Ana Beatriz B. Henriques, Paola L. de Aguiar, Raphael G. dos Santos, Joice Miagava
In order to combine advantages of both additive and subtractive manufacturing, hybrid machine tools have been developed. In the hybrid process, directed energy deposition (DED) is the most used additive manufacturing technology due to its adaptability to CNC milling centers. However, in order to assure the integrity of a printed part, several process parameters must be set appropriately. Not only there are several parameters, but also some of these parameters influence different variables — e.g.: scan speed influences both the energy input per unit area and the powder volume that is deposited. In addition, another fact that complicates the achievement of a good quality in a workpiece is that some relevant parameters for additive manufacturing cannot be controlled due to CNC milling center constraints (e.g.: atmosphere). In this work, laser power (280 to 340 W) and scan speed (5 to 7 mm/s) were systematically varied to print 316L test samples with the aim of building a quality matrix. In the future, this matrix will be used to create strategies to optimize the quality of printed parts. Optical stereoscopy shows that the higher the laser power, the higher the sample, indicating that more powder is melted and deposited with an increasing laser power. By fixing the laser power and increasing the scan speed, printed samples were lower, indicating that less powder was deposited. Other parameters were preliminarily tested — e.g.: sample size and shield gas flow. Decreasing the sample size from 9 to 6 mm was sufficient to double the sample height, showing that the heat transfer rate was dramatically changed. Findings of this study shows that all process parameters act together and are determining factors for a good quality printed part. Moreover, it was noted that sample integrity is very sensitive to minimal changes in some process parameters.
{"title":"Evaluation of Process Parameters Modifications on Directed Energy Deposition Manufactured Parts Obtained in a Hybrid Additive Manufacturing Machine","authors":"Ana Beatriz B. Henriques, Paola L. de Aguiar, Raphael G. dos Santos, Joice Miagava","doi":"10.1115/msec2022-85315","DOIUrl":"https://doi.org/10.1115/msec2022-85315","url":null,"abstract":"\u0000 In order to combine advantages of both additive and subtractive manufacturing, hybrid machine tools have been developed. In the hybrid process, directed energy deposition (DED) is the most used additive manufacturing technology due to its adaptability to CNC milling centers. However, in order to assure the integrity of a printed part, several process parameters must be set appropriately. Not only there are several parameters, but also some of these parameters influence different variables — e.g.: scan speed influences both the energy input per unit area and the powder volume that is deposited. In addition, another fact that complicates the achievement of a good quality in a workpiece is that some relevant parameters for additive manufacturing cannot be controlled due to CNC milling center constraints (e.g.: atmosphere). In this work, laser power (280 to 340 W) and scan speed (5 to 7 mm/s) were systematically varied to print 316L test samples with the aim of building a quality matrix. In the future, this matrix will be used to create strategies to optimize the quality of printed parts. Optical stereoscopy shows that the higher the laser power, the higher the sample, indicating that more powder is melted and deposited with an increasing laser power. By fixing the laser power and increasing the scan speed, printed samples were lower, indicating that less powder was deposited. Other parameters were preliminarily tested — e.g.: sample size and shield gas flow. Decreasing the sample size from 9 to 6 mm was sufficient to double the sample height, showing that the heat transfer rate was dramatically changed. Findings of this study shows that all process parameters act together and are determining factors for a good quality printed part. Moreover, it was noted that sample integrity is very sensitive to minimal changes in some process parameters.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"197 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82392924","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 emerging smart manufacturing technologies pave the way for flexible and autonomous monitoring and control of complex manufacturing systems, which facilitate the implementation of real-time price (RTP) based demand response management towards sustainability. The demand response management requires scheduling of smart manufacturing systems in advance, and thus the quality of RTP predictions directly impacts the performance of demand response. Although several prediction evaluation metrics are currently available, they are designed to show the similarities between prediction and actual RTP, which are not necessarily related to demand response performance. Therefore, in this study, the daily energy cost reductions obtained by solving a demand response management problem are adopted as an indicator of demand response performance. Six commonly used evaluation metrics are examined, and their correlations with energy cost reductions are investigated. In addition, a new metric called k-peak distance considering the characteristics of the demand response problem is proposed and compared with the other six metrics. The case studies show that the proposed metric has two to four times higher correlation with energy cost reductions and only about half of the standard error compared to other metrics. The results indicate that the proposed metric can better represent the prediction quality in the demand response problem.
