M. Baral, Ali Al-Jewad, A. Breunig, J. Ha, P. Groche, Y. Korkolis, B. Kinsey
Elastic waves are generated and propagate when a material undergoes plastic deformation and can be detected by acoustic emission (AE). In this work, AE measurements are obtained during a uniaxial tension (UT) test using a custom-made sensor employing piezoelectric crystals. The UT tests are performed on an MTS machine with two AE sensors clamped on each end of the specimen gage section. A low pass Butterworth filter is designed to attenuate the high frequency noise from the AE signals. Also, full-field strain measurements on the specimen surface are acquired using the 2-D digital image correlation (DIC) method. A typical result from a UT test reveals, as the plastic deformation increases, the AE signals from each sensor increase until they reach a maximum value followed by a drop of signal until the specimen fractures. It is found through interrogation of the DIC images that the maximum amplitude from the AE signals corresponds to the early onset of localized necking. The goal of this work is to implement the UT findings in an actual forming process (e.g., cup drawing) and monitor the event in real time using closed loop control to achieve improved formability.
{"title":"Acoustic Emission Sensors to Monitor Early Onset of Necking During Uniaxial Tension","authors":"M. Baral, Ali Al-Jewad, A. Breunig, J. Ha, P. Groche, Y. Korkolis, B. Kinsey","doi":"10.1115/msec2022-85554","DOIUrl":"https://doi.org/10.1115/msec2022-85554","url":null,"abstract":"\u0000 Elastic waves are generated and propagate when a material undergoes plastic deformation and can be detected by acoustic emission (AE). In this work, AE measurements are obtained during a uniaxial tension (UT) test using a custom-made sensor employing piezoelectric crystals. The UT tests are performed on an MTS machine with two AE sensors clamped on each end of the specimen gage section. A low pass Butterworth filter is designed to attenuate the high frequency noise from the AE signals. Also, full-field strain measurements on the specimen surface are acquired using the 2-D digital image correlation (DIC) method. A typical result from a UT test reveals, as the plastic deformation increases, the AE signals from each sensor increase until they reach a maximum value followed by a drop of signal until the specimen fractures. It is found through interrogation of the DIC images that the maximum amplitude from the AE signals corresponds to the early onset of localized necking. The goal of this work is to implement the UT findings in an actual forming process (e.g., cup drawing) and monitor the event in real time using closed loop control to achieve improved formability.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88705886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work studies Metal Inert Gas (MIG) based Wire Arc Additive Manufacturing (WAAM) for nanoparticle enhanced AA7075. MIG WAAM is important for production and large structures due to its high deposition rates compared to Tungsten Inert Gas (TIG) or powder-based AM processes. Both MIG and TIG take advantage of wire feedstock, which is more readily available than powdered metals since the welding technology has been established for decades. Powder based processes allow for more complicated geometries but take significantly more time to produce and can suffer from voids which lead to non-uniform part density. TIG is normally used in welding of aluminum because it results in fewer defects, but the TiC/TiB2 nanoparticles eliminate solidification cracking normally associated with high strength aluminum alloys during welding. Porosity is another problem faced when welding aluminum, which can be affected by many things including deposition parameters, atmosphere and even the welding equipment used. Effects of different deposition parameters have been comprehensively studied including the deposition geometry and metallurgical properties. The process is also monitored with current/voltage measurement and high-speed imaging to understand the droplet transfer mode and molten pool development. The results are used to optimize process parameters to achieve the fewest defects possible while comparing different metal transfer modes. Multi-scale characterizations will be performed to examine the porosity distribution, solidification mode and grain size through optical microscopy. Future works will explore the distribution of secondary phases, precipitates, and nanoparticles through scanning electron microscopy (SEM) as well as conducting some mechanical testing of the as built structures such as hardness mapping and tensile tests.
