Relevant bibliographies by topics / Sensor Manufacturing

Academic literature on the topic 'Sensor Manufacturing'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Sensor Manufacturing"

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Konyha, József, and Tamás Bányai. "Sensor Networks for Smart Manufacturing Processes." Solid State Phenomena 261 (August 2017): 456–62. http://dx.doi.org/10.4028/www.scientific.net/ssp.261.456.

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Each factory and manufacturing plant needs a flexible and reliable in-plant resource supply to serve production processes efficiently. Manufacturing systems are composed of several numbers of elements, workstations, machines and logistics resources. Production line is a complex system because of the complicated manufacturing process, multiple types, high machining difficulty and many special processes in it. In the Industry 4.0 based on smart manufacturing, it is essential to support the processes with intelligent sensor networks. In this article, we give a brief overview about sensors often used in manufacturing processes. Sensor networks generate a massive and increasing amount of data that needs to be processed. Computationally intensive algorithms are used for the data processing (image, voice and signal processing, different classification functions, numeric optimization routines). Finally, we discuss how GPGPU can improve the real-time processing of data generated by intelligent sensor networks.
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Ziesche, Steffen, Adrian Goldberg, Uwe Partsch, Holger Kappert, Heidrun Kind, Mirko Aden, and Falk Naumann. "On-turbine multisensors based on Hybrid Ceramic Manufacturing Technology." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2019, HiTen (July 1, 2019): 000107–11. http://dx.doi.org/10.4071/2380-4491.2019.hiten.000107.

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Abstract The contribution evaluates the potential of piezoresistive multilayer ceramic sensory solutions using Low or High Temperature Cofired Ceramics (LTCC/HTCC) for on-turbine sensors. Relevant ceramic materials were characterized and evaluated with regard to applicability and reliability under application-like conditions. A multilayer ceramic sensory element was designed including a ceramic embedded pressure sensing membrane, a Pt100 temperature sensor and ceramic integrated wiring. Appropriate sealing methods to implement the ceramic into metal housings as well as electrical connection solutions were worked out, which allow for an operation under the increased temperatures (> 300°C) of the application. A system concept, including sensory element, signal conditioning electronics, mechanical and electrical interfaces is part of the investigation.
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Bloss, Richard. "Sensors stand out as an important key to improving manufacturing quality and productivity at the latest IMTS show." Sensor Review 34, no. 2 (March 17, 2014): 149–53. http://dx.doi.org/10.1108/sr-12-2012-732.

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Purpose – Review of the IMTS show in Chicago with emphasis on the new sensor innovations and applications on display. The paper aims to discuss these issues. Design/methodology/approach – In-depth interviews with exhibitors of sensors and sensor integrators at the show. Findings – Sensors continue to match an ever-increasing number of manufacturing tasks to improve quality and productivity at every step in the manufacturing and inspection process. Practical implications – Customers may be surprised at the sensor innovations and new applications to which sensors are being applied in the manufacturing environment. Originality/value – A review of some of the latest sensor innovations and applications that one might have seen if they had been on the exhibition floor at the Chicago IMTS show.
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Du, H., and B. E. Klamecki. "Force Sensors Embedded in Surfaces for Manufacturing and Other Tribological Process Monitoring." Journal of Manufacturing Science and Engineering 121, no. 4 (November 1, 1999): 739–48. http://dx.doi.org/10.1115/1.2833131.

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The measurement of tooling-workpiece interface forces is needed for process design, process modeling and analysis and process monitoring for control. One approach to measuring local interface loads with minimal disturbance of the surface and process is to embed sensors in the surface below the surface region of interest. Small piezoelectric sensing elements were cast into surfaces and their ability to measure loads applied normal to the surfaces was assessed. Sensor outputs were analyzed in terms of sensor depth below the surface, distance along the surface from the load location to the sensor location and sensor to sensor spacing. A mechanical model of the sensor-surface system was developed which predicted sensor output. The use of this sensing concept and type of sensor was demonstrated in strip drawing tests. Using temperature compensated sensors, workpiece holddown force was measured in tests during which drawbead penetration was varied.
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Johnson, Timothy L., and Mark E. Dausch. "SENSOR INFORMATICS FOR MANUFACTURING." IFAC Proceedings Volumes 39, no. 3 (2006): 125–30. http://dx.doi.org/10.3182/20060517-3-fr-2903.00078.

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Chryssolouris, G., M. Domroese, and P. Beaulieu. "Sensor Synthesis for Control of Manufacturing Processes." Journal of Engineering for Industry 114, no. 2 (May 1, 1992): 158–74. http://dx.doi.org/10.1115/1.2899768.

