An Integrated Telemetric Thermocouple Sensor for Process Monitoring of CFRP Milling Operations

AbstractA wireless temperature measurement system was developed and integrated into a cutting tool holder via a thermocouple embedded within the cutting tool. The primary purpose of such an embedded thermal measurement sensor/system is for online process monitoring of machining processes within which thermal damage poses a significant threat both for the environment and productivity alike – as is the case with the machining of carbon fibre reinforced polymer (CFRP) components. A full system calibration was performed on the device. Response times were investigated and thermal errors, in the form of damping and lag, were identified. Experimental temperature results are presented which demonstrate the performance of the integrated wireless telemetry sensor during the edge trimming of CFRP composite materials. Thermocouple positioning relative to heat source effect was among the statistical factors investigated during machining experiments. Initial results into the thermal response of the sensor were obtained and a statistical package was used to determine the presence of significant main effects and interactions between a number of tested factors. The potential application of the embedded wireless temperature measurement sensor for online process monitoring in CFRP machining is demonstrated and recommendations are made for future advancements in such sensor technology

On the relationship between cutting temperature and workpiece polymer degradation during CFRP edge trimming

This research aims to investigate thermal effects generated during conventional CNC machining of polymer-based composites. A sensorised cutting tool incorporating a wireless embedded thermocouple is used to extract tool-side temperature information during edge trimming of carbon fibre reinforced polymer (CFRP) laminates whilst an infrared thermal imaging camera extracted workpiece temperature. Force data was collected to measure the specific cutting energy for each set of parameters tested. Parameters varied included feed rate, workpiece thickness and vertical cutting strategy in conjunction with design of experiment (DOE) methods. The temperature results of this investigation indicate that both vertical cutting strategy and depth of cut produce strongly significant effects on the steady-state temperature reached during machining. Force results demonstrate that while feed rate and axial depth of cut variations cause significant changes to the process forces, cutting strategy does not. The extremely sensitive nature of this process is demonstrated through detrimental surface quality, particularly at low chip loads, due to thermal constriction occurring when trimming takes place at the tool tip

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost

Varying CFRP workpiece temperature during slotting : effects on surface metrics, cutting forces and chip geometry

Carbon fibre reinforced thermoset polymer (CFRP) components are typically edge trimmed using a milling process to achieve final part shape. During this process the material is subject to significant heating at the tool-workpiece interface. Damage due to heating is fibre orientation specific; for some orientations it can lead to matrix smearing, potentially hiding defects and for others it can increase pullout. Understanding these relationships is critical to attaining higher throughput by edge milling. For the first time this study focuses on active heating of the CFRP rather than passive measurement, through use of a thermocouple controlled system to heat a CFRP workpiece material from room temperature (RT) up to 110 °C prior to machining. Differences in cutting mechanisms for fibres oriented at 0, 45, 90 and -45° are observed with scanning electron microscopy (SEM), and quantified with using focus variation with an increase of 89.9% Sa reported between RT and 110°C CFRP panel pre-heating. Relationships to cutting forces through dynamometer readings and tool temperature through infra-red (IR) measurements are also made with a novel optical method to measure cut chips presented. Results show an increase in chip length and width for increasing cutting temperature from RT to 110°C (3.39 and 0.79 µm for length and width, respectively). This work improves current understandings of how the cutting mechanism changes with increased temperature and suggests how improved milling throughput can be achieved