{"title":"Assessing the Impacts of Real-Time Price Prediction Quality on Demand Response Management for Sustainable Smart Manufacturing","authors":"Lingxiang Yun, Lin Li","doi":"10.1115/msec2022-85833","DOIUrl":"https://doi.org/10.1115/msec2022-85833","url":null,"abstract":"\u0000 The emerging smart manufacturing technologies pave the way for flexible and autonomous monitoring and control of complex manufacturing systems, which facilitate the implementation of real-time price (RTP) based demand response management towards sustainability. The demand response management requires scheduling of smart manufacturing systems in advance, and thus the quality of RTP predictions directly impacts the performance of demand response. Although several prediction evaluation metrics are currently available, they are designed to show the similarities between prediction and actual RTP, which are not necessarily related to demand response performance. Therefore, in this study, the daily energy cost reductions obtained by solving a demand response management problem are adopted as an indicator of demand response performance. Six commonly used evaluation metrics are examined, and their correlations with energy cost reductions are investigated. In addition, a new metric called k-peak distance considering the characteristics of the demand response problem is proposed and compared with the other six metrics. The case studies show that the proposed metric has two to four times higher correlation with energy cost reductions and only about half of the standard error compared to other metrics. The results indicate that the proposed metric can better represent the prediction quality in the demand response problem.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76610771","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}
B. Poudel, H. Nguyen, Aaron O'Neil, Mohsan Uddin Ahmad, Z. Qu, P. Kwon, Haseung Chung
Haynes 214, a nickel-based superalloy, and its oxide dispersion-strengthened (ODS) versions (addition of 0.3, and 1.5 wt. % yttria (Y2O3)) have been successfully fabricated using selective laser melting (SLM). For each feedstock formulation, optimal processing conditions were identified and high temperature tensile testing coupons were produced. Feedstock preparation and laser scanning strategy have been proven to be critical in the dispersion of nanoparticles in the metal matrix, as well as preventing the formation of extensive crack networks. The impact of Y2O3 addition on the high-temperature tensile properties of Haynes 214 was evaluated and discussed.
{"title":"Selective Laser Melting and Mechanical Properties of Oxide Dispersion Strengthened Haynes 214 Alloy","authors":"B. Poudel, H. Nguyen, Aaron O'Neil, Mohsan Uddin Ahmad, Z. Qu, P. Kwon, Haseung Chung","doi":"10.1115/msec2022-85620","DOIUrl":"https://doi.org/10.1115/msec2022-85620","url":null,"abstract":"\u0000 Haynes 214, a nickel-based superalloy, and its oxide dispersion-strengthened (ODS) versions (addition of 0.3, and 1.5 wt. % yttria (Y2O3)) have been successfully fabricated using selective laser melting (SLM). For each feedstock formulation, optimal processing conditions were identified and high temperature tensile testing coupons were produced. Feedstock preparation and laser scanning strategy have been proven to be critical in the dispersion of nanoparticles in the metal matrix, as well as preventing the formation of extensive crack networks. The impact of Y2O3 addition on the high-temperature tensile properties of Haynes 214 was evaluated and discussed.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73088879","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 reliability of manufacturing equipment is critical for ensuring the productivity and energy efficiency of a manufacturing facility. An unexpected machine breakdown may lead to unexpected downtime, disruption of manufacturing schedule, lower production efficiency, higher operation and maintenance cost. The recent development in machine learning and artificial intelligence enables data-driven Predictive Maintenance (PdM) by means of perceiving the dynamics of manufacturing systems and abstracting them into learnable features to provide a better interpretation of machine failures or unplanned downtimes. PdM, often translated to Prognostics and Health Management (PHM), aims to continue the optimal/normal operation of manufacturing systems. Often, vibration is used as a proxy of an early indicator of impending failure. In this study, tri-axial acceleration data collected from the two different machines are utilized. PdM-based strategies for machine condition monitoring and smart scheduling of equipment maintenance using an anomaly scoring model are discussed for two critical elements in a manufacturing system: 1) Chiller 2) Compressor. An anomaly scoring model is developed to extract meaningful information from the vibration data.