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When a human controls a manufacturing process he or she uses multiple senses to monitor the process. Similarly, one can consider a control approach where measurements of process variables are performed by several sensing devices which in turn feed their signals into process models. Each of these models contains mathematical expressions based on the physics of the process which relate the sensor signals to process state variables. The information provided by the process models should be synthesized in order to determine the best estimates for the state variables. In this paper two basic approaches to the synthesis of multiple sensor information are considered and compared. The first approach is to synthesize the state variable estimates determined by the different sensors and corresponding process models through a mechanism based on training such as a neural network. The second approach utilizes statistical criteria to estimate the best synthesized state variable estimate from the state variable estimates provided by the process models. As a “test bed” for studying the effectiveness of the above sensor synthesis approaches turning has been considered. The approaches are evaluated and compared for providing estimates of the state variable tool wear based on multiple sensor information. The robustness of each scheme with respect to noisy and inaccurate sensor information is investigated.
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Syafrudin, Muhammad, Ganjar Alfian, Norma Fitriyani, and Jongtae Rhee. "Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing." Sensors 18, no. 9 (September 4, 2018): 2946. http://dx.doi.org/10.3390/s18092946.

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With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.
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Baranov, A. M., and T. V. Osipova. "RECENT TRENDS IN THE DEVELOPMENT OF SENSORS FOR PRE-EXPLOSIVE CONCENTRATIONS OF FLAMMABLE GASES AND VAPORS OF FLAMMABLE LIQUIDS (REVIEW)." NAUCHNOE PRIBOROSTROENIE 31, no. 4 (November 30, 2021): 3–29. http://dx.doi.org/10.18358/np-31-4-i329.

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This paper presents a review of current trends in the development of manufacturing technologies of sensors of pre-explosive concentrations of flammable gases and vapors of flammable liquids. Various types of gas sensors are discussed, including catalytic, semiconductor, and optical sensor types, and the principles of their operation. The advantages and disadvantages of each type of gas sensor are highlighted. New and traditional technologies for manufacturing sensitive elements that improve sensor parameters such as processability, miniaturization and reduce energy consumption are discussed. In conclusion, this article suggests future trends and prospects for development and research to improve the sensitivity and selectivity of sensors.
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Zaid, Islam Mohamed, Mohamad Halwani, Abdulla Ayyad, Adil Imam, Fahad Almaskari, Hany Hassanin, and Yahya Zweiri. "Elastomer-Based Visuotactile Sensor for Normality of Robotic Manufacturing Systems." Polymers 14, no. 23 (November 24, 2022): 5097. http://dx.doi.org/10.3390/polym14235097.

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Modern aircrafts require the assembly of thousands of components with high accuracy and reliability. The normality of drilled holes is a critical geometrical tolerance that is required to be achieved in order to realize an efficient assembly process. Failure to achieve the required tolerance leads to structures prone to fatigue problems and assembly errors. Elastomer-based tactile sensors have been used to support robots in acquiring useful physical interaction information with the environments. However, current tactile sensors have not yet been developed to support robotic machining in achieving the tight tolerances of aerospace structures. In this paper, a novel elastomer-based tactile sensor was developed for cobot machining. Three commercial silicon-based elastomer materials were characterised using mechanical testing in order to select a material with the best deformability. A Finite element model was developed to simulate the deformation of the tactile sensor upon interacting with surfaces with different normalities. Additive manufacturing was employed to fabricate the tactile sensor mould, which was chemically etched to improve the surface quality. The tactile sensor was obtained by directly casting and curing the optimum elastomer material onto the additively manufactured mould. A machine learning approach was used to train the simulated and experimental data obtained from the sensor. The capability of the developed vision tactile sensor was evaluated using real-world experiments with various inclination angles, and achieved a mean perpendicularity tolerance of 0.34°. The developed sensor opens a new perspective on low-cost precision cobot machining.
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Barmpakos, Dimitris, and Grigoris Kaltsas. "A Review on Humidity, Temperature and Strain Printed Sensors—Current Trends and Future Perspectives." Sensors 21, no. 3 (January 22, 2021): 739. http://dx.doi.org/10.3390/s21030739.