{"title":"Experimental Analysis of Metal Inert Gas Based Wire Arc Additive Manufacturing of Aluminum Nanocomposite AA7075","authors":"M. Darnell, D. Harwig, Xun Liu","doi":"10.1115/msec2022-85413","DOIUrl":"https://doi.org/10.1115/msec2022-85413","url":null,"abstract":"\u0000 This work studies Metal Inert Gas (MIG) based Wire Arc Additive Manufacturing (WAAM) for nanoparticle enhanced AA7075. MIG WAAM is important for production and large structures due to its high deposition rates compared to Tungsten Inert Gas (TIG) or powder-based AM processes. Both MIG and TIG take advantage of wire feedstock, which is more readily available than powdered metals since the welding technology has been established for decades. Powder based processes allow for more complicated geometries but take significantly more time to produce and can suffer from voids which lead to non-uniform part density. TIG is normally used in welding of aluminum because it results in fewer defects, but the TiC/TiB2 nanoparticles eliminate solidification cracking normally associated with high strength aluminum alloys during welding. Porosity is another problem faced when welding aluminum, which can be affected by many things including deposition parameters, atmosphere and even the welding equipment used. Effects of different deposition parameters have been comprehensively studied including the deposition geometry and metallurgical properties. The process is also monitored with current/voltage measurement and high-speed imaging to understand the droplet transfer mode and molten pool development. The results are used to optimize process parameters to achieve the fewest defects possible while comparing different metal transfer modes. Multi-scale characterizations will be performed to examine the porosity distribution, solidification mode and grain size through optical microscopy. Future works will explore the distribution of secondary phases, precipitates, and nanoparticles through scanning electron microscopy (SEM) as well as conducting some mechanical testing of the as built structures such as hardness mapping and tensile tests.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"462 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79845541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Harish Singh Dhami, Pritish Panda, Puli Saikiran, K. Viswanathan
Commercial metal powders used as feedstock for additive manufacturing (AM) applications are primarily produced via gas or water atomization techniques. These are highly capital-intensive and inflexible, making the resulting powders as much as 3–10 times more expensive than corresponding cast ingots. Recently, we have demonstrated a potential alternative route for making metal powders — using surface grinding. The resulting powders have shown promise for use as stock in metal directed energy deposition (DED) processes. This work explores the applicability of these alternatively produced powders for laser sintering and related applications. Spherical metal powder particles (AISI 52100, SS 304) in the range of 5–100 microns were first produced using surface grinding. These powders were post-processed and segregated into monodisperse and polydisperse batches, representing high quality-low yield and low-quality-high yield stock, respectively. A high-power fiber laser source of 50 microns spot diameter was used to sinter these two stock powders, with nitrogen as a shielding gas, and their performance was evaluated using a range of ex situ analysis techniques. The latter included metallography, SEM/EDS and XRD analysis and was used to evaluate sintering quality in both cases, including melt pool and heat-affected zone characterization. Based on these results, we present recommendations for the use of mono- and polydisperse metallic powders and demonstrate the potential utility of using grinding as an alternative technique for the production of metal powders for laser sintering applications.
作为增材制造(AM)应用的原料的商业金属粉末主要通过气体或水雾化技术生产。这些都是高度资本密集型和不灵活的，使得所得粉末比相应的铸锭贵3-10倍。最近，我们已经证明了一种潜在的替代路线，使金属粉末-使用表面磨削。所得到的粉末已经显示出在金属定向能沉积(DED)工艺中用作库存的希望。这项工作探讨了这些替代生产粉末在激光烧结和相关应用中的适用性。在5-100微米范围内的球形金属粉末颗粒(AISI 52100, SS 304)首次使用表面磨削生产。这些粉末经过后处理并分离成单分散批次和多分散批次，分别代表优质-低收率和低品质-高收率的原料。利用光斑直径为50微米的高功率光纤激光源，在氮气保护气体下对这两种原料粉进行烧结，并使用一系列非原位分析技术对其性能进行了评估。后者包括金相、SEM/EDS和XRD分析，并用于评估两种情况下的烧结质量，包括熔池和热影响区表征。基于这些结果，我们提出了单分散和多分散金属粉末的使用建议，并展示了使用研磨作为激光烧结应用中金属粉末生产的替代技术的潜在效用。
{"title":"Metal Powders via Surface Grinding: Applicability and Performance Evaluation for Laser Sintering","authors":"Harish Singh Dhami, Pritish Panda, Puli Saikiran, K. Viswanathan","doi":"10.1115/msec2022-85120","DOIUrl":"https://doi.org/10.1115/msec2022-85120","url":null,"abstract":"\u0000 Commercial metal powders used as feedstock for additive manufacturing (AM) applications are primarily produced via gas or water atomization techniques. These are highly capital-intensive and inflexible, making the resulting powders as much as 3–10 times more expensive than corresponding cast ingots. Recently, we have demonstrated a potential alternative route for making metal powders — using surface grinding. The resulting powders have shown promise for use as stock in metal directed energy deposition (DED) processes. This work explores the applicability of these alternatively produced powders for laser sintering and related applications. Spherical metal powder particles (AISI 52100, SS 304) in the range of 5–100 microns were first produced using surface grinding. These powders were post-processed and segregated into monodisperse and polydisperse batches, representing high quality-low yield and low-quality-high yield stock, respectively. A high-power fiber laser source of 50 microns spot diameter was used to sinter these two stock powders, with nitrogen as a shielding gas, and their performance was evaluated using a range of ex situ analysis techniques. The latter included metallography, SEM/EDS and XRD analysis and was used to evaluate sintering quality in both cases, including melt pool and heat-affected zone characterization. Based on these results, we present recommendations for the use of mono- and polydisperse metallic powders and demonstrate the potential utility of using grinding as an alternative technique for the production of metal powders for laser sintering applications.