The current work explored additive friction stir deposition of AZ31B magnesium alloy with the aid of MELD?technology.AZ31B magnesium bar stock was fed through a hollow friction stir tool rotating at constant velocity ...The current work explored additive friction stir deposition of AZ31B magnesium alloy with the aid of MELD?technology.AZ31B magnesium bar stock was fed through a hollow friction stir tool rotating at constant velocity of 400 rpm and translating at linear velocity varied from 4.2 to 6.3 mm/s.A single wall consisting of five layers with each layer of 140×40×1 mm^(3)dimensions was deposited under each processing condition.Microstructure,phase,and crystallographic texture evolutions as a function of additive friction stir deposition parameters were studied with the aid of scanning electron microscopy including electron back scatter diffraction and X-ray diffraction.Both feed material and additively produced samples consisted of theα-Mg phase.The additively produced samples exhibited a refined grain structure compared to the feed material.The feed material appeared to have a weak basal texture,while the additively produced samples experienced a strengthening of this basal texture.The additively produced samples showed a marginally higher hardness compared to the feed material.The current work provided a pathway for solid state additive manufacturing of Mg suitable for structural applications such as automotive components and consumable biomedical implants.展开更多
Additive friction stir deposition(AFSD)is a novel structural repair and manufacturing technology has become a research hotspot at home and abroad in the past five years.In this work,the microstructural evolution and m...Additive friction stir deposition(AFSD)is a novel structural repair and manufacturing technology has become a research hotspot at home and abroad in the past five years.In this work,the microstructural evolution and mechanical performance of the Al-Mg-Si alloy plate repaired by the preheating-assisted AFSD process were investigated.To evaluate the tool rotation speed and substrate preheating for repair quality,the AFSD technique was used to additively repair 5 mm depth blind holes on 6061 aluminum alloy substrates.The results showed that preheat-assisted AFSD repair significantly improved joint bonding and joint strength compared to the control non-preheat substrate condition.Moreover,increasing rotation speed was also beneficial to improve the metallurgical bonding of the interface and avoid volume defects.Under preheating conditions,the UTS and elongation were positively correlated with rotation speed.Under the process parameters of preheated substrate and tool rotation speed of 1000 r/min,defect-free specimens could be obtained accompanied by tensile fracture occurring in the substrate rather than the repaired zone.The UTS and elongation reached the maximum values of 164.2MPa and 13.4%,which are equivalent to 99.4%and 140%of the heated substrate,respectively.展开更多
Additive friction stir deposition(AFSD)provides strong flexibility and better performance in component design,which is controlled by the process parameters.It is an essential and difficult task to tune those parameter...Additive friction stir deposition(AFSD)provides strong flexibility and better performance in component design,which is controlled by the process parameters.It is an essential and difficult task to tune those parameters.The recent exploration of machine learning(ML)exhibits great potential to obtain a suitable balance between productivity and set parameters.In this study,ML techniques,including support vector machine(SVM),random forest(RF)and artificial neural network(ANN),are applied to predict the mechanical properties of the AFSD-based AA6061 deposition.Expect for the stable parameters(temperature,force and torque)in situ monitored by the self-developed process-aware kit during the AFSD process and the other factors(rotation speed,traverse speed,feed rate and layer thickness)are also set as input variables.The output variables are microhardness and ultimate tensile strength(UTS).Prediction results show that the ANN model performs the best prediction accuracy with the highest R2(0.9998)and the lowest mean absolute error(MAE,0.0050)and root mean square error(RMSE,0.0063).Furthermore,analysis suggests that the feed rate(24.8%/24.1%)and layer thickness(25.6%/26.6%)indicate a higher contribution that affects the mechanical properties.展开更多
Additive manufacturing(AM)has the potential to transform manufacturing by enabling previously un-thinkable products,digital inventory and delivery,and distributed manufacturing.Here we presented an extrusion-based met...Additive manufacturing(AM)has the potential to transform manufacturing by enabling previously un-thinkable products,digital inventory and delivery,and distributed manufacturing.Here we presented an extrusion-based metal AM method(refer to“SoftTouch”depositionin thefiledpatent)thatis suitablefor making the metal feedstock flowable prior to the deposition through dynamic recrystallization induced grain refinement at elevated temperatures.The flowable metal was extruded out of the printer head like a paste for building dense metal parts with fine equiaxed grains and wrought mechanical properties.Off-the-shelf metal rods were used as feedstock and the printing process was completed in an open-air environment,avoiding pricy powders and costly inert or vacuum conditions.The resulting multi-layer de-posited 6061 aluminum alloys yield strength and ductility comparable to wrought 6061 aluminum alloys after the same T6 heat treatment.The extrusion-based metal AM method can also be advanced as green manufacturing technologies for fabricating novel alloys and composites,adding novel features to existing parts,repairing damaged metal parts,and welding advanced metals for supporting sustainable manufac-turing,in addition to being developed into a cost-effective manufacturing process for the fabrication of dense metal of complex structural forms.展开更多
基金the infrastructure and support of Center for Agile and Adaptive Additive Manufacturing(CAAAM)funded through State of Texas Appropriation:190405-105-805008-220。
文摘The current work explored additive friction stir deposition of AZ31B magnesium alloy with the aid of MELD?technology.AZ31B magnesium bar stock was fed through a hollow friction stir tool rotating at constant velocity of 400 rpm and translating at linear velocity varied from 4.2 to 6.3 mm/s.A single wall consisting of five layers with each layer of 140×40×1 mm^(3)dimensions was deposited under each processing condition.Microstructure,phase,and crystallographic texture evolutions as a function of additive friction stir deposition parameters were studied with the aid of scanning electron microscopy including electron back scatter diffraction and X-ray diffraction.Both feed material and additively produced samples consisted of theα-Mg phase.The additively produced samples exhibited a refined grain structure compared to the feed material.The feed material appeared to have a weak basal texture,while the additively produced samples experienced a strengthening of this basal texture.The additively produced samples showed a marginally higher hardness compared to the feed material.The current work provided a pathway for solid state additive manufacturing of Mg suitable for structural applications such as automotive components and consumable biomedical implants.