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Printing technologies have been attracting increasing interest in the manufacture of electronic devices and sensors. They offer a unique set of advantages such as additive material deposition and low to no material waste, digitally-controlled design and printing, elimination of multiple steps for device manufacturing, wide material compatibility and large scale production to name but a few. Some of the most popular and interesting sensors are relative humidity, temperature and strain sensors. In that regard, this review analyzes the utilization and involvement of printing technologies for full or partial sensor manufacturing; production methods, material selection, sensing mechanisms and performance comparison are presented for each category, while grouping of sensor sub-categories is performed in all applicable cases. A key aim of this review is to provide a reference for sensor designers regarding all the aforementioned parameters, by highlighting strengths and weaknesses for different approaches in printed humidity, temperature and strain sensor manufacturing with printing technologies.
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Dissertations / Theses on the topic "Sensor Manufacturing"

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Bergman, Stacy (Stacy Adele) 1972. "Building competencies in sensor harness manufacturing through prototyping." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/34717.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2001.
Vita.
Includes bibliographical references (p. 105-115).
Organizational knowledge is information about products, processes and customers that is held by the personnel in the organization. Institutionalization of the best practices contained within this knowledge is essential in creating competencies. Unfortunately, organizations do not utilize this knowledge effectively. This thesis introduces a framework that shows how establishing a prototype system to enhance product and process understanding can start to build organizational knowledge in the short-term. Along with external benchmarking, this internal knowledge can be used to form best practices based competencies. This framework will be illustrated through the use of a case study. The case study was conducted at the Delphi Automotive Systems Mexico Technical Center. The case study project was undertaken to improve the Energy and Chassis Systems division's competencies in sensor wire harness products. In order to support this competency development, the author worked with Delphi Automotive Systems personnel to develop best practices based competencies employing the competency development framework introduced in this thesis. Installation of a working prototyping system was achieved in the six-month project period. Through the production and delivery of these harnesses, internal best practices were developed. These were used along with benchmarking of other harness manufacturers to develop a baseline best practices based competencies repository for the division. Effective use of the best practices based competencies developed during the case study project should foster improvement in competitive metrics, particularly speed-to-market and product development costs. Extension of best practices based competencies development to other products will allow human and monetary capital to be released from re-engineering tasks. These resources can then be refocused on product advancement to exceed customer expectations in the automotive market. It will also allow the organization to focus resources on development of strategic nonautomotive markets in order to diversify the business to protect against future downturn in the automotive market.
by Stacy Bergman.
S.M.
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Kumile, CM, and G. Bright. "Sensor fusion control system for computer integrated manufacturing." South African Journal of Industrial Engineering, 2008. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000669.

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Manufacturing companies of today face unpredictable, high frequency market changes driven by global competition. To stay competitive, these companies must have the characteristics of cost-effective rapid response to the market needs. As an engineering discipline, mechatronics strives to integrate mechanical, electronic, and computer systems optimally in order to create high precision products and manufacturing processes. This paper presents a methodology of increasing flexibility and reusability of a generic computer integrated manufacturing (CIM) cell-control system using simulation and modelling of mechatronic sensory system (MSS) concepts. The utilisation of sensors within the CIM cell is highlighted specifically for data acquisition, analysis, and multi-sensor data fusion. Thus the designed reference architecture provides comprehensive insight for the functions and methodologies of a generic shop-floor control system (SFCS), which consequently enables the rapid deployment of a flexible system.
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Roberson, David Mathew III. "Sensor-based Online Process Monitoring in Advanced Manufacturing." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72911.

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Effective quality improvement in the manufacturing industry is continually pursued. There is an increasing demand for real-time fault detection, and avoidance of destructive post-process testing. Therefore, it is desirable to employ sensors for in-process monitoring, allowing for real-time quality assurance. Chapter 3 describes the application of sensor based monitoring to additive manufacturing, in which sensors are attached to a desktop model fused deposition modeling machine, to collect data during the manufacturing process. A design of experiments plan is conducted to provide insight into the process, particularly the occurrence of process failure. Subsequently, machine learning classification techniques are applied to detect such failure, and successfully demonstrate the future potential of this platform and methodology. Chapter 4 relates the application of online, image-based quantification of the surface quality of workpieces produced by cylindrical turning. Representative samples of cylindrical shafts, machined by turning under various conditions, are utilized, and an apparatus is constructed for acquiring images while the part remains mounted on a lathe. The surface quality of these specimens is analyzed, employing an algebraic graph theoretic approach, and preliminary regression modeling displays an average surface roughness (Ra) prediction error of less than 8%. Prediction occurs in less than 2 seconds, showing the capability for future application in a real-time, quality control setting. Both of these cases, in additive manufacturing and in turning, are validated using real experimental data and analysis, showing application of sensor-based online process monitoring in multiple manufacturing areas.
Master of Science
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Locks, Stephanie Isabel. "General Bayesian approach for manufacturing equipment diagnostics using sensor fusion." Thesis, Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55036.

