Elizabeth Cristine Adam Trindade, Camille Ruest, J. Deschênes, J. Brousseau
Selective laser sintering (SLS) is a typical method of 3D printing in an industrial environment. It is often used to print different materials, such as metals, ceramics, and plastics. Nylon 12 is the most common plastic and material processed by SLS technology. In the present paper, the water absorption and wettability of Nylon 12 in additive manufacturing (AM) products are explored. The research for obtaining inert, non-absorbent and non-corrosive surfaces, and globally more effective materials to reduce the proliferation of microorganisms is becoming a necessity for the development of novel food contact materials. Surface treatments aim at improving the porosity and general roughness of the material and are expected to improve its hydrophobicity. The wetting state between Nylon 12 and water was studied by measuring the contact angles as primary data. The measurement of absorbed water (ASTM 570) is thus used as an indicator of material quality to prevent bacterial growth and degradation of the material mechanical properties. Therefore, water absorption tests were performed with SLS printed plates with and without surface treatment. Plates with surface treatment showed a mass increase of 0.35 ± 0.04% while those without surface treatment showed a mass increase of 0.76 ± 0.08%.
{"title":"Food Contact Materials: An Analysis of Water Absorption in Nylon 12 3D Printed Parts Using SLS After VaporFuse Surface Treatment","authors":"Elizabeth Cristine Adam Trindade, Camille Ruest, J. Deschênes, J. Brousseau","doi":"10.1115/iam2022-93944","DOIUrl":"https://doi.org/10.1115/iam2022-93944","url":null,"abstract":"\u0000 Selective laser sintering (SLS) is a typical method of 3D printing in an industrial environment. It is often used to print different materials, such as metals, ceramics, and plastics. Nylon 12 is the most common plastic and material processed by SLS technology. In the present paper, the water absorption and wettability of Nylon 12 in additive manufacturing (AM) products are explored. The research for obtaining inert, non-absorbent and non-corrosive surfaces, and globally more effective materials to reduce the proliferation of microorganisms is becoming a necessity for the development of novel food contact materials. Surface treatments aim at improving the porosity and general roughness of the material and are expected to improve its hydrophobicity. The wetting state between Nylon 12 and water was studied by measuring the contact angles as primary data. The measurement of absorbed water (ASTM 570) is thus used as an indicator of material quality to prevent bacterial growth and degradation of the material mechanical properties. Therefore, water absorption tests were performed with SLS printed plates with and without surface treatment. Plates with surface treatment showed a mass increase of 0.35 ± 0.04% while those without surface treatment showed a mass increase of 0.76 ± 0.08%.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115385214","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}
Igor Ortiz, P. Álvarez, M. A. Montealegre, F. Cordovilla, J. Ocaña
The paper envisages the development of specific toolpaths for additive repair and cladding of full 3D geometry components by the Laser Metal Deposition Additive Manufacturing technique. Due to the essential difference between substractive and additive manufacturing approaches, the use of traditional substractive CAD-CAM programs is hardly suitable for a proper design and manufacturing of 3D additive manufactured AM’d components. The main key points for the development of CAD-CAM tools specifically applicable to Additive Manufacturing - AM processes are the need for an intrinsic process stability in terms of coating and layer growth, the need for a well-tailored additive track overlapping over the whole selected surface area and the need for integration of specific features relative to the laser, addition material and surface properties monitoring and control. The expected result of the full AM process based on the appropriate design tools is an efficient capability to meet not only the full 3D geometry according to the specified tolerances, but, very importantly, the microstructure specifications for the deposited material, avoiding the existence of critical defaults invalidating the fabrication or repair of the component. Moreover, the developed AZALA software must comply with the geometric specifications usual for manufacturing workstations, detecting preventively possible part-tool collisions with part and assuring an overall efficient manufacturing chain.