{"title":"Anomaly Scoring Model for Diagnosis on Machine Condition and Health Management","authors":"B. Joung, Zhongtian Li, J. Sutherland","doi":"10.1115/msec2022-85459","DOIUrl":"https://doi.org/10.1115/msec2022-85459","url":null,"abstract":"\u0000 The reliability of manufacturing equipment is critical for ensuring the productivity and energy efficiency of a manufacturing facility. An unexpected machine breakdown may lead to unexpected downtime, disruption of manufacturing schedule, lower production efficiency, higher operation and maintenance cost. The recent development in machine learning and artificial intelligence enables data-driven Predictive Maintenance (PdM) by means of perceiving the dynamics of manufacturing systems and abstracting them into learnable features to provide a better interpretation of machine failures or unplanned downtimes. PdM, often translated to Prognostics and Health Management (PHM), aims to continue the optimal/normal operation of manufacturing systems. Often, vibration is used as a proxy of an early indicator of impending failure. In this study, tri-axial acceleration data collected from the two different machines are utilized. PdM-based strategies for machine condition monitoring and smart scheduling of equipment maintenance using an anomaly scoring model are discussed for two critical elements in a manufacturing system: 1) Chiller 2) Compressor. An anomaly scoring model is developed to extract meaningful information from the vibration data.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73871505","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}
To encounter current issues regarding climate change, the hybridization of structures with lighter, often dissimilar, materials is an essential cornerstone of lightweight design. The different mechanical behavior of these materials results in challenges in terms of joining. This paper utilizes the joining process by hydraulic expansion to manufacture tube-to-tube joints of aluminum alloy AA6060 T66 and thermoplastic polycarbonate (Lexan) at room temperature. In contrast to metals, elastic and plastic strains coexist in thermoplastics from the beginning of deformation. Based on the theory of linear elasticity, an equation was derived to calculate the fluid pressure that expands the polycarbonate up to a strain value where plastic strains start to increase significantly in comparison to elastic strains. Tensile tests of the joined tubes revealed that the transferable tensile load increased approximately exponentially with increasing plastic deformation of the polycarbonate. With ongoing plastic deformation, micro-cracks appeared and merged within the thermoplastic. The appearance of these so-called crazes had no negative influence on the transferable load within the range of applied fluid pressure.
{"title":"Joining of Metal-Thermoplastic-Tube-Joints by Hydraulic Expansion","authors":"F. Weber, Peter Lehmenkühler, M. Hahn, A. Tekkaya","doi":"10.1115/msec2022-84991","DOIUrl":"https://doi.org/10.1115/msec2022-84991","url":null,"abstract":"\u0000 To encounter current issues regarding climate change, the hybridization of structures with lighter, often dissimilar, materials is an essential cornerstone of lightweight design. The different mechanical behavior of these materials results in challenges in terms of joining. This paper utilizes the joining process by hydraulic expansion to manufacture tube-to-tube joints of aluminum alloy AA6060 T66 and thermoplastic polycarbonate (Lexan) at room temperature. In contrast to metals, elastic and plastic strains coexist in thermoplastics from the beginning of deformation. Based on the theory of linear elasticity, an equation was derived to calculate the fluid pressure that expands the polycarbonate up to a strain value where plastic strains start to increase significantly in comparison to elastic strains. Tensile tests of the joined tubes revealed that the transferable tensile load increased approximately exponentially with increasing plastic deformation of the polycarbonate. With ongoing plastic deformation, micro-cracks appeared and merged within the thermoplastic. The appearance of these so-called crazes had no negative influence on the transferable load within the range of applied fluid pressure.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82106344","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}
In today’s distributed manufacturing reality, investors worldwide are faced with the dilemma of deciding on the optimal geographic spot for their manufacturing plants. On the one hand, emerging economies could be appealing because of their cheap labor as well as possibly their lack of or reduced regulations, litigation, and paperwork in some cases. On the other hand, these very same emerging economies can be quite risky because of the lack of stability of their political systems and hence, the associated economic volatility. Such economies can collapse in a relatively short period of time due to factors such as political instability, corruption, lack of democracy and the rule of law, social and racial injustices, and religious extremism, to name a few. In this paper, we propose a modeling approach where an economy is represented as an engineering system, the lifespan of which is subject to potential conditions, events, and failure modes. Such conditions and factors in the face of these fragile economies are modeled as pushers and deflators contributing to their instability. Hence, all laws of Reliability Engineering can be used to decide on the probability of success of such a system and its lifetime in the face of all uncertainty and given risks in today’s global climate. It is imperative that the health of the economic climate is a critical element solving the facility location and allocation problem; this entails deciding on large manufacturing investments in the form of new manufacturing plants being constructed and the accompanied supply chains. Enablers to allow for packageable manufacturing systems easier to relocate in the wake of this uncertain economic turmoil are also discussed. System Dynamics will be used as future work to account for the forces (deflators and pushers) when quantifying the proposed metrics. AI and Data Analytics techniques are also recommended to quantify the reliability parameters.