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Statistical analysis is used quite heavily in production operations. To use certain advanced statistical approaches such as Bayesian analysis, statistical models must be built. This thesis demonstrates the process of building the Bayesian models and addresses some of the classical limitations by presenting mathematical examples and proofs, by demonstrating the process with experimental and simulated implementations, and by completing basic analysis of the performance of the implemented models. From the analysis, it is shown that the performance of the Bayesian models is directly related to the amount of separation between the likelihood distributions that describe the behavior of the data features used to generate the multivariate Bayesian models. More specifically, the more features that had clear separation between the likelihood distributions for each possible condition, the more accurate the results were. This is shown to be true regardless of the quantity of data used to generate the model distributions during model building. In cases where distribution overlap is present, it is found that models performance become more consistent as the amount of data used to generate the models increases. In cases where distribution overlap is minimal, it is found that models performance become consistent within 4-6 data sets.
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Mason, Julian. "Guided-wave optical and hybrid sensor systems." Thesis, Kingston University, 1995. http://eprints.kingston.ac.uk/20587/.

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This thesis is concerned with the development of new fibre optic based sensors for liquid level and temperature measurement. For the measurement of fluid level an intrinsic sensor has been developed whereby the fibre itself forms the sensing medium. This fibre is made from a plastic, doped with a dye that fluoresces when illuminated by ultra-violet light. The sensing fibre is continuously illuminated but the fluid around it absorbs the light and hence the fluid level controls the intensity of light generated within the fluorescing fibre. The measurement of temperature forms the bulk of this thesis with liquid crystals being used as the transducing medium. Many different schemes have been investigated and characterised using their thermochromic, light scattering, and electro-optic properties. The latter necessitated the production of miniature photovoltaic devices which, apart from being a novel way of generating quasi-regulated electrical power, provide an alternative approach to energising many other types of hybrid sensor systems that at present are forced to use more complex techniques. Finally a simple theory has been developed to describe the behaviour of the liquid crystal based systems. The theory has been solved numerically and is in good agreement with the experimental results.
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Nieves, Bogonez Francisco Daniel. "Manufacturing and characterisation of a fibre optic acoustic emission sensor." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7762/.

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The value of Remote Condition Monitoring for the real-time evaluation of the structural integrity of critical components is undeniable. Fibre-reinforced polymer composites are a class of materials which offer significant advantages over conventional metal alloys used for manufacturing load bearing structures in cases where weight and/or energy consumption need to be kept to a minimum, for example automotive and aerospace applications. This is due to the excellent strength to weight ratio that FRPCs exhibit. However, their strongly anisotropic microstructure of poses significant challenges for Non-Destructive Evaluation of the actual structural health of components made from such materials. Acoustic Emission is a passive condition monitoring technique based on the detection of elastic stress waves emitted when damage evolves in a structure. Conventional piezoelectric AE sensors need to be surface-mounted as their embedding in FRPCs is impractical. Fibre Optic Acoustic Emission Sensors (FOAES) offer a distinct advantage since they are light weight, have small size and can be effectively embedded in composite laminates. Moreover, they can be multiplexed with the entire structure being monitored more effectively. This study has focused in the evaluation of the manufacturing process and characterisation of FOAES. Comparison of their performance with conventional commercial sensors was carried out.
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Brownlow, Michael James. "A time-of-flight optical range sensor for mobile robot navigation." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359420.

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Chen, Nongji. "Aspects of an open architecture robot controller and its integration with a stereo vision sensor." Thesis, University of Surrey, 1994. http://epubs.surrey.ac.uk/842989/.

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The work presented in this thesis attempts to improve the performance of industrial robot systems in a flexible manufacturing environment by addressing a number of issues related to external sensory feedback and sensor integration, robot kinematic positioning accuracy, and robot dynamic control performance. To provide a powerful control algorithm environment and the support for external sensor integration, a transputer based open architecture robot controller is developed. It features high computational power, user accessibility at various robot control levels and external sensor integration capability. Additionally, an on-line trajectory adaptation scheme is devised and implemented in the open architecture robot controller, enabling a real-time trajectory alteration of robot motion to be achieved in response to external sensory feedback. An in depth discussion is presented on integrating a stereo vision sensor with the robot controller to perform external sensor guided robot operations. Key issues for such a vision based robot system are precise synchronisation between the vision system and the robot controller, and correct target position prediction to counteract the inherent time delay in image processing. These were successfully addressed in a demonstrator system based on a Puma robot. Efforts have also been made to improve the Puma robot kinematic and dynamic performance. A simple, effective, on-line algorithm is developed for solving the inverse kinematics problem of a calibrated industrial robot to improve robot positioning accuracy. On the dynamic control aspect, a robust adaptive robot tracking control algorithm is derived that has an improved performance compared to a conventional PID controller as well as exhibiting relatively modest computational complexity. Experiments have been carried out to validate the open architecture robot controller and demonstrate the performance of the inverse kinematics algorithm, the adaptive servo control algorithm, and the on-line trajectory generation. By integrating the open architecture robot controller with a stereo vision sensor system, robot visual guidance has been achieved with experimental results showing that the integrated system is capable of detecting, tracking and intercepting random objects moving in 3D trajectory at a velocity up to 40mm/s.
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Li, Shan. "Monitoring and diagnosis of process faults and sensor faults in manufacturing processes." Diss., University of Iowa, 2008. https://ir.uiowa.edu/etd/206.

