Artificial neural networks (ANN) were used to model the strength, ductility and hardness of multi-pass welds deposited by gas tungsten arc welding (GTAW) in plates of commercial titanium alloys. The input parameters o...Artificial neural networks (ANN) were used to model the strength, ductility and hardness of multi-pass welds deposited by gas tungsten arc welding (GTAW) in plates of commercial titanium alloys. The input parameters of the ANN are the alloy composition and heat treatment conditions and its output is one of the mechanical properties of the weld metal of titanium alloys, namely ultimate tensile strength (UTS), yield strength, elongation, reduction of the area (ROA) and hardness. The titanium alloys used in the work include commercially pure titanium, alpha or near-alpha titanium, alpha-beta titanium and beta or near-beta titanium.展开更多
In the face of the increased global campaign to minimize the emission of greenhouse gases and the need for sustainability in manufacturing, there is a great deal of research focusing on environmentally benign and rene...In the face of the increased global campaign to minimize the emission of greenhouse gases and the need for sustainability in manufacturing, there is a great deal of research focusing on environmentally benign and renewable materials as a substitute for synthetic and petroleum-based products. Natural fiber-reinforced polymeric composites have recently been proposed as a viable alternative to synthetic materials. The current work investigates the suitability of coconut fiber-reinforced polypropylene as a structural material. The coconut fiber-reinforced polypropylene composites were developed. Samples of coconut fiber/polypropylene (PP) composites were prepared using Fused Filament Fabrication (FFF). Tests were then conducted on the mechanical properties of the composites for different proportions of coconut fibers. The results obtained indicate that the composites loaded with 2 wt% exhibited the highest tensile and flexural strength, while the ones loaded with 3 wt% had the highest compression strength. The ultimate tensile and flexural strength at 2 wt% were determined to be 34.13 MPa and 70.47 MPa respectively. The compression strength at 3 wt% was found to be 37.88 MPa. Compared to pure polypropylene, the addition of coconut fibers increased the tensile, flexural, and compression strength of the composite. In the study, an artificial neural network model was proposed to predict the mechanical properties of polymeric composites based on the proportion of fibers. The model was found to predict data with high accuracy.展开更多
In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties ...In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).展开更多
Factors that affect weld mechanical properties of commercially pure titanium have been investigated using artificial neural networks. Input data were obtained from mechanical testing of single-pass, autogenous welds, ...Factors that affect weld mechanical properties of commercially pure titanium have been investigated using artificial neural networks. Input data were obtained from mechanical testing of single-pass, autogenous welds, and neural network models were used to predict the ultimate tensile strength, yield strength, elongation, reduction of area, Vickers hardness and Rockwell B hardness. The results show that both oxygen and nitrogen have the most significant effects on the strength while hydrogen has the least effect over the range investigated. Predictions of the mechanical properties are shown and agree well with those obtained using the 'oxygen equivalent' (OE) equations.展开更多
The uniaxial compressive strength(UCS)of intact rock is one of the most important parameters required and determined for rock mechanics studies in engineering projects.The limitations and difficulty of conducting test...The uniaxial compressive strength(UCS)of intact rock is one of the most important parameters required and determined for rock mechanics studies in engineering projects.The limitations and difficulty of conducting tests on rocks,specifically on thinly bedded,highly fractured,highly porous and weak rocks,as well as the fact that these tests are destructive,expensive and time-consuming,lead to development of soft computing-based techniques.Application of artificial neural networks(ANNs)for predicting UCS has become an attractive alternative for geotechnical engineering scientists.In this study,an ANN was designed with the aim of indirectly predicting UCS through the serpentinization percentage,and physical,dynamic and mechanical characteristics of serpentinites.For this purpose,data obtained in earlier experimental work from central Greece were used.The ANN-based results were compared with the experimental ones and those obtained from previous analysis.The proposed ANN-based formula was found to be very efficient in predicting UCS values and the samples could be classified with simple physical,dynamic and mechanical tests,thus the expensive,difficult,time-consuming and destructive mechanical tests could be avoided.展开更多
Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research.This study aims to investigate the implicit relationship between the compositions and mechanical pro...Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research.This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-Al alloys.Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-Al alloys,a momentum back-propagation(BP)neural network with a single hidden layer was established.Particle swarm optimization(PSO)was applied to optimize the BP model.In the neural network,the input variables were the contents of Mg,Li and Al,and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-Al alloy's composition and its mechanical properties.It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model.The prediction of the influence of Al addition on the mechanical properties of as-cast Mg-Li-Al alloys is consistent with the related research results.展开更多