{"title":"Decision Support for Locating Manufacturing Plants in Emerging Economies Using a Reliability Approach","authors":"M. Gadalla, Ahmed E. Azab","doi":"10.1115/msec2022-83098","DOIUrl":"https://doi.org/10.1115/msec2022-83098","url":null,"abstract":"\u0000 In today’s distributed manufacturing reality, investors worldwide are faced with the dilemma of deciding on the optimal geographic spot for their manufacturing plants. On the one hand, emerging economies could be appealing because of their cheap labor as well as possibly their lack of or reduced regulations, litigation, and paperwork in some cases. On the other hand, these very same emerging economies can be quite risky because of the lack of stability of their political systems and hence, the associated economic volatility. Such economies can collapse in a relatively short period of time due to factors such as political instability, corruption, lack of democracy and the rule of law, social and racial injustices, and religious extremism, to name a few. In this paper, we propose a modeling approach where an economy is represented as an engineering system, the lifespan of which is subject to potential conditions, events, and failure modes. Such conditions and factors in the face of these fragile economies are modeled as pushers and deflators contributing to their instability. Hence, all laws of Reliability Engineering can be used to decide on the probability of success of such a system and its lifetime in the face of all uncertainty and given risks in today’s global climate. It is imperative that the health of the economic climate is a critical element solving the facility location and allocation problem; this entails deciding on large manufacturing investments in the form of new manufacturing plants being constructed and the accompanied supply chains. Enablers to allow for packageable manufacturing systems easier to relocate in the wake of this uncertain economic turmoil are also discussed. System Dynamics will be used as future work to account for the forces (deflators and pushers) when quantifying the proposed metrics. AI and Data Analytics techniques are also recommended to quantify the reliability parameters.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74538022","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}
Xihui Wang, Ajanta Saha, Ye Mi, A. Shakouri, Muhammad Ashraful Alam, G. Chiu, J. Allebach
In the modern industrial setting, there is an increasing demand for all types of sensors. The demand for both the quantity and quality of sensors is increasing annually. Our research focuses on thin-film nitrate sensors in particular, and it seeks to provide a robust method to monitor the quality of the sensors while reducing the cost of production. We are researching an image-based machine learning method to allow for real-time quality assessment of every sensor in the manufacturing pipeline. It opens up the possibility of real-time production parameter adjustments to enhance sensor performance. This technology has the potential to significantly reduce the cost of quality control and improve sensor quality at the same time. Previous research has proven that the texture of the topical layer (ion-selective membrane (ISM) layer) of the sensor directly correlates with the performance of the sensor. Our method seeks to use the correlation so established to train a learning-based system to predict the performance of any given sensor from a still photo of the sensor active region, i.e. the ISM. This will allow for the real-time assessment of every sensor instead of sample testing. Random sample testing is both costly in time and labor, and therefore, it does not account for all of the individual sensors. Sensor measurement is a crucial portion of the data collection process. To measure the performance of the sensors, the sensors are taken to a specialized lab to be measured for performance. During the measurement process, noise and error are unavoidable; therefore, we generated credibility data based on the performance data to show the reliability of each sensor performance signal at each sample time. In this paper, we propose a machine learning based method to predict sensor performance using image features extracted from the non-contact sensor images guided by the credibility data. This will eliminate the need to test every sensor as it is manufactured, which is not practical in a high-speed roll-to-roll setting, thus truely enabling a certify as built framework.