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The substantial growth in the use of automated in-process sensing technologies creates great opportunities for manufacturers to detect abnormal manufacturing processes and identify the root causes quickly. It is critical to locate and distinguish two types of faults - process faults and sensor faults. The procedures to monitor and diagnose process and sensor mean shift faults are presented with the assumption that the manufacturing processes can be modeled by a linear fault-quality model. A W control chart is developed to monitor the manufacturing process and quickly detect the occurrence of the sensor faults. Since the W chart is insensitive to process faults, when it is combined with U chart, both process faults and sensor faults can be detected and distinguished. A unit-free index referred to as the sensitivity ratio (SR) is defined to measure the sensitivity of the W chart. It shows that the sensitivity of the W chart is affected by the potential influence of the sensor measurement. A Bayesian variable selection based fault diagnosis approach is presented to locate the root causes of the abnormal processes. A Minimal Coupled Pattern (MCP) and its degree are defined to denote the coupled structure of a system. When less than half of the faults within an MCP occur, which is defined as sparse faults, the proposed fault diagnosis procedure can identify the correct root causes with high probability. Guidelines are provided for the hyperparameters selection in the Bayesian hierarchical model. An alternative CML method for hyperparameters selection is also discussed. With the large number of potential process faults and sensor faults, an MCMC method, e.g. Metropolis-Hastings algorithm can be applied to approximate the posterior probabilities of candidate models. The monitor and diagnosis procedures are demonstrated and evaluate through an autobody assembly example.
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Ma, Yong Qing. "Investigation into the materials and manufacturing of a new thin film magnetoresistive sensor." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/2834.

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There is increasing interest in utilising very thin anisotropic magnetoresistive films (typically in the range of a few nano-metres) to make magnetic sensors which can be made very small and with a high signal-to-noise ratio. In this thesis, a new type of sensor is investigated which has a pair of thin film MR stripes whose magnetisation is switched alternately in opposite directions using bias fields from currents in overlay conductor films. The investigation considers in particular the Barkhausen noise in these sensors and its relationship with factors such as sensor film thickness. The effect of thickness and grain size on the coercivity of Ni81Fe19 permalloy thin films deposited by r.f. sputtering with negative substrata bias is systematically investigated as a function of under-layer materials, thickness, and substrata temperature. The results show that there is a minimum in coercivity at a thickness of about 7.5 nm with a grain size of 4 nm. This may be attributed to the grain size via its relationship with magnetic domain wall thickness. A tantalum under-layer favours a (111) (low anisotropy) surface plane in permalloy sensor films. Elevated deposition temperature may reduce defects in the films. A comparison of the magnetic domain wall structure and the state of magnetisation in the very thin (5 - 30 nm) permalloy films with and without Ta underlayer is made by using Lorentz TEM observation. The result shows that it is different in both cases. With Ta underlayer, the wider, straighter and more regular domain walls together with less rippling in magnetisation in adjacent domains may lead to a reduction in the Barkhausen noise. Barkhausen noise studies of films are carried out by sweeping an ac field of 77 Hz onto the permalloy films and recording the induced output voltage due to the flux change as a function of time on a digital storage oscilloscope. Barkhausen noise and corresponding hysteresis of the sensor is studied by analysis of the MR response of various sensors together with their magnetoresistive hysteresis, which may be reduced or improved by using a suitable external high frequency field (5 kHz to 20 kHz). Variation in the sensitivity of the sensor to the magnitude of a switched-biasing field was measured by applying a very small alternating field (from about tens to a few hundreds of nano-Tesla) at frequency above about 250 Hz and varying a transverse de field component (δH.), The effect of biasing field frequency and external linearising field on the sensitivities of these MR sensors is studied using an in-house built measurement system. Highly sensitive magnetoresistive sensors (120 different types have been available within 30 substrates) have been designed and then fabricated by photolithography in a temperature and humidity controlled clean room. Sensor thicknesses are typically in the range from 5 nm to 40 nm and other dimensions typically (10 µm - 80 µm wide) and 6.4 mm long. The sensor is linear in the range from zero to 14 400 nT. It produces an almost noise-free output of 20 to 30 millivolts for a field change of about 160 nano-Tesla.
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Books on the topic "Sensor Manufacturing"

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Polyakov, Leonid. Soviet sensor technology for automated manufacturing (1983-1990). Falls Church, VA: Delphic Associates, 1991.