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"3999 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86687505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Under the fourth industrial revolution (Industry 4.0), Augmented Reality (AR) provides new affordances for a variety of applications, such as AR-based human-robot interaction, virtual assembly assistance, and workforce virtual training. The see-through head-mounted displays (STHMDs), based on either optical see-through or video see-through technologies, are the primary AR device to augment the visual perception of the real environment with computer-generated contents through a hand-free headset. Specifically, the video see-through STHMDs process the superimposing of the real environment and virtual contents based on the digital images and output it to users, while optical see-through STHMDs display virtual contents through the optics-based near-eyes display with users’ normal view of the real scene kept. For both types of AR devices, the accuracy of visualization is essential. For example, in AR-based human-robot interaction, the inaccurate rendering of 3D virtual objects with respect to the real environment, will lead to users’ mistaking operations, and therefore, causes an invalid tool path planning result. In spite of many works related to system calibration and error reduction for optical see-through STHMDs, there are few efforts at figuring out the nature and factors of those errors in video see-through STHMDs. In this paper, taking consumer-available AR video see-through STHMDs as an example, we identify error sources of registration and build a mathematical model of the display progress to describe the error propagation in the stereo video see-through systems. Then, based on the mathematical model of the system, the sensitivity of each error source to the final registration error is analyzed. Finally, possible solutions of error correction are suggested and summarized in the general video see-through STHMDs.
{"title":"Visualization Error Analysis for Augmented Reality Stereo Video See-Through Head-Mounted Displays in Industry 4.0 Applications","authors":"Wenhao Yang, Yunbo Zhang","doi":"10.1115/msec2022-85440","DOIUrl":"https://doi.org/10.1115/msec2022-85440","url":null,"abstract":"\u0000 Under the fourth industrial revolution (Industry 4.0), Augmented Reality (AR) provides new affordances for a variety of applications, such as AR-based human-robot interaction, virtual assembly assistance, and workforce virtual training. The see-through head-mounted displays (STHMDs), based on either optical see-through or video see-through technologies, are the primary AR device to augment the visual perception of the real environment with computer-generated contents through a hand-free headset. Specifically, the video see-through STHMDs process the superimposing of the real environment and virtual contents based on the digital images and output it to users, while optical see-through STHMDs display virtual contents through the optics-based near-eyes display with users’ normal view of the real scene kept. For both types of AR devices, the accuracy of visualization is essential. For example, in AR-based human-robot interaction, the inaccurate rendering of 3D virtual objects with respect to the real environment, will lead to users’ mistaking operations, and therefore, causes an invalid tool path planning result. In spite of many works related to system calibration and error reduction for optical see-through STHMDs, there are few efforts at figuring out the nature and factors of those errors in video see-through STHMDs. In this paper, taking consumer-available AR video see-through STHMDs as an example, we identify error sources of registration and build a mathematical model of the display progress to describe the error propagation in the stereo video see-through systems. Then, based on the mathematical model of the system, the sensitivity of each error source to the final registration error is analyzed. Finally, possible solutions of error correction are suggested and summarized in the general video see-through STHMDs.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"161 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77211568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shenghan Guo, P. Paradise, Nicole Van Handel, D. Bhate
Scanning Electron Microscopy (SEM) is traditionally leveraged to image fracture surfaces and generate information for analysis. Conventionally, experts identify patterns of interest in SEM images and link them to fracture phenomena based on knowledge and experience. Such practice has substantial limitations. It relies on expert opinions for decision-making, which poses barriers for practitioners without relevant background; manual inspection must be done for individual SEM images, thus time-consuming and inapt for industrial automation. There is a genuine demand for a fast, automatic method for fractographic pattern recognition. Targeting the problem, this study proposes a two-stage data-driven approach based on clustering. In offline analysis (Stage 1), a clustering algorithm identifies the generic fractographic patterns on part. Each pattern corresponds to a cluster. Expert evaluation of the part’s crack status is leveraged to map individual patterns (clusters) to a crack type. In in-situ monitoring (Stage 2), SEM images of new parts are matched to the clusters from stage 1, which reveals the generic patterns on the part and indicates the potential crack status. The proposed approach enables automatic fractographic analysis without experts. It is demonstrated to be effective on real SEM images of additively manufactured Inconel-718 specimens subjected to high cycle fatigue.