{"title":"Thin-Film Nitrate Sensor Performance Prediction Based on Image Analysis and Credibility Data to Enable a Certify As Built Framework","authors":"Xihui Wang, Ajanta Saha, Ye Mi, A. Shakouri, Muhammad Ashraful Alam, G. Chiu, J. Allebach","doi":"10.1115/msec2022-85638","DOIUrl":"https://doi.org/10.1115/msec2022-85638","url":null,"abstract":"\u0000 In the modern industrial setting, there is an increasing demand for all types of sensors. The demand for both the quantity and quality of sensors is increasing annually. Our research focuses on thin-film nitrate sensors in particular, and it seeks to provide a robust method to monitor the quality of the sensors while reducing the cost of production.\u0000 We are researching an image-based machine learning method to allow for real-time quality assessment of every sensor in the manufacturing pipeline. It opens up the possibility of real-time production parameter adjustments to enhance sensor performance. This technology has the potential to significantly reduce the cost of quality control and improve sensor quality at the same time. Previous research has proven that the texture of the topical layer (ion-selective membrane (ISM) layer) of the sensor directly correlates with the performance of the sensor. Our method seeks to use the correlation so established to train a learning-based system to predict the performance of any given sensor from a still photo of the sensor active region, i.e. the ISM. This will allow for the real-time assessment of every sensor instead of sample testing. Random sample testing is both costly in time and labor, and therefore, it does not account for all of the individual sensors.\u0000 Sensor measurement is a crucial portion of the data collection process. To measure the performance of the sensors, the sensors are taken to a specialized lab to be measured for performance. During the measurement process, noise and error are unavoidable; therefore, we generated credibility data based on the performance data to show the reliability of each sensor performance signal at each sample time.\u0000 In this paper, we propose a machine learning based method to predict sensor performance using image features extracted from the non-contact sensor images guided by the credibility data. This will eliminate the need to test every sensor as it is manufactured, which is not practical in a high-speed roll-to-roll setting, thus truely enabling a certify as built framework.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90927943","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}
Electrically assisted manufacturing (EAM) is defined as the direct application of electricity to a workpiece in situ with a manufacturing process. This is commonly used in forming to reduce the flow stress and increase the ductility of metals. Under certain conditions, there seem to be effects of the electricity that occur in addition to the inherent resistive heating in metals. This electroplastic effect is often deduced by estimating temperatures through analytical or numerical simulations and comparing this to the temperatures required to effect thermal stress reductions observed in experimental tests. For tests which utilized pulsed or AC currents, an RMS current value may be used to simplify simulations since current transience can be averaged to a constant representative value. However, there is often no justification of this assumption and it is possible that assumption could lead to erroneous results. Various assumptions applied to EAM research are explicitly explored herein to determine their validity in thermal estimations. It was concluded that AC, square wave, and sawtooth currents at frequencies greater than 1 Hz, or pulses from power supplies with significant ripple, can be approximated with a DC current of similar RMS value to obtain similar thermal estimations. Simulation geometries should incorporate as much of the experimental setup as possible. An example from literature was used to test several other assumptions as well, including the use of analytical simulations, rather than numerical.
{"title":"Thermal Analyses of Electrically Assisted Forming","authors":"T. Grimm, L. Mears","doi":"10.1115/msec2022-85262","DOIUrl":"https://doi.org/10.1115/msec2022-85262","url":null,"abstract":"\u0000 Electrically assisted manufacturing (EAM) is defined as the direct application of electricity to a workpiece in situ with a manufacturing process. This is commonly used in forming to reduce the flow stress and increase the ductility of metals. Under certain conditions, there seem to be effects of the electricity that occur in addition to the inherent resistive heating in metals. This electroplastic effect is often deduced by estimating temperatures through analytical or numerical simulations and comparing this to the temperatures required to effect thermal stress reductions observed in experimental tests.\u0000 For tests which utilized pulsed or AC currents, an RMS current value may be used to simplify simulations since current transience can be averaged to a constant representative value. However, there is often no justification of this assumption and it is possible that assumption could lead to erroneous results. Various assumptions applied to EAM research are explicitly explored herein to determine their validity in thermal estimations. It was concluded that AC, square wave, and sawtooth currents at frequencies greater than 1 Hz, or pulses from power supplies with significant ripple, can be approximated with a DC current of similar RMS value to obtain similar thermal estimations. Simulation geometries should incorporate as much of the experimental setup as possible. An example from literature was used to test several other assumptions as well, including the use of analytical simulations, rather than numerical.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88673009","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}
Jiang-Xiao Pei, Wang Mingxing, Liao Xiaobin, Yin Chao
In the cloud manufacturing (CMfg) model, users can get various high-quality, efficient manufacturing services (MSs) on-demand through the connection between the Internet of things and cloud platforms. While the problem of the reliable identification of MSs is one of the keys to the efficient operation of the cloud platform and the popularization and application of CMfg. To address this problem, a trust evaluation index system and a credible evaluation model considered the similarity and recommendation reliability between users’ behaviors are proposed in this paper. Based on the analysis of the factors that affect the credibility of MSs in the cloud environment, the analytic hierarchy process (AHP) is introduced to calculate the weight of each trusted evaluation index. In addition, a trusted estimation method based on collaborative filtering recommendation algorithm (CFRA) is proposed to solve the model and judge whether the MSs are trusted to the target user according to the obtained predictive valuation value. Finally, compared with PSO and GA, an example is employed to demonstrate the validity and effectiveness of the model and method, which can find a trusted MS for users and greatly save retrieval time.