{"title":"Development of Adaptive Toolpaths for Repair and Cladding of Complex 3D Components by Laser Metal Deposition","authors":"Igor Ortiz, P. Álvarez, M. A. Montealegre, F. Cordovilla, J. Ocaña","doi":"10.1115/iam2022-94946","DOIUrl":"https://doi.org/10.1115/iam2022-94946","url":null,"abstract":"\u0000 The paper envisages the development of specific toolpaths for additive repair and cladding of full 3D geometry components by the Laser Metal Deposition Additive Manufacturing technique. Due to the essential difference between substractive and additive manufacturing approaches, the use of traditional substractive CAD-CAM programs is hardly suitable for a proper design and manufacturing of 3D additive manufactured AM’d components. The main key points for the development of CAD-CAM tools specifically applicable to Additive Manufacturing - AM processes are the need for an intrinsic process stability in terms of coating and layer growth, the need for a well-tailored additive track overlapping over the whole selected surface area and the need for integration of specific features relative to the laser, addition material and surface properties monitoring and control. The expected result of the full AM process based on the appropriate design tools is an efficient capability to meet not only the full 3D geometry according to the specified tolerances, but, very importantly, the microstructure specifications for the deposited material, avoiding the existence of critical defaults invalidating the fabrication or repair of the component. Moreover, the developed AZALA software must comply with the geometric specifications usual for manufacturing workstations, detecting preventively possible part-tool collisions with part and assuring an overall efficient manufacturing chain.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"71 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120874753","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}
Songqi Zhang, S. Enk, Moritz Kolter, J. Schleifenbaum
Laser powder bed fusion (LPBF) is a promising technology to manufacture complex geometry in a layer wised manner. Shifting from low volume prototyping to high volume production the demand for quality assurance and reliability of additive manufacturing systems increases hence in-situ monitoring systems are required to monitor process anomalies as input for further process control. Optical based monitoring systems, such as CMOS and CCD camera, are proved as an effective way to monitor layer wise geometrical distortion during manufacturing process. However, due to complex illumination condition in the process chamber, geometries of the printed parts are hard to distinguished and extracted from powder bed properly. In this study, we propose a novel method for an illumination setup by using polarized light sources to improve the distinguishability of printed parts compared to the powder bed on the layer wised monitoring images. In the proposed setup LED light sources are installed on each side of the optical camera with polarizing filters. For every printed layer, two images of powder bed are captured using each light source before recoating. The images are calibrated and stacked afterwards to get the polarized monitoring image at the current layer. The polarized image made in the new setup shows significant improvement of contrast between printed part and powder. The illumination setup was tested on an EOS M290 LPBF machine with AlSi10Mg powder. Polarized monitoring images were compared with images under original machine illumination. The result shows the distinguishable difference between grey values of printed parts and powder bed, where the geometry of the printed part can be extracted with F1 score = 0.977 using Otsu binarization algorithm.
{"title":"Polarized Illumination for Optical Monitoring System in Laser Powder Bed Fusion","authors":"Songqi Zhang, S. Enk, Moritz Kolter, J. Schleifenbaum","doi":"10.1115/iam2022-94437","DOIUrl":"https://doi.org/10.1115/iam2022-94437","url":null,"abstract":"\u0000 Laser powder bed fusion (LPBF) is a promising technology to manufacture complex geometry in a layer wised manner. Shifting from low volume prototyping to high volume production the demand for quality assurance and reliability of additive manufacturing systems increases hence in-situ monitoring systems are required to monitor process anomalies as input for further process control. Optical based monitoring systems, such as CMOS and CCD camera, are proved as an effective way to monitor layer wise geometrical distortion during manufacturing process. However, due to complex illumination condition in the process chamber, geometries of the printed parts are hard to distinguished and extracted from powder bed properly. In this study, we propose a novel method for an illumination setup by using polarized light sources to improve the distinguishability of printed parts compared to the powder bed on the layer wised monitoring images. In the proposed setup LED light sources are installed on each side of the optical camera with polarizing filters. For every printed layer, two images of powder bed are captured using each light source before recoating. The images are calibrated and stacked afterwards to get the polarized monitoring image at the current layer. The polarized image made in the new setup shows significant improvement of contrast between printed part and powder. The illumination setup was tested on an EOS M290 LPBF machine with AlSi10Mg powder. Polarized monitoring images were compared with images under original machine illumination. The result shows the distinguishable difference between grey values of printed parts and powder bed, where the geometry of the printed part can be extracted with F1 score = 0.977 using Otsu binarization algorithm.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131826476","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}