50CrVA cold-rolled spring steel strip was used to fabricate the diaphragm of the automotive horn. The material parameters which were taken into account in the design of the dia-phragm involve elongation, elastic limit...50CrVA cold-rolled spring steel strip was used to fabricate the diaphragm of the automotive horn. The material parameters which were taken into account in the design of the dia-phragm involve elongation, elastic limit, Young's modulus, yield strength and tensile strength. The tempering process was carried out in order to enable the diaphragm to possess the excellent mechanical properties, such as high elastic limit, high fatigue strength and perfect stress relaxation resistance. As a nonlinear information processing system, the backpropagation artificial neural network (BPANN) was applied to predict and simulate the relationship between the mechanical properties of the diaphragm and the tempering process parameters. Experimental results show that a BPANN with 3-8-5 architec-ture is capable of predicting the relationship between the mechanical properties of the diaphragm and the tempering temperature successfully and lays the profound foundations for optimizing the design of the diaphragm. BPANN simulation results show that the tempering temperature ranging from 380 to 420 ℃ contributes to enhancing the comprehensive mechanical properties of the diaphragm including high Young's modulus, high elastic limit and high fatigue strength.展开更多
Aluminium alloy LY12CZ sheet without any pre-treatment has been used to study the influence of cavity damage on the mechanical properties of the superplastically-deformed materials room temper- ature. The experimenta...Aluminium alloy LY12CZ sheet without any pre-treatment has been used to study the influence of cavity damage on the mechanical properties of the superplastically-deformed materials room temper- ature. The experimental results show that: (1) the lower the rate of cavitation as the superplastic strain increases, the higher the superplasticity is; (2) the mechanical properties of superplastically- deformed materials at room temperature decrease as the superplastically-deformed strain increases, especially, a noteworthy decrease in elasticity modulus, yield strenth and ultimate strength at room temperature begins to appear as the level of cavitation by area comes up to about 4%, while at the same level the reduction in area drops down a very large quantity. Then, a three-layer back- propagation neural network has been developed to predict the machanichl properties of the superplasti- cally-deformed material.The results acquired from the neural network are very inspiring展开更多
Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure ...Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure on the density and porosity of the composites reinforced with 0, 5, 10, and 20 wt% B4C particles were studied. Moreover, an artificial neural network model has been developed for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy Al-Cu-Mg/B4Cp composites. This model can be used for predicting the densification behavior of Al-Cu-Mg/B4Cp composites produced under reinforcement of different sizes and amounts with various milling times and compact pressures. The mean absolute percentage error for the predicted values did not exceed 1.6%.展开更多
An improved neural network model was developed for prediction of mechanical properties in the de-sign and development of new types of magnesium alloys by refining the types of input variables and using a more reasonab...An improved neural network model was developed for prediction of mechanical properties in the de-sign and development of new types of magnesium alloys by refining the types of input variables and using a more reasonable algorithm. The results showed that the improved model apparently decreased the prediction errors, and raised the accuracy of the prediction results. Better preprocessing parame-ters were found to be [0.15, 0.90] for the tensile strength, [0.1, 0.9] for the yield strength, and [0.15, 0.90] for the elongation. When the above parameters were used, the relativity for predicition of strength was bigger than 0.95. By using improved ANN analysis, more reasonable process parameters and compo- sition could be obtained in some magnesium alloys without addition of strontoum.展开更多
A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone (HAZ) in X70 pipeline ...A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone (HAZ) in X70 pipeline steels. The mass percent of chemical compositions (i. e. carbon equivalent based upon the International Institute of Welding equation (CEIIw), the carbon equivalent based upon the chemical portion of the ho-Bessyo carbon equivalent equation (CEecm), the sum of the niobium, vanadium and titanium concentrations (CvTaNb), the sum of the niobium and vanadium concentrations (CNbv), the sum of the chromium, molybdenum, nickel and copper concentrations (CcrMoNiCu)), yield strength (YS) at 0. 005 offset, ultimate tensile strength (UTS) and percent elongation (El) were considered as input parameters to the network, while Vickers microhardness with 10 N load was considered as its output. For the purpose of constructing this model, 104 different data were gathered from the experimental re- sul.ts. Scatter diagrams and two statistical criteria, i.e. absolute fraction of variance (R2 ) and mean relative error (MRE), were used to evaluate the prediction performance of the developed model. The developed model can be fur- ther used in practical applications of alloy and thermo-mechanical schedule design in manufacturing process of pipe line steels.展开更多
文摘Artificial neural networks (ANN) were used to model the strength, ductility and hardness of multi-pass welds deposited by gas tungsten arc welding (GTAW) in plates of commercial titanium alloys. The input parameters of the ANN are the alloy composition and heat treatment conditions and its output is one of the mechanical properties of the weld metal of titanium alloys, namely ultimate tensile strength (UTS), yield strength, elongation, reduction of the area (ROA) and hardness. The titanium alloys used in the work include commercially pure titanium, alpha or near-alpha titanium, alpha-beta titanium and beta or near-beta titanium.