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1968-, Willig Andreas, ed. Protocols and architectures for wireless sensor networks. 2nd ed. Chichester, West Sussex, England: John Wiley & Sons, 2007.

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K, Tönshoff H., and Inasaki Ichiro, eds. Sensors in manufacturing. Weinheim: Wiley-VCH, 2001.

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Soloman, Sabrie. Sensors and control systems in manufacturing. 2nd ed. New York: McGraw-Hill, 2010.

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Sensors and control systems in manufacturing. New York: McGraw Hill, 1994.

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Soloman, Sabrie. Sensors and control systems in manufacturing. 2nd ed. New York: McGraw-Hill, 2010.

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John Wiley & Sons. Technical Insights., ed. Throwaway sensors: Ultra-low cost sensors key to smart products, smarter manufacturing. 2nd ed. New York: Technical Insights/J. Wiley, 1999.

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Automated manufacturing systems: Actuators, controls, sensors, and robotics. New York: Glencoe, 1995.

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John Wiley & Sons. Technical Insights., ed. Technical Insights' throwaway sensors: Ultra-low cost sensors key to smart products, smarter manufacturing. New York: Wiley, 1996.

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Singh, Rupinder, Balwinder Singh Dhaliwal, and Shyam Sundar Pattnaik. 3D Printing of Sensors, Actuators, and Antennas for Low-Cost Product Manufacturing. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003194224.

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Book chapters on the topic "Sensor Manufacturing"

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Shimizu, Yuki. "Contact-Type Micro Thermal Sensor for Surface Defect Detection." In Precision Manufacturing, 1–21. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-4912-5_18-1.

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Shimizu, Yuki. "Contact-Type Micro Thermal Sensor for Surface Defect Detection." In Precision Manufacturing, 515–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-4938-5_18.

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Pal, Surjya Kanta, Debasish Mishra, Arpan Pal, Samik Dutta, Debashish Chakravarty, and Srikanta Pal. "Sensor Electronics for Digital Twin." In Springer Series in Advanced Manufacturing, 21–115. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81815-9_2.

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Pau, L. F. "Sensor Fusion for Integrated Circuit Testing." In Computer Vision for Electronics Manufacturing, 87–104. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0507-1_6.

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Zhu, Kunpeng. "Sensor Fusion in Machining System Monitoring." In Springer Series in Advanced Manufacturing, 339–60. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87878-8_10.

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Cai, Yindi. "Molecular Dynamics Characterization of a Force Sensor Integrated Fast Tool Servo for On-Machine Surface Metrology." In Precision Manufacturing, 1–28. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-4912-5_21-1.

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Cai, Yindi. "Molecular Dynamics Characterization of a Force Sensor Integrated Fast Tool Servo for On-Machine Surface Metrology." In Precision Manufacturing, 601–29. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-4938-5_21.

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Jacubasch, A., H. Franke, and H. B. Kuntze. "Sensor Data Processing for Flexible Manufacturing Processes." In Kinematic and Dynamic Issues in Sensor Based Control, 61–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84012-8_3.

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Lehmhus, Dirk, and Matthias Busse. "Sensor and Electronics Integration in Additive Manufacturing." In Material-Integrated Intelligent Systems - Technology and Applications, 217–56. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2017. http://dx.doi.org/10.1002/9783527679249.ch9.

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Huang, Yiming, and Shanben Chen. "Welding Arc Spectrum Sensor and Welding System." In Key Technologies of Intelligentized Welding Manufacturing, 27–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7549-1_2.

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Conference papers on the topic "Sensor Manufacturing"

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Lee, Kang B., Eugene Y. Song, and Peter S. Gu. "A Sensor Model for Enhancement of Manufacturing Equipment Data Interoperability." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-88726.

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Sensors can provide real-time production information to optimize manufacturing processes in a factory. Recently, more attention has been paid to the application of sensors in smart manufacturing systems. Sensor data exchange, sharing, and interoperability are challenges for manufacturing equipment monitoring in smart manufacturing. Standardized sensor data formats and communication protocols can help to solve these problems. MTConnect is an open, free, extensible protocol for the data exchange between monitoring applications and shop floor devices which include machine tools, sensors, and actuators. This paper introduces a sensor model for MTConnect to enhance manufacturing equipment data interoperability. The sensor model defines a Sensor and SensorChannel, as well as an interface to access the Sensor and its SensorChannels, which include sensing element, calibration, signal conditioning, and analog-to-digital conversion (ADC) information. The sensor model has been implemented in a virtual milling machine with a built-in sensor. Two case studies of MTConnect Probe and Sample requests for sensor information are provided to verify the sensor model.
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Yoder, Valerie J., Steven W. Havens, Arthur J. Na, and Rachel E. Weingrad. "Sensor Fusion for Industrial Applications Using Transducer Markup Language." In ASME 2006 International Manufacturing Science and Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/msec2006-21116.