Thiago Assis Dutra, Catarina Costa, J. R. Matos, Bruna F. Oliveira, L. Oliveira, C. Coutinho
Traditionally, cellulosic materials have been applied in power transformers due to their good electrical insulation and oil absorption, although their hygroscopic characteristics consequently lead to time-consuming processes. Viewing to circumvent these limitations, the Additive Manufacturing of high-performance thermoplastics has been investigated as an alternative solution for solid insulation. In this context, the present work investigates the effect of process parameters on the geometrical and mechanical properties of 3D-printed Polyetheretherketone (PEEK) and Polyetherimide (PEI). To this end, the residual stresses and distortions are numerically computed considering different ranges of extrusion temperatures, printing speeds, and layer heights. Then, resulting elastic properties are predicted using the Asymptotic Homogenization technique. For that, two unit cells representing the microstructures found for the PEEK and PEI are adopted. From the obtained results, it was verified that lower layer heights and printing speeds, as well as higher extrusion temperatures, resulted in higher residual stresses. In contrast, higher layer heights, higher extrusion temperatures, and lower printing speeds resulted in higher distortions for both materials. In regards to the design of components, the obtained results provide useful data for both preliminary and critical analyses, potentially saving time and reducing waste of materials in future investigations involving 3D-printed high-performance thermoplastics.
{"title":"Effects of Printing Parameters on Geometrical and Mechanical Properties of 3D-Printed High-Performance Thermoplastics, Toward the Digitalization of Power Transformers","authors":"Thiago Assis Dutra, Catarina Costa, J. R. Matos, Bruna F. Oliveira, L. Oliveira, C. Coutinho","doi":"10.1115/iam2022-91989","DOIUrl":"https://doi.org/10.1115/iam2022-91989","url":null,"abstract":"\u0000 Traditionally, cellulosic materials have been applied in power transformers due to their good electrical insulation and oil absorption, although their hygroscopic characteristics consequently lead to time-consuming processes. Viewing to circumvent these limitations, the Additive Manufacturing of high-performance thermoplastics has been investigated as an alternative solution for solid insulation. In this context, the present work investigates the effect of process parameters on the geometrical and mechanical properties of 3D-printed Polyetheretherketone (PEEK) and Polyetherimide (PEI). To this end, the residual stresses and distortions are numerically computed considering different ranges of extrusion temperatures, printing speeds, and layer heights. Then, resulting elastic properties are predicted using the Asymptotic Homogenization technique. For that, two unit cells representing the microstructures found for the PEEK and PEI are adopted. From the obtained results, it was verified that lower layer heights and printing speeds, as well as higher extrusion temperatures, resulted in higher residual stresses. In contrast, higher layer heights, higher extrusion temperatures, and lower printing speeds resulted in higher distortions for both materials. In regards to the design of components, the obtained results provide useful data for both preliminary and critical analyses, potentially saving time and reducing waste of materials in future investigations involving 3D-printed high-performance thermoplastics.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121995369","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}
Sahil P. Wankhede, Xian Du, Ali Alshehri, Keith W. Brashler, D. Turcan
We have developed a proof of concept for a flexible sensor in harsh environmental conditions by using the inkjet printing technique. Printing a conductive pattern on a flexible substrate poses several challenges like surface energy mismatch, nonuniform ink deposition, and crack formation leading to poor conductivity. Further, there is a need for a flexible, oil and chemical-resistant encapsulant material to protect the sensor from harsh environments. We proposed a process to overcome these challenges and validated this process by measuring the actual and theoretical resistance values of the printed patterns on the flexible substrates that were found to be comparable. The printed patterns were encapsulated with fluoroelastomer, well-known for excellent oil and chemical resistance. We investigated the effect of a harsh environment on conductivity by submerging it in hydraulic oil at temperatures 80°C–180°C. Results revealed a negligible change in resistance. Thus, we devised a single process that can be used for printing conductive patterns on various flexible substrates like Polyethylene terephthalate, Polydimethylsiloxane, and Silicone rubber. Furthermore, the effectiveness of fluoroelastomer as an encapsulant for the harsh environment was investigated.
