimal Process ◽  
Orthogonal Experiments ◽  
Fcc Phase

The dilution rate has a significant impact on the composition and microstructure of the coatings, and the dilution rate and process parameters have a complex coupling relationship. In this study, three process parameters, namely laser power, powder feeding rate, and scanning speed, were selected as variables to design the orthogonal experiment. The dilution rate and hardness data were obtained from AlCoCrFeNi coatings based on orthogonal experiments. Then, a BP neural network was used to establish a prediction model of the process parameters on the dilution rate. The established BP neural network exhibited good prediction of the dilution rate of AlCoCrFeNi coatings, and the average relative error between the predicted value and the experimental value was only 5.89%. Subsequently, the AlCoCrFeNi coating was fabricated with the optimal process parameters. The results show that the coating was well-formed without defects, such as cracks and pores. The microhardness of the AlCoCrFeNi coating prepared with the optimal process parameters was 521.6 HV0.3. The elements were uniformly distributed in the microstructure, and the grain size was about 20–60 μm. The microstructure of the AlCoCrFeNi coating was only composed of the BCC phase without the existence of the FCC phase and intermetallic compounds.

Research on Optimization of Laser Cladding Process Parameters Based on Orthogonal Experimental Method

2020 ◽  
Vol 866 ◽  
pp. 72-81
Author(s):  
Da Shu ◽  
Si Chao Dai ◽  
Ji Chao Sun ◽  
Feng Tao ◽  
Ping Xiao ◽  
...  
Keyword(s):  
Dilution Rate ◽  
Laser Cladding ◽  
Process Parameters ◽  
Orthogonal Experiment ◽  
Scanning Speed ◽  
Optimum Level ◽  
Scoring Method ◽  
Laser Cladding Process ◽  
Potential Applications ◽  
Orthogonal Experiment Method

The orthogonal experiment method is used in optimal design of laser cladding, such as laser power (P), scanning speed (SS), powder feeding rate (PFR) and shielding gas velocity (SGV) etc. Both the dilution rate and the aspect ratio are investigated by comprehensive scoring method, which transforms multi-index into single index. In view of the nonlinear characteristics of laser cladding process parameters, the optimum level of each factor based on interaction effect is obtained by analyzing binary tables. Finally, the relationship between the laser cladding process parameters and the two indexes (the dilution rate and the ratio of width to height of coating) is obtained. This method has potential applications for the further investigating on the laser cladding process rules.

The Process Parameters Modeling and Experimental Study Based on BP Neural Network for Laser Direct Rapid Forming Metal Parts

2010 ◽  
Vol 156-157 ◽  
pp. 737-741 ◽  
Author(s):  
Jian Bin Wang ◽  
Ji Shu Yin ◽  
Bing Huang Chen
Keyword(s):  
Neural Network ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Quantitative Relationship ◽  
Scanning Speed ◽  
Performance Measure ◽  
Sintering Process ◽  
Metal Parts ◽  
Relationship Model ◽  
Bp Neural Network Model

Discussed in detail using BP neural network to establish the quantitative relationship model between the process parameters and components density on the laser direct rapid forming (LDRF) metal parts, in which input of single-pass sintering model is: laser power (P), scanning speed (V ) and powder feeding rate (G), performance indicators to measure the width of the sintered layer (W) and height (H); input of multi-pass multi-sintering model is: P、V、G、scan spacing (D) and layer thick ( ), the performance measure for the density of sintered parts，And neural network simulation results and experimental results are analyzed and compared. The results show that using BP neural network model can quantitative analyze the effect on sintering process parameters and the sintering performance, the model for the optimization of LDRF process parameters has built the foundation.

Accurate prediction of the extrusion forming bonding reliability for heterogeneous welded sheets based on GA-BP neural network

2021 ◽  
Author(s):  
Lei Gao ◽  
Feng Li ◽  
Peng Da Huo ◽  
Chao Li ◽  
Jie Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Bonding Strength ◽  
Prediction Accuracy ◽  
High Performance ◽  
Orthogonal Experiment ◽  
Optimization Method ◽  
Factors Affecting

Abstract As a widely recognized optimization method, BP neural network can provide scientific guidance for the formulation of reasonable process parameters. However, due to the randomness of its own weights and thresholds, the prediction accuracy remains to be further improved. The forming and manufacturing of heterogeneous welded sheet is a new extrusion connection method. There are many factors affecting the bonding quality, which brings trouble to the evaluation of bonding strength and quality. In this paper, orthogonal experiment, finite element simulation and process experiment were used to design and verify the key process parameters that affected the bonding strength of heterogeneous sheets. BP neural network and genetic algorithm neural network were used to predict the bonding strength. The results showed that the genetic algorithm neural network model has higher reliability, and the prediction accuracy was 99.5 %. Compared with the traditional BP neural network, the prediction accuracy was improved by 5.78 %, and the error was reduced to 0.5 %. It has good generalization ability, and provides a new way for intelligent reliability evaluation of high performance heterogeneous sheets extrusion manufacturing.

scholarly journals Optimization of SLS forming parameters in the dimensional accuracy of formed parts