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Manufacturing processes would greatly benefit from fusing data from many disparate sensors, but systems today do not fully exploit available sensor data. Disparate sensors could include Coordinate Measurement Machines (CMM), laser surface scanners, micro sensors, cameras, acoustic devices, thermocouples, or other various devices which provide measurement or visual data. Often, sensor data requires separate customized software for each type of sensor system, as opposed to having common tools for use across a wide array of sensor systems. This process of stove-piping requires proprietary software for analysis and display of each sensor type, and inhibits interoperability. There are several challenges to sensor fusion which need to be addressed. First, many sensors providing data are heterogeneous in phenomena detection and operation, providing measurements of different target attributes. This makes the measurements very difficult to fuse directly. Second, these disparate sensors are asynchronous in time. The collection, integration, buffering and transmitting time can each affect the way time is calculated and stored by the sensor. Transducer Markup Language (TML), developed by IRIS Corporation, addresses these challenges. This paper describes TML and addresses examples of industrial applications of TML-enabled transducer networks.
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Raymond, Christopher J., Michael R. Murnane, S. Sohail H. Naqvi, and John R. McNeil. "Scatterometric sensor for lithography." In Microelectronic Manufacturing, edited by Anant G. Sabnis. SPIE, 1994. http://dx.doi.org/10.1117/12.186798.

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Lee, Kang B., Eugene Y. Song, and Peter S. Gu. "Integration of MTConnect and Standard-Based Sensor Networks for Manufacturing Equipment Monitoring." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7398.

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MTConnect is an open and extensible protocol designed for the exchange of data between shop floor devices and software applications. MTConnect allows manufacturers to facilitate retrieval of information and data from factory devices, such as machine tools, sensors, and controllers. Currently, MTConnect users read data from sensors through proprietary sensor interfaces using adaptors. The suite of Institute of Electrical and Electronics Engineers (IEEE) 1451 standards defines a set of open, common communication interfaces for sensor networks, including both sensor interfaces and network interfaces. This paper proposes an integration architecture of MTConnect with IEEE 1451 standard-based sensor networks. In the architecture, MTConnect plays a network interface role in the IEEE 1451 standard-based sensor networks via an MTConnect Agent. An adaptor is used to provide the mapping between the MTConnect Agent and the IEEE 1451 sensor network. A prototype system integrating MTConnect with IEEE 1451.2-based sensor network has been developed. Two case studies are provided to illustrate the integration.
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Cheng, Xudong, and Xiaochun Li. "Fabrication and Application of Micro Sensor Arrays on Nickel Substrate for Meso-Scale Manufacturing Processes." In ASME 2006 International Manufacturing Science and Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/msec2006-21113.

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Real time monitoring, diagnosis and control of numerous manufacturing processes is of critical importance in reducing operation costs, improving product quality, and shortening response time. Current sensors used in manufacturing, especially at meso/micro scale, are normally unable to provide measurements with desired spatial and temporal resolution at critical locations in metal structures (e.g. tooling). Micro sensors are expected to offer tremendous benefits for real time sensing in manufacturing processes. In this study, a batch production of micro thin film sensor arrays was realized by transferring thin film sensors from silicon wafers directly onto nickel substrates through standard microfabrication and electroplating techniques. To demonstrate the potential applications, micro sensor arrays that consist of multiple thermocouples and thermopiles were designed, fabricated and transferred into electroplated nickel to study temperature field and heat generation during meso-scale ultrasonic welding. Sensor arrays are arranged immediately adjacent to the mesoscale welding area for in-situ temperature and surface heat flux measurement. These micro sensor arrays provide high spatial and temporal resolution that cannot be achieved with conventional macro sensors.
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Gyurcsik, Ronald S. "Sensor bus cost of ownership investigation." In Microelectronic Manufacturing, edited by Anant G. Sabnis. SPIE, 1994. http://dx.doi.org/10.1117/12.186782.

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Wang, Xihui, Ajanta Saha, Ye Mi, Ali Shakouri, Muhammad Ashraful Alam, George T. C. Chiu, and Jan P. Allebach. "Thin-Film Nitrate Sensor Performance Prediction Based on Image Analysis and Credibility Data to Enable a Certify As Built Framework." In ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-85638.