{"title":"Encapsulating and Inkjet-Printing Electronics on Flexible Substrates for Harsh Environment","authors":"Sahil P. Wankhede, Xian Du, Ali Alshehri, Keith W. Brashler, D. Turcan","doi":"10.1115/iam2022-92250","DOIUrl":"https://doi.org/10.1115/iam2022-92250","url":null,"abstract":"\u0000 We have developed a proof of concept for a flexible sensor in harsh environmental conditions by using the inkjet printing technique. Printing a conductive pattern on a flexible substrate poses several challenges like surface energy mismatch, nonuniform ink deposition, and crack formation leading to poor conductivity. Further, there is a need for a flexible, oil and chemical-resistant encapsulant material to protect the sensor from harsh environments. We proposed a process to overcome these challenges and validated this process by measuring the actual and theoretical resistance values of the printed patterns on the flexible substrates that were found to be comparable. The printed patterns were encapsulated with fluoroelastomer, well-known for excellent oil and chemical resistance. We investigated the effect of a harsh environment on conductivity by submerging it in hydraulic oil at temperatures 80°C–180°C. Results revealed a negligible change in resistance. Thus, we devised a single process that can be used for printing conductive patterns on various flexible substrates like Polyethylene terephthalate, Polydimethylsiloxane, and Silicone rubber. Furthermore, the effectiveness of fluoroelastomer as an encapsulant for the harsh environment was investigated.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122868544","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}
Structures made by additive manufacturing processes are highly anisotropic and carry defects. Complete elimination of these defects is not possible, and these defects degrade the mechanical properties (such as elastic modulus, tensile strength, and fracture strain). In the present study, mechanical properties are quantified as a function of building parameters, in particular, filling patterns, raster angle and orientation of build direction with respect to that of loading, in polylactic acid (PLA). The tensile strength of 3D printed PLA is the same for hexagonal and linear pattern filling when build direction is along thickness and width, while better toughness is offered by hexagonal pattern filling. Build direction along specimen gauge length gives very low tensile strength and toughness. Damage tolerance was quantified in terms of work of fracture and hexagonal filling provided better damage tolerance than line filling patterns for conditions of 0° and 45° with respect to crack whereas line filling tolerated damage better than hexagonal filling for the 90° orientation.
{"title":"Impact of Build Direction, Infill Pattern and Raster Angle on Mechanical Properties and Damage Tolerance of 3D Printed PLA","authors":"Deepesh Yadav, Prerna Gupta, B. N. Jaya","doi":"10.1115/iam2022-93940","DOIUrl":"https://doi.org/10.1115/iam2022-93940","url":null,"abstract":"\u0000 Structures made by additive manufacturing processes are highly anisotropic and carry defects. Complete elimination of these defects is not possible, and these defects degrade the mechanical properties (such as elastic modulus, tensile strength, and fracture strain). In the present study, mechanical properties are quantified as a function of building parameters, in particular, filling patterns, raster angle and orientation of build direction with respect to that of loading, in polylactic acid (PLA). The tensile strength of 3D printed PLA is the same for hexagonal and linear pattern filling when build direction is along thickness and width, while better toughness is offered by hexagonal pattern filling. Build direction along specimen gauge length gives very low tensile strength and toughness. Damage tolerance was quantified in terms of work of fracture and hexagonal filling provided better damage tolerance than line filling patterns for conditions of 0° and 45° with respect to crack whereas line filling tolerated damage better than hexagonal filling for the 90° orientation.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123433964","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}
Compressed sensing takes advantage of the sparsity of data representation in the reciprocal space and achieves data compression. The performance of compressed sensing however depends on the measurement and basis matrices. To maximize the sparsity level of recovered coefficient vectors, dictionary learning has been developed to optimize the basis matrices for specific signals. Nevertheless, the theoretically optimal results from dictionary learning can be difficult to achieve in manufacturing process monitoring because the physical realization is restricted by the number of sensors, physical sizes of sensors, and sensor accessibility in the manufacturing environment. In this work, a physics-constrained dictionary learning (PCDL) approach is proposed to optimize the measurement and basis matrices separately with the considerations of these restrictions. The uniqueness of the PCDL is that there is only one non-zero entry in each row in the optimized measurement matrix so that the physical locations for the sensor placement are directly determined. Additional constraints of sensor accessibility are also incorporated. The proposed PCDL is demonstrated with thermal imaging for fused filament fabrication process monitoring. High-resolution thermal images are reconstructed with the optimized basis matrix and the limited pixel values at the optimized locations to allow for efficient monitoring.