{"title":"Image-Based Fractographic Pattern Recognition With Cluster Analysis","authors":"Shenghan Guo, P. Paradise, Nicole Van Handel, D. Bhate","doi":"10.1115/msec2022-82773","DOIUrl":"https://doi.org/10.1115/msec2022-82773","url":null,"abstract":"\u0000 Scanning Electron Microscopy (SEM) is traditionally leveraged to image fracture surfaces and generate information for analysis. Conventionally, experts identify patterns of interest in SEM images and link them to fracture phenomena based on knowledge and experience. Such practice has substantial limitations. It relies on expert opinions for decision-making, which poses barriers for practitioners without relevant background; manual inspection must be done for individual SEM images, thus time-consuming and inapt for industrial automation. There is a genuine demand for a fast, automatic method for fractographic pattern recognition. Targeting the problem, this study proposes a two-stage data-driven approach based on clustering. In offline analysis (Stage 1), a clustering algorithm identifies the generic fractographic patterns on part. Each pattern corresponds to a cluster. Expert evaluation of the part’s crack status is leveraged to map individual patterns (clusters) to a crack type. In in-situ monitoring (Stage 2), SEM images of new parts are matched to the clusters from stage 1, which reveals the generic patterns on the part and indicates the potential crack status. The proposed approach enables automatic fractographic analysis without experts. It is demonstrated to be effective on real SEM images of additively manufactured Inconel-718 specimens subjected to high cycle fatigue.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83298023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel tool wear predictive model was developed based on the current signals in this study. The system adapts to different part geometry with accurate prediction of the tool wear during the operation. The current sensor was utilized presenting a practical and better choice for tool wear monitoring which is inexpensive and no need to be attached to the working table or spindle. To avoid interruptions during the machining process, the tool wear was only measured at the end of the operation. The Long Short-Term Memory model was used to develop the tool wear prediction system. The tool wear prediction results indicate 23.92% and 36.41% average error for all the testing samples after 1/3 of the operations for profiling and straight turning, respectively. When the tool wear prediction was carried out after 2/3 of the operations, excellent results are observed with 6.15% error for profiling and 9.44% error for straight turning. The prediction results at the end of the operation shows 0.18% and 0.68% error for profiling and straight turning. The performance of the model using the current sensor shows that the model can predict the tool wear with less than 10% error after 2/3 of the turning operation without interfering with the turning process.
{"title":"Pass-Wise Tool Wear Prediction in Turning Based on Long-Short Term Memory Algorithm Using Current Signals","authors":"Benvolence Chinomona, C. Chung, Po-Chieh Wang","doi":"10.1115/msec2022-85339","DOIUrl":"https://doi.org/10.1115/msec2022-85339","url":null,"abstract":"\u0000 A novel tool wear predictive model was developed based on the current signals in this study. The system adapts to different part geometry with accurate prediction of the tool wear during the operation. The current sensor was utilized presenting a practical and better choice for tool wear monitoring which is inexpensive and no need to be attached to the working table or spindle. To avoid interruptions during the machining process, the tool wear was only measured at the end of the operation. The Long Short-Term Memory model was used to develop the tool wear prediction system. The tool wear prediction results indicate 23.92% and 36.41% average error for all the testing samples after 1/3 of the operations for profiling and straight turning, respectively. When the tool wear prediction was carried out after 2/3 of the operations, excellent results are observed with 6.15% error for profiling and 9.44% error for straight turning. The prediction results at the end of the operation shows 0.18% and 0.68% error for profiling and straight turning. The performance of the model using the current sensor shows that the model can predict the tool wear with less than 10% error after 2/3 of the turning operation without interfering with the turning process.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90458338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Asghar Rezasoltani, Matthew Crocker, J. Rice, Avery Black
The robotic incremental sheet forming process, including the CAD design, programming, and robotic manufacturing, as performed to form the school’s Big Red Mascot shape on a flat 30″ × 30″, gauge 26, AL 3003 sheet is explained in this research. This paper explains the technics used in the design, programming, and manufacturing processes to avoid or minimize typical ISF manufacturing problems such as spring-back effect, pillow effect, orange peel effect, and maximum wall angles. The quality of the forming process was investigated visually and by measuring the surface quality and geometric accuracy using a surface tester machine and a 3D scanner. The novelty of the work is the ISF manufacturing of the complex Big Red shape compared to the simple shapes reported in other research, as well as the experimental and measurement setup used in this research.