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Abstract 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.
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Stemmer, Andreas, and Markus Brunner. "Sensor-guided nanorobots." In Intelligent Systems & Advanced Manufacturing, edited by Armin Sulzmann. SPIE, 1998. http://dx.doi.org/10.1117/12.298040.

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Werner, Andrew, Laine Mears, and Andrew Clark. "Dynamic Evaluation of a Nanocomposite Force Sensor." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50059.

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This paper describes the dynamic characteristics of a newly-designed force sensor comprised of carbon nanoparticles embedded in a polyphenylene sulfide matrix and operating on the principle of contact resistance change with pressure. Sensor performance was investigated for frequencies ranging from 1 to 1,000 Hz using two testing setups: a load frame for low frequency characterization and a piezo-electric stack for describing higher-frequency behavior. Bode magnitude and phase response plots were developed and it was determined that the sensor under study can be modeled as a first order system up to 600 Hz. The −3 dB bandwidth was found to be 90 Hz and the sensor’s time constant was determined to be 0.0018 seconds. A dynamic model of the sensor is constructed and compared against performance data. The sensor was found to have non-linear spring properties, allowing for two damping coefficients, one for each spring constant range, to be calculated. The damping coefficient was calculated to be 619 lb-s/in for loadings under 600 lbs and 1928 lb-s/in for loadings greater than 600 lbs. The sensor’s time response was also found to be more similar in shape to the input loading waveform when it was compared to piezoelectric load transducers.
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Murphy, Kent A., Michael F. Gunther, Russell G. May, Richard O. Claus, Tuan A. Tran, Jonathan A. Greene, and Paul G. Duncan. "EFPI sensor manufacturing and applications." In 1996 Symposium on Smart Structures and Materials, edited by C. Robert Crowe. SPIE, 1996. http://dx.doi.org/10.1117/12.239154.

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Reports on the topic "Sensor Manufacturing"

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Vann, C. S. ,. LLNL. New six degree of freedom position sensor greatly improves flexible manufacturing -- but will manufacturers adapt? Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/672336.

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Chappell, Mark, Wu-Sheng Shih, Cynthia Price, Rishi Patel, Daniel Janzen, John Bledsoe, Kay Mangelson, et al. Environmental life cycle assessment on CNTRENE® 1030 material and CNT based sensors. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42086.

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This report details a study investigating the environmental impacts associated with the development and manufacturing of carbon nanotube (CNT)–based ink (called CNTRENE 1030 material) and novel CNT temperature, flex, and moisture sensors. Undertaken by a private-public partnership involving Brewer Science (Rolla, Missouri), Jordan Valley Innovation Center of Missouri State University (Springfield, Missouri), and the US Army Engineer Research and Development Center (Vicksburg, Mississippi), this work demonstrates the environmental life cycle assessment (ELCA) methodology as a diagnostic tool to pinpoint the particular processes and materials posing the greatest environmental impact associated with the manufacture of the CNTRENE material and CNT-based sensor devices. Additionally, ELCA tracked the degree to which optimizing the device manufacturing process for full production also changed its predicted marginal environmental impacts.
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Sbriglia, Lexey Raylene. Embedding Sensors During Additive Manufacturing. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1209455.

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Peterson, Delvin, and Mark Costello. Calibration of Smart Weapon Motion Sensors for High-Speed Manufacturing Lines. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada441892.

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Saboriendo, Brian, Alexander Heifetz, Peter Kozak, Thomas W. Elmer, and Sasan Bakhtiari. Additive Manufacturing of Sensors and Components for Non-Destructive Evaluation Applications. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1480539.

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Lin, Yirong. Investigation on Smart Parts with Embedded Piezoelectric Sensors via Additive Manufacturing. Office of Scientific and Technical Information (OSTI), December 2017. http://dx.doi.org/10.2172/1412094.

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Lin, Yirong. Additive Manufacturing of Energy Harvesting Material System for Active Wireless MEMS Sensors. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1755669.

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McMurtrey, M. D., G. Ilevbare, and I. Van Rooyen. Report on technical activities and technical plan for university advanced manufacturing work for in-pile sensors. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1473593.

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Panat, Rahul. Low-Cost, Efficient and Durable High Temperature Wireless Sensors by Direct Write Additive Manufacturing for Application in Fossil Energy Systems. Office of Scientific and Technical Information (OSTI), December 2019. http://dx.doi.org/10.2172/1603308.

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Franklin, K. W., L. N. Howell, D. G. Lewis, C. A. Neugebauer, D. W. O'Brien, and S. A. Schilling. Why Model-Based Engineering and Manufacturing Makes Sense for the Plants and Laboratories of the Nuclear Weapon Complex. Office of Scientific and Technical Information (OSTI), May 2001. http://dx.doi.org/10.2172/15005294.

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