2021 ◽  
Vol 233 ◽  
pp. 01069
Author(s):  
Hong ZHU ◽  
Gaoyan HOU
Keyword(s):  
Process Parameters ◽  
Selective Laser Sintering ◽  
Dimensional Accuracy ◽  
Scanning Speed ◽  
Axis Deviation ◽  
Reference Standard ◽  
Optimal Process ◽  
Molded Part ◽  
Formed Part ◽  
Powder Forming

In selective laser sintering powder forming, the performance and dimensional accuracy of the formed part are affected by the process parameters. Different materials have different process parameters, and there is still no reference standard for PA materials. To solve this problem, in response to this problem, PA2200 material was selected, and the influence of scanning interval and scanning speed on the dimensional accuracy of the formed part was analyzed. Through theoretical analysis and experiments, the optimal process parameters were obtained. The best combination of parameters is a scanning speed of 4000mm/s, a scanning interval of 0.5mm, and the size of the molded part has a X-axis deviation -0.35%, a Y-axis deviation -0.4%, and a Z-axis deviation -0.25%.

scholarly journals Springback Study on Profile Flexible 3D Stretch-Bending Process Using the Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Li ◽  
Ce Liang ◽  
Xiangfeng Lin ◽  
Jicai Liang ◽  
Zhongyi Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Material Properties ◽  
Simulation Analysis ◽  
Orthogonal Experiment ◽  
Range Analysis ◽  
Finite Element Software ◽  
Stretch Bending ◽  
Aluminum Profile ◽  
Bending Process

The springback is one of the main defects in the flexible 3D stretch-bending process. In this paper, according to the orthogonal design of experiments, the numerical simulation analysis of the springback for the 3D stretch-bending aluminum profile is carried out by the ABAQUS finite element software. And to investigate the effect of material properties on the springback, the range analysis of the orthogonal experiment is performed. The results show that these material properties of the aluminum profile (elastic modulus E, yield strength σy, and tangent modulus E1) might have the biggest influence on the springback of the aluminum profile, and the optimized forming parameters are founded as follows: the horizontal bending degree is 14°, the vertical bending degree is 14°, the number of multipoint stretch-bending dies is 10, the friction coefficient is 0.15, and aluminum alloy grade is 6063. Moreover, the model of the BP neural network for the prediction of the springback is established and trained based on the orthogonal experiment, and the results with the BP neural network model are in good agreement with experimental results. So it is obvious that the BP neural network could predict effectively the springback of 3D multipoint stretch-bending parts.

Studying on the Fiber Diameter of Polypropylene (PP) Spunbonding Fabric by Means of Artificial Neural Network Model and Physical Model

2010 ◽  
Vol 426-427 ◽  
pp. 356-360
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Good Prediction ◽  
Artificial Neural

In this work, the artificial neural network model and physical model are established and utilized for predicting the fiber diameter of polypropylene(PP) spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the physical model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

scholarly journals Preparation of electrospun nanofiber membrane for air filtration and process optimization based on BP neural network

2021 ◽  
Author(s):  
Le Kang ◽  
Yuankun Liu ◽  
Liping Wang ◽  
Xiaoping Gao
Keyword(s):  
Neural Network ◽  
Quality Factor ◽  
Flow Rate ◽  
Bp Neural Network ◽  
Polyvinylidene Fluoride ◽  
Orthogonal Experiment ◽  
Solution Concentration ◽  
Filtration Efficiency ◽  
Air Filtration ◽  
Filtration Resistance

Abstract The filtration layer in a medical protective mask can effectively prevent aerosol particles that might carry viruses from air. A nanofiber/microfiber composite membrane (NMCM) was successfully fabricated by electrospinning polyvinylidene fluoride (PVDF) nanofibers collected on the electrified and melt-blown polypropylene (PP) nonwovens, aiming to improve the filtration efficiency and reduce the resistance of respiration of mask. A four-factor and three-level orthogonal experiment was designed to study the effect of electrospinning parameters such as spinning solution concentration, voltage, tip-collect distance (TCD), and flow rate of solution on the filtration efficiency, resistance of respiration as well as quality factor of NMC developed to predict the resistance of respiration. Experimental results demonstrated that the filtration efficiency of NMCM≥95% in comparison to that of electrified and melt-blown PP nonwovens 79.38%, which increases by 19.68%. Additionally, the average resistance of respiration is 94.78 Pa, which meets the protection requirements. Multivariate analysis of variance indicated that the resistance of respiration of the NMCM has significantly dependent on the concentration, voltage, TCD, and flow rate of the spinning solution and the quality factor of the NMCM has dependent on the resistance of respiration. The air permeability ranges from 166.23 to 314.35mm/s, which is inversely proportional to the filtration resistance. As far as the filtration resistance is concerned, the optimal spinning parameters were obtained as follows. The concentration of spinning solution is 15%, the voltage is 27 kV, the TCD is 22 cm, and the flow rate is 2.5 mL/h. The relative error of the BP neural network varies from 0.49505% to 1.49217%, i.e. the error value varies from 0.17 to1.33 Pa. The predicted resistance of respiration corresponding to the optimal process is 68.1374 Pa.

Sign in / Sign up

Export Citation Format

Share Document