{"title":"Temperature Field Monitoring in Fused Filament Fabrication Process Based on Physics-Constrained Dictionary Learning","authors":"Yanglong Lu, Yan Wang","doi":"10.1115/iam2022-93987","DOIUrl":"https://doi.org/10.1115/iam2022-93987","url":null,"abstract":"\u0000 Compressed sensing takes advantage of the sparsity of data representation in the reciprocal space and achieves data compression. The performance of compressed sensing however depends on the measurement and basis matrices. To maximize the sparsity level of recovered coefficient vectors, dictionary learning has been developed to optimize the basis matrices for specific signals. Nevertheless, the theoretically optimal results from dictionary learning can be difficult to achieve in manufacturing process monitoring because the physical realization is restricted by the number of sensors, physical sizes of sensors, and sensor accessibility in the manufacturing environment. In this work, a physics-constrained dictionary learning (PCDL) approach is proposed to optimize the measurement and basis matrices separately with the considerations of these restrictions. The uniqueness of the PCDL is that there is only one non-zero entry in each row in the optimized measurement matrix so that the physical locations for the sensor placement are directly determined. Additional constraints of sensor accessibility are also incorporated. The proposed PCDL is demonstrated with thermal imaging for fused filament fabrication process monitoring. High-resolution thermal images are reconstructed with the optimized basis matrix and the limited pixel values at the optimized locations to allow for efficient monitoring.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114711819","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 front matter for this proceedings is available by clicking on the PDF icon.
通过点击PDF图标可获得本次会议的主题。
{"title":"IAM2022 Front Matter","authors":"","doi":"10.1115/iam2022-fm1","DOIUrl":"https://doi.org/10.1115/iam2022-fm1","url":null,"abstract":"\u0000 The front matter for this proceedings is available by clicking on the PDF icon.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126602645","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}
Karan Shah, Anqi He, Zifeng Wang, Xian Du, Xiaoning Jin
Roll-to-Roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based products, as opposed to sheet-to-sheet (S2S) approach suited for low-volume work. However, meeting the tight alignment tolerance requirements of additive multi-layer printed electronics specified by device resolution that is usually at micrometer scale has become a major challenge in R2R flexible electronics printing, preventing the fabrication technology from being transferred from conventional S2S to high-speed R2R production. Print registration in a R2R process is to align successive print patterns on the flexible substrate and to ensure quality printed devices through effective control of various process variables. Conventional model-based control methods require an accurate web-handling dynamic model and real-time tension measurements to ensure control laws can be faithfully derived. For complex multistage R2R systems, physics-based state-space models are difficult to derive, and real-time tension measurements are not always acquirable. In this paper, we present a novel data-driven model predictive control (DD-MPC) method to minimize the multistage register errors effectively. We show that the DD-MPC can handle multi-input and multi-output systems and obtain the plant model from sensor data via an Eigensystem Realization Algorithm (ERA) and Observer Kalman filter identification (OKID) system identification method. In addition, the proposed control scheme works for systems with partially measurable system states.