基金financially supported by Science and Technology Major Project of Changsha,China(No.kh2401034)the Fundamental Research Funds for the Central Universities of Central South University(No.CX20230182)the National Key Research and Development Project of China(No.2019YFA0709002)。
文摘Additive friction stir deposition(AFSD)is a novel structural repair and manufacturing technology has become a research hotspot at home and abroad in the past five years.In this work,the microstructural evolution and mechanical performance of the Al-Mg-Si alloy plate repaired by the preheating-assisted AFSD process were investigated.To evaluate the tool rotation speed and substrate preheating for repair quality,the AFSD technique was used to additively repair 5 mm depth blind holes on 6061 aluminum alloy substrates.The results showed that preheat-assisted AFSD repair significantly improved joint bonding and joint strength compared to the control non-preheat substrate condition.Moreover,increasing rotation speed was also beneficial to improve the metallurgical bonding of the interface and avoid volume defects.Under preheating conditions,the UTS and elongation were positively correlated with rotation speed.Under the process parameters of preheated substrate and tool rotation speed of 1000 r/min,defect-free specimens could be obtained accompanied by tensile fracture occurring in the substrate rather than the repaired zone.The UTS and elongation reached the maximum values of 164.2MPa and 13.4%,which are equivalent to 99.4%and 140%of the heated substrate,respectively.
基金the support from the Science and Technology Development Fund(FDCT)of Macao SAR(File/Project No.0015/2021/AFJ and 0110/2023/AMJ)Innovation Support Plan,Hong Kong,Macao and Taiwan science and technology cooperation project of Jiangsu Province(BZ2022047)the Joint Fund of Basic and Applied Basic Research Fund of Guangdong Province(No.2021B1515130009).
文摘Additive friction stir deposition(AFSD)provides strong flexibility and better performance in component design,which is controlled by the process parameters.It is an essential and difficult task to tune those parameters.The recent exploration of machine learning(ML)exhibits great potential to obtain a suitable balance between productivity and set parameters.In this study,ML techniques,including support vector machine(SVM),random forest(RF)and artificial neural network(ANN),are applied to predict the mechanical properties of the AFSD-based AA6061 deposition.Expect for the stable parameters(temperature,force and torque)in situ monitored by the self-developed process-aware kit during the AFSD process and the other factors(rotation speed,traverse speed,feed rate and layer thickness)are also set as input variables.The output variables are microhardness and ultimate tensile strength(UTS).Prediction results show that the ANN model performs the best prediction accuracy with the highest R2(0.9998)and the lowest mean absolute error(MAE,0.0050)and root mean square error(RMSE,0.0063).Furthermore,analysis suggests that the feed rate(24.8%/24.1%)and layer thickness(25.6%/26.6%)indicate a higher contribution that affects the mechanical properties.
基金This work was financially supported by the University of Michi-gan College of Engineering startup grant and FL and PD acknowl-edge the technical support from the Michigan Center for Materials Characterization(MC^(2)).
文摘Additive manufacturing(AM)has the potential to transform manufacturing by enabling previously un-thinkable products,digital inventory and delivery,and distributed manufacturing.Here we presented an extrusion-based metal AM method(refer to“SoftTouch”depositionin thefiledpatent)thatis suitablefor making the metal feedstock flowable prior to the deposition through dynamic recrystallization induced grain refinement at elevated temperatures.The flowable metal was extruded out of the printer head like a paste for building dense metal parts with fine equiaxed grains and wrought mechanical properties.Off-the-shelf metal rods were used as feedstock and the printing process was completed in an open-air environment,avoiding pricy powders and costly inert or vacuum conditions.The resulting multi-layer de-posited 6061 aluminum alloys yield strength and ductility comparable to wrought 6061 aluminum alloys after the same T6 heat treatment.The extrusion-based metal AM method can also be advanced as green manufacturing technologies for fabricating novel alloys and composites,adding novel features to existing parts,repairing damaged metal parts,and welding advanced metals for supporting sustainable manufac-turing,in addition to being developed into a cost-effective manufacturing process for the fabrication of dense metal of complex structural forms.