文摘In the face of the increased global campaign to minimize the emission of greenhouse gases and the need for sustainability in manufacturing, there is a great deal of research focusing on environmentally benign and renewable materials as a substitute for synthetic and petroleum-based products. Natural fiber-reinforced polymeric composites have recently been proposed as a viable alternative to synthetic materials. The current work investigates the suitability of coconut fiber-reinforced polypropylene as a structural material. The coconut fiber-reinforced polypropylene composites were developed. Samples of coconut fiber/polypropylene (PP) composites were prepared using Fused Filament Fabrication (FFF). Tests were then conducted on the mechanical properties of the composites for different proportions of coconut fibers. The results obtained indicate that the composites loaded with 2 wt% exhibited the highest tensile and flexural strength, while the ones loaded with 3 wt% had the highest compression strength. The ultimate tensile and flexural strength at 2 wt% were determined to be 34.13 MPa and 70.47 MPa respectively. The compression strength at 3 wt% was found to be 37.88 MPa. Compared to pure polypropylene, the addition of coconut fibers increased the tensile, flexural, and compression strength of the composite. In the study, an artificial neural network model was proposed to predict the mechanical properties of polymeric composites based on the proportion of fibers. The model was found to predict data with high accuracy.
文摘In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).
基金This work is supported by the Scientific Research Foun-dation for the Returned Overseas Chinese Scholars,Ministry of Education,China
文摘Factors that affect weld mechanical properties of commercially pure titanium have been investigated using artificial neural networks. Input data were obtained from mechanical testing of single-pass, autogenous welds, and neural network models were used to predict the ultimate tensile strength, yield strength, elongation, reduction of area, Vickers hardness and Rockwell B hardness. The results show that both oxygen and nitrogen have the most significant effects on the strength while hydrogen has the least effect over the range investigated. Predictions of the mechanical properties are shown and agree well with those obtained using the 'oxygen equivalent' (OE) equations.
文摘The uniaxial compressive strength(UCS)of intact rock is one of the most important parameters required and determined for rock mechanics studies in engineering projects.The limitations and difficulty of conducting tests on rocks,specifically on thinly bedded,highly fractured,highly porous and weak rocks,as well as the fact that these tests are destructive,expensive and time-consuming,lead to development of soft computing-based techniques.Application of artificial neural networks(ANNs)for predicting UCS has become an attractive alternative for geotechnical engineering scientists.In this study,an ANN was designed with the aim of indirectly predicting UCS through the serpentinization percentage,and physical,dynamic and mechanical characteristics of serpentinites.For this purpose,data obtained in earlier experimental work from central Greece were used.The ANN-based results were compared with the experimental ones and those obtained from previous analysis.The proposed ANN-based formula was found to be very efficient in predicting UCS values and the samples could be classified with simple physical,dynamic and mechanical tests,thus the expensive,difficult,time-consuming and destructive mechanical tests could be avoided.
基金supported by the Program of New Century Excellent Talents of the Ministry of Education of China(NCET-08-0080)the National High Technology Research and Development Program("863"Program)of China(2009AA03Z525)+1 种基金the Fundamental Research Funds for the Central Universities(DUT11ZD115)the Science and Technology Fund of Dalian City(2009J21DW003)
文摘Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research.This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-Al alloys.Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-Al alloys,a momentum back-propagation(BP)neural network with a single hidden layer was established.Particle swarm optimization(PSO)was applied to optimize the BP model.In the neural network,the input variables were the contents of Mg,Li and Al,and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-Al alloy's composition and its mechanical properties.It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model.The prediction of the influence of Al addition on the mechanical properties of as-cast Mg-Li-Al alloys is consistent with the related research results.