{"title":"Incremental Sheet Forming of the WKU Big Red Mascot","authors":"Asghar Rezasoltani, Matthew Crocker, J. Rice, Avery Black","doi":"10.1115/msec2022-78600","DOIUrl":"https://doi.org/10.1115/msec2022-78600","url":null,"abstract":"\u0000 The robotic incremental sheet forming process, including the CAD design, programming, and robotic manufacturing, as performed to form the school’s Big Red Mascot shape on a flat 30″ × 30″, gauge 26, AL 3003 sheet is explained in this research. This paper explains the technics used in the design, programming, and manufacturing processes to avoid or minimize typical ISF manufacturing problems such as spring-back effect, pillow effect, orange peel effect, and maximum wall angles.\u0000 The quality of the forming process was investigated visually and by measuring the surface quality and geometric accuracy using a surface tester machine and a 3D scanner. The novelty of the work is the ISF manufacturing of the complex Big Red shape compared to the simple shapes reported in other research, as well as the experimental and measurement setup used in this research.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85994444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Richie Garg, Harish Singh Dhami, Priti Ranjan Panda, K. Viswanathan
Metal additive manufacturing (AM) enables the production of non-trivial geometries and intricate internal structures. Directed energy deposition (DED) is one such AM process that has the inherent advantage of producing multi-material components on complex pre-existing geometries. Significant recent interest in DED processes has been driven by the need for inexpensive powders and potential material recycling. In this work, we explore the possibility of using non-standard arbitrary shaped metal powders within the DED process. A standard numerical model, comprising a three-dimensional viscous, compressible, turbulent solver with two-way discrete phase coupling is employed to understand the mechanics of gas-driven non-spherical powder flow. Spatial distributions of non-spherical powder on a set of pre-existing geometric features (e.g., corners, curved surfaces) are evaluateds and compared with standard spherical powders. The effect of particle collisions on the substrate is evaluated and corresponding density distributions are quantified. Non-spherical particles are generally found to exhibit higher velocities, and greater deposition track width, compared to spherical particles. Our simulations also reveal the effect of particle shape on their flow properties and final powder density. Using a custom-built DED configuration, we present preliminary experimental results of single-track depositions using both spherical and non-spherical powder particles. Based on our findings, we make a case for the use of non-spherical powders for DED applications.
{"title":"Directed Energy Deposition Using Non-Spherical Metal Powders?","authors":"Richie Garg, Harish Singh Dhami, Priti Ranjan Panda, K. Viswanathan","doi":"10.1115/msec2022-84945","DOIUrl":"https://doi.org/10.1115/msec2022-84945","url":null,"abstract":"\u0000 Metal additive manufacturing (AM) enables the production of non-trivial geometries and intricate internal structures. Directed energy deposition (DED) is one such AM process that has the inherent advantage of producing multi-material components on complex pre-existing geometries. Significant recent interest in DED processes has been driven by the need for inexpensive powders and potential material recycling. In this work, we explore the possibility of using non-standard arbitrary shaped metal powders within the DED process. A standard numerical model, comprising a three-dimensional viscous, compressible, turbulent solver with two-way discrete phase coupling is employed to understand the mechanics of gas-driven non-spherical powder flow. Spatial distributions of non-spherical powder on a set of pre-existing geometric features (e.g., corners, curved surfaces) are evaluateds and compared with standard spherical powders. The effect of particle collisions on the substrate is evaluated and corresponding density distributions are quantified. Non-spherical particles are generally found to exhibit higher velocities, and greater deposition track width, compared to spherical particles. Our simulations also reveal the effect of particle shape on their flow properties and final powder density. Using a custom-built DED configuration, we present preliminary experimental results of single-track depositions using both spherical and non-spherical powder particles. Based on our findings, we make a case for the use of non-spherical powders for DED applications.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"852 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74227154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Sheikh-Ahmad, R. U. Rehman, S. Deveci, F. Almaskari
In this study we investigate the effect of material temperatures on material flow and weld quality in the friction stir welding of bi-modal high density polyethylene (HDPE). The heat input to the process was controlled by varying the tool rotational speed, welding speed and the material initial temperature. Preheating of the HDPE blanks on the bottom surface of the weld was incorporated in order to increase the material flow in this relatively colder region. Temperatures on the boundary surfaces of the HDPE blanks were measured using an infrared camera and thermocouples. Material flow patterns were observed by welding two different colors of the polymer blanks, white on the advancing side and black on the retreating side. Joint quality was assessed using optical microscopy and joint strength was measured by tensile testing. It was found that material temperatures greatly affect the material flow in the weld zone, which in turn affects the tendency to form defects and the overall joint quality. High joint efficiencies and large elongations in excess of 100% were obtained when the material temperatures across the thickness were in excess of 100 °C.