{"title":"Collaborative Filtering Recommendation Based Trust Evaluation Method for Cloud Manufacturing Service","authors":"Jiang-Xiao Pei, Wang Mingxing, Liao Xiaobin, Yin Chao","doi":"10.1115/msec2022-86090","DOIUrl":"https://doi.org/10.1115/msec2022-86090","url":null,"abstract":"\u0000 In the cloud manufacturing (CMfg) model, users can get various high-quality, efficient manufacturing services (MSs) on-demand through the connection between the Internet of things and cloud platforms. While the problem of the reliable identification of MSs is one of the keys to the efficient operation of the cloud platform and the popularization and application of CMfg. To address this problem, a trust evaluation index system and a credible evaluation model considered the similarity and recommendation reliability between users’ behaviors are proposed in this paper. Based on the analysis of the factors that affect the credibility of MSs in the cloud environment, the analytic hierarchy process (AHP) is introduced to calculate the weight of each trusted evaluation index. In addition, a trusted estimation method based on collaborative filtering recommendation algorithm (CFRA) is proposed to solve the model and judge whether the MSs are trusted to the target user according to the obtained predictive valuation value. Finally, compared with PSO and GA, an example is employed to demonstrate the validity and effectiveness of the model and method, which can find a trusted MS for users and greatly save retrieval time.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81351856","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}
Roll-to-roll manufacturing is a promising platform to produce low cost, high quality electrical components and sensors for Internet of Things (IoT) applications. Within the roll-to-roll laboratories at Purdue University, a range of processes and machines have been developed to: 1) print patterns of conductive and sensing materials for use in electronic sensors, 2) cast films precisely with integrated functional materials, and 3) monitor quality of the printing and casting processes in-line in real time. A major development is the custom-designed and built Maxwell coating machine, which has enabled substantial quality improvements in slot-die coatings, as demonstrated by precise in-line thickness monitoring and imaging. As an illustrative example, we will show how we converted a design for a printed ion-selective electrode from a batch process to a roll-to-roll process. In-line process monitoring of coating thickness allowed us to identify high variability in the coating thickness due to an interaction between underlying printed electrodes and the drying process. By modifying the drying process, we demonstrated a substantial improvement as evidenced by the same in-line measurement technique. The overall process integrates several existing machines and processes in a novel way to create functional parts continuously, with data on individual parts gathered in real time.
{"title":"Roll to Roll Manufacturing and In-Line Imaging and Characterization of Functional Films","authors":"N. Glassmaker, Ye Mi, M. Cakmak, A. Shakouri","doi":"10.1115/msec2022-85553","DOIUrl":"https://doi.org/10.1115/msec2022-85553","url":null,"abstract":"\u0000 Roll-to-roll manufacturing is a promising platform to produce low cost, high quality electrical components and sensors for Internet of Things (IoT) applications. Within the roll-to-roll laboratories at Purdue University, a range of processes and machines have been developed to: 1) print patterns of conductive and sensing materials for use in electronic sensors, 2) cast films precisely with integrated functional materials, and 3) monitor quality of the printing and casting processes in-line in real time. A major development is the custom-designed and built Maxwell coating machine, which has enabled substantial quality improvements in slot-die coatings, as demonstrated by precise in-line thickness monitoring and imaging. As an illustrative example, we will show how we converted a design for a printed ion-selective electrode from a batch process to a roll-to-roll process. In-line process monitoring of coating thickness allowed us to identify high variability in the coating thickness due to an interaction between underlying printed electrodes and the drying process. By modifying the drying process, we demonstrated a substantial improvement as evidenced by the same in-line measurement technique. The overall process integrates several existing machines and processes in a novel way to create functional parts continuously, with data on individual parts gathered in real time.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83607233","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：