基金Funded by the Research Foundation of Harbin Engineering University (No.HEUFT06040)
文摘50CrVA cold-rolled spring steel strip was used to fabricate the diaphragm of the automotive horn. The material parameters which were taken into account in the design of the dia-phragm involve elongation, elastic limit, Young's modulus, yield strength and tensile strength. The tempering process was carried out in order to enable the diaphragm to possess the excellent mechanical properties, such as high elastic limit, high fatigue strength and perfect stress relaxation resistance. As a nonlinear information processing system, the backpropagation artificial neural network (BPANN) was applied to predict and simulate the relationship between the mechanical properties of the diaphragm and the tempering process parameters. Experimental results show that a BPANN with 3-8-5 architec-ture is capable of predicting the relationship between the mechanical properties of the diaphragm and the tempering temperature successfully and lays the profound foundations for optimizing the design of the diaphragm. BPANN simulation results show that the tempering temperature ranging from 380 to 420 ℃ contributes to enhancing the comprehensive mechanical properties of the diaphragm including high Young's modulus, high elastic limit and high fatigue strength.
文摘Aluminium alloy LY12CZ sheet without any pre-treatment has been used to study the influence of cavity damage on the mechanical properties of the superplastically-deformed materials room temper- ature. The experimental results show that: (1) the lower the rate of cavitation as the superplastic strain increases, the higher the superplasticity is; (2) the mechanical properties of superplastically- deformed materials at room temperature decrease as the superplastically-deformed strain increases, especially, a noteworthy decrease in elasticity modulus, yield strenth and ultimate strength at room temperature begins to appear as the level of cavitation by area comes up to about 4%, while at the same level the reduction in area drops down a very large quantity. Then, a three-layer back- propagation neural network has been developed to predict the machanichl properties of the superplasti- cally-deformed material.The results acquired from the neural network are very inspiring
基金the Karadeniz Technical University Research Fund for financially supporting this research (No: 2010.112.010.4)
文摘Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure on the density and porosity of the composites reinforced with 0, 5, 10, and 20 wt% B4C particles were studied. Moreover, an artificial neural network model has been developed for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy Al-Cu-Mg/B4Cp composites. This model can be used for predicting the densification behavior of Al-Cu-Mg/B4Cp composites produced under reinforcement of different sizes and amounts with various milling times and compact pressures. The mean absolute percentage error for the predicted values did not exceed 1.6%.
基金Supported by the National Natural Science Foundation of China (Grant No. 50725413)the National Basic Research Program of China ("973" Project) (Grant No. 2007CB613704)the National Key Technologies R&D Program of China (Grant No. 2006BAE04B09-7)
文摘An improved neural network model was developed for prediction of mechanical properties in the de-sign and development of new types of magnesium alloys by refining the types of input variables and using a more reasonable algorithm. The results showed that the improved model apparently decreased the prediction errors, and raised the accuracy of the prediction results. Better preprocessing parame-ters were found to be [0.15, 0.90] for the tensile strength, [0.1, 0.9] for the yield strength, and [0.15, 0.90] for the elongation. When the above parameters were used, the relativity for predicition of strength was bigger than 0.95. By using improved ANN analysis, more reasonable process parameters and compo- sition could be obtained in some magnesium alloys without addition of strontoum.
文摘A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone (HAZ) in X70 pipeline steels. The mass percent of chemical compositions (i. e. carbon equivalent based upon the International Institute of Welding equation (CEIIw), the carbon equivalent based upon the chemical portion of the ho-Bessyo carbon equivalent equation (CEecm), the sum of the niobium, vanadium and titanium concentrations (CvTaNb), the sum of the niobium and vanadium concentrations (CNbv), the sum of the chromium, molybdenum, nickel and copper concentrations (CcrMoNiCu)), yield strength (YS) at 0. 005 offset, ultimate tensile strength (UTS) and percent elongation (El) were considered as input parameters to the network, while Vickers microhardness with 10 N load was considered as its output. For the purpose of constructing this model, 104 different data were gathered from the experimental re- sul.ts. Scatter diagrams and two statistical criteria, i.e. absolute fraction of variance (R2 ) and mean relative error (MRE), were used to evaluate the prediction performance of the developed model. The developed model can be fur- ther used in practical applications of alloy and thermo-mechanical schedule design in manufacturing process of pipe line steels.