{"title":"Process Temperatures and Material Flow in Friction Stir Welding of High Density Polyethylene (HDPE)","authors":"J. Sheikh-Ahmad, R. U. Rehman, S. Deveci, F. Almaskari","doi":"10.1115/msec2022-85464","DOIUrl":"https://doi.org/10.1115/msec2022-85464","url":null,"abstract":"\u0000 In this study we investigate the effect of material temperatures on material flow and weld quality in the friction stir welding of bi-modal high density polyethylene (HDPE). The heat input to the process was controlled by varying the tool rotational speed, welding speed and the material initial temperature. Preheating of the HDPE blanks on the bottom surface of the weld was incorporated in order to increase the material flow in this relatively colder region. Temperatures on the boundary surfaces of the HDPE blanks were measured using an infrared camera and thermocouples. Material flow patterns were observed by welding two different colors of the polymer blanks, white on the advancing side and black on the retreating side. Joint quality was assessed using optical microscopy and joint strength was measured by tensile testing. It was found that material temperatures greatly affect the material flow in the weld zone, which in turn affects the tendency to form defects and the overall joint quality. High joint efficiencies and large elongations in excess of 100% were obtained when the material temperatures across the thickness were in excess of 100 °C.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"116 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84922895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present study highlighted the effect of alloying elements in Al alloy on the interfacial microstructure, and the corresponding fracture behaviour of the Al alloy/steel inertia friction welded joint by selectively adopting two types of Al alloys. A strong texture of <111>//radial direction was formed on the Al alloy side in both types of joints, while no obvious changes were identified on the steel side. Different types of intermetallic compounds (IMCs) were formed at the weld interface. In the Al-Mg-Si alloy/steel joint produced at a low heat input, the interfacial microstructure was composed of a nanoscale amorphous layer and partially crystallised layer, while it turned into a fully crystallised Fe2Al5 phase with Si enriched when the heat input was enhanced. In the Al-Cu alloy/steel joint, Cu was enriched at the weld interface, with the possible formation of Fe-Al-Cu based IMCs. Moreover, a two-layered structure of IMC with different compositions of Cu appeared when the joint was prepared at a high heat input. Such distinct interfacial microstructure caused different fracture behaviours of joints. An interfacial reaction layer less than 130 nm thick led to the failure of Al alloy rather than the weld interface which easily happened at a thicker IMC.
{"title":"Effect of Alloying Elements of Al Alloy on the Interfacial Microstructure and Fracture Behaviour of Al Alloy/Steel Inertia Friction Welded Joint: A Comparative Study","authors":"Hong Ma, Peihao Geng, G. Qin","doi":"10.1115/msec2022-85196","DOIUrl":"https://doi.org/10.1115/msec2022-85196","url":null,"abstract":"\u0000 The present study highlighted the effect of alloying elements in Al alloy on the interfacial microstructure, and the corresponding fracture behaviour of the Al alloy/steel inertia friction welded joint by selectively adopting two types of Al alloys. A strong texture of <111>//radial direction was formed on the Al alloy side in both types of joints, while no obvious changes were identified on the steel side. Different types of intermetallic compounds (IMCs) were formed at the weld interface. In the Al-Mg-Si alloy/steel joint produced at a low heat input, the interfacial microstructure was composed of a nanoscale amorphous layer and partially crystallised layer, while it turned into a fully crystallised Fe2Al5 phase with Si enriched when the heat input was enhanced. In the Al-Cu alloy/steel joint, Cu was enriched at the weld interface, with the possible formation of Fe-Al-Cu based IMCs. Moreover, a two-layered structure of IMC with different compositions of Cu appeared when the joint was prepared at a high heat input. Such distinct interfacial microstructure caused different fracture behaviours of joints. An interfacial reaction layer less than 130 nm thick led to the failure of Al alloy rather than the weld interface which easily happened at a thicker IMC.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"217 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86499063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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