{"title":"Data-Driven Model Predictive Control for Roll-to-Roll Process Register Error","authors":"Karan Shah, Anqi He, Zifeng Wang, Xian Du, Xiaoning Jin","doi":"10.1115/iam2022-96840","DOIUrl":"https://doi.org/10.1115/iam2022-96840","url":null,"abstract":"\u0000 Roll-to-Roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based products, as opposed to sheet-to-sheet (S2S) approach suited for low-volume work. However, meeting the tight alignment tolerance requirements of additive multi-layer printed electronics specified by device resolution that is usually at micrometer scale has become a major challenge in R2R flexible electronics printing, preventing the fabrication technology from being transferred from conventional S2S to high-speed R2R production. Print registration in a R2R process is to align successive print patterns on the flexible substrate and to ensure quality printed devices through effective control of various process variables. Conventional model-based control methods require an accurate web-handling dynamic model and real-time tension measurements to ensure control laws can be faithfully derived. For complex multistage R2R systems, physics-based state-space models are difficult to derive, and real-time tension measurements are not always acquirable. In this paper, we present a novel data-driven model predictive control (DD-MPC) method to minimize the multistage register errors effectively. We show that the DD-MPC can handle multi-input and multi-output systems and obtain the plant model from sensor data via an Eigensystem Realization Algorithm (ERA) and Observer Kalman filter identification (OKID) system identification method. In addition, the proposed control scheme works for systems with partially measurable system states.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121430359","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}
Zhuo Yang, Jaehyuk Kim, Yan Lu, H. Yeung, B. Lane, Albert T. Jones, Yande Ndiaye
Data fusion techniques aim to improve inference results or decision making by ‘combining’ multiple data sources. Additive manufacturing (AM) in-situ monitoring systems measure various physical phenomena and generate multiple types of data. Data types that occur at different scales and sampling rates during a build process. Data types that can be used to monitor the state of that process. Monitoring typically requires software tools to analyze multiple data sources. There are two reasons. First, data only from an individual data source may not be accurate enough or large enough to monitor the process stat. Second, a single source will be limited by the relevancy of the observations, signal-to-noise ratio, or other measurement uncertainties. This work proposes a decision-level, multimodal, data fusion method that combines multiple, in-situ, AM monitoring data sources to improve overall, process-monitoring performance. The work is based on a recent, laser powder bed fusion (LPBF) experiment that was conducted to create overhang surfaces throughout a 3D part. The data from that experiment is used to illustrate and validate the proposed method. The overhang features were designed with different shapes. angles, and build locations. The features are formed using constant laser power and scan speed. A high-frequency, coaxial, melt-pool, imaging system and a low-frequency layerwise staring camera are the two, in-situ, monitoring, data sources used in that experiment. The Naïve Bayes and the k-nearest-neighbors algorithms are first applied to each data set for overhang feature detection. Then both hard voting and soft voting are adopted in fusing the classification outcomes. The results show that while none of the individual classifiers are perfect in detecting overhang features, the fused decision of the 324 test samples achieved 100% detection accuracy.
{"title":"A Multi-Modal Data-Driven Decision Fusion Method for Process Monitoring in Metal Powder Bed Fusion Additive Manufacturing","authors":"Zhuo Yang, Jaehyuk Kim, Yan Lu, H. Yeung, B. Lane, Albert T. Jones, Yande Ndiaye","doi":"10.1115/iam2022-96740","DOIUrl":"https://doi.org/10.1115/iam2022-96740","url":null,"abstract":"\u0000 Data fusion techniques aim to improve inference results or decision making by ‘combining’ multiple data sources. Additive manufacturing (AM) in-situ monitoring systems measure various physical phenomena and generate multiple types of data. Data types that occur at different scales and sampling rates during a build process. Data types that can be used to monitor the state of that process. Monitoring typically requires software tools to analyze multiple data sources. There are two reasons. First, data only from an individual data source may not be accurate enough or large enough to monitor the process stat. Second, a single source will be limited by the relevancy of the observations, signal-to-noise ratio, or other measurement uncertainties.\u0000 This work proposes a decision-level, multimodal, data fusion method that combines multiple, in-situ, AM monitoring data sources to improve overall, process-monitoring performance. The work is based on a recent, laser powder bed fusion (LPBF) experiment that was conducted to create overhang surfaces throughout a 3D part. The data from that experiment is used to illustrate and validate the proposed method. The overhang features were designed with different shapes. angles, and build locations. The features are formed using constant laser power and scan speed. A high-frequency, coaxial, melt-pool, imaging system and a low-frequency layerwise staring camera are the two, in-situ, monitoring, data sources used in that experiment. The Naïve Bayes and the k-nearest-neighbors algorithms are first applied to each data set for overhang feature detection. Then both hard voting and soft voting are adopted in fusing the classification outcomes. The results show that while none of the individual classifiers are perfect in detecting overhang features, the fused decision of the 324 test samples achieved 100% detection accuracy.","PeriodicalId":184278,"journal":{"name":"2022 International Additive Manufacturing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130324018","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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