Directly Printed Nanomaterial Sensor for Strain and Vibration Measurement

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 안성훈.Most discussions about Industrie 4.0 tacitly assume that any such system would involve the processing and evaluation of large data volumes. Specifically, the operation of complex production processes requires stable and reliable data measurement and communication systems. However, while modern sensor technology may already be capable of collecting a wide range of machine and production data, it has been proving difficult to measure and analyse the data which is not easy to measurable and feed the results quickly back into an optimised production cycle. This is why the cost and installation of sensor, data acquisition, and transmission systems for flexible and adaptive manufacturing process have not been match the requirement of industrial demands. In this dissertation, directly printed nanomaterial sensor capable of strain and vibration measurement with high sensitivity and wide measurable range was fabricated using aerodynamically focused nanomaterial (AFN) printing system which is a direct printing technique for conductive and stretchable pattern printing onto flexible substrate. Specifically, microscale porous conductive pattern composed of silver nanoparticles (AgNPs) and multi-walled carbon nanotubes (MWCNTs) composite was printed onto polydimethylsiloxane (PDMS). Printing mechanism of AFN printing system for nanocomposite onto flexible substrate in order of mechanical crack generation, seed layer deposition, partial aggregation, and fully deposition was demonstrated and experimentally validated. The printed nanocomposite sensor exhibited gauge factor (GF) of 58.7, measurable range of 0.74, and variance in peak resistance under 0.05 during 1,000 times life cycle evaluation test. Furthermore, vibration measurement performance was evaluated according to vibration amplitude and frequency with Q-factor evaluation and statistical verification. Sensing mechanism for nanocomposite sensor was also analysed and discussed by both analytical and statistical methods. First, electron tunnelling effect among nanomaterials was analysed statistically using bivariate probit model. Since electrical property varies by the geometrical properties of nanomaterial, Monte Carlo simulation method based on Lennard-Jones (LJ) potential model and the voter model was developed for deeper understanding of the dynamics of nanomaterial by strain. By simply counting the average attachment among nanomaterials by strain, electrical conductivity was easily estimated with low simulation cost. The main objective of all processes to manufacture high-tech products is compliance with the specified ranges of permissible variation. In this perspective, all data must be recorded that might provide some evidence of status changes anywhere along the process chain. This dissertation covers the monitoring of forming and milling process. By measurement of mechanical deformation of stamp during forming process, it was possible to estimate the forming force according to various process parameters including maximum force, force gradient, and the thickness of sheet metal. Furthermore, accurate and reliable vibration monitoring was also conducted during milling process by simple and direct attachment of printed sensor to workpiece. Using frequency and power spectrum analysis of obtained data, the vibration of workpiece was measured during milling process according to process parameters including RPM, feed rate, cutting depth and width of spindle. Finally, developed sensor was applied to the digital twin of turbine blade manufacturing that vibration greatly affects the quality of product to predict the process defects in real time. To overcome the wire required data acquisition and transmission system, directly printed wireless communication sensor was also developed using chipless radio frequency identification (RFID) technology. It is one of the widely used technique for internet-of-things (IoT) devices due to low-cost, printability, and simplicity. The developed stretchable and chipless RFID sensor exhibited GF more than 0.6 and maximum measurable range more than 0.2 with high degree-of-freedom of motion. Since it showed its original characteristics of sensing in only one direction independently, sensor patch composed of various sensor with different resonance frequency was capable of measuring not only normal strains but also shear strains in all directions. Sensors in machinery and equipment can provide valuable clues as to whether or not the actual values will fall into the tolerance range. In this aspect, a real-time, accurate, and reliable process monitoring is a basic and crucial enabler of intelligent manufacturing operations and digital twin applications. In this dissertation, developed sensor was used for various manufacturing process include forming process, milling process, and wireless communication using highly sensitive and wide measuring properties with low fabrication cost. It is expected that developed sensor could be applied for the digital twin and process defects prediction in real-time.4차 산업혁명에 대한 대부분의 논의는 많은 양의 데이터를 처리하고 평가하는 시스템을 암묵적으로 가정한다. 특히, 복잡한 생산 공정을 운영하기 위해서는 안정적이고 신뢰할 수 있는 데이터 측정 및 통신 시스템이 필요하다. 하지만, 최신 센서 기술은 광범위한 기계 및 생산 공정 중 데이터를 수집하는 것이 가능하지만 측정하기 쉽지 않은 데이터를 측정하고 분석하여 그 결과를 최적화된 생산 공정에 신속하게 제공하는데 한계를 가지고 있다. 때문에, 유연하고 적응 가능한 제조 공정을 위한 센서의 가격과 설치 방법, 데이터 수집 및 전송 시스템이 실제 산업의 요구 사항에 도달하지 못하고 있다. 이 학위 논문에서는 유연 기판에 전도성 및 신축성 패턴을 직접 인쇄할 수 있는 공기역학적 나노물질 집속 인쇄 시스템을 사용하여 높은 민감도와 넓은 측정 가능 범위를 가진 변위 및 진동 센서를 개발하였다. 구체적으로, 은 나노입자와 다중 벽 탄소 나노튜브로 구성된 나노 복합재를 폴리디메틸실록산 위에 직접 인쇄하였다. 유연 기판 위에 공기역학적 나노물질 집속 인쇄 시스템을 사용한 나노 복합재 인쇄 방법의 기작이 기계적 균열 발생, 시드층 적층, 부분 응집 및 완전 증착 순으로 논의 및 실험적으로 검증되었다. 인쇄된 나노 복합재 센서는 58.7의 게이지 팩터, 0.74의 측정 가능 범위를 나타내었으며 1,000번 반복된 수명 주기 평가에서 5% 미만의 정점 저항 변화를 확인할 수 있었다. 또한 Q 인자 평가 및 통계 검증을 사용하여 진동의 진폭 및 주파수에 따른 진동 측정 성능을 평가하였다. 나노 복합재 센서에 대한 측정 기작 또한 해석적 및 통계적 방법으로 분석되었다. 먼저, 나노물질 간 터널 효과가 이변량 프로빗 모델을 통해 통계적으로 분석되었다. 센서의 전기적 물성이 나노물질의 기하학적 물성에 따라 상이하기 때문에 변위에 따른 나노물질의 동적인 이해를 위해 레너드존스 전위 및 유권자 모델을 기반으로 한 몬테카를로 시뮬레이션 방법이 개발되었다. 이를 활용하여 나노물질 간 평균 부착 수를 계산하여 낮은 비용으로 전기전도도를 추정할 수 있었다. 첨단 제품을 제조하기 위한 모든 공정의 주요 목표는 지정된 범위의 허용 가능한 변동을 준수하는 것이다. 이를 위해 공정 중 어디에서나 상태 변경의 증거를 제공할 수 있는 모든 데이터를 기록하는 것이 필수적이다. 이 학위 논문에서는 제작된 센서를 통해 성형 및 절삭 공정의 데이터를 기록함으로써 공정을 모니터링하였다. 성형 공정 동안 스탬프의 기계적 변형을 측정함으로써 최대 힘, 힘의 구배 및 판금의 두께를 포함하는 다양한 공정 변수에 따라 성형 힘을 추정할 수 있었다. 또한, 절삭 공정 중 공작물에 제작된 센서를 직접 부착하여 정확하고 안정적인 진동 모니터링을 수행하였다. 얻어진 데이터의 주파수 및 전력 스펙트럼 분석을 이용하여, 분당 회전 수, 이송 속도, 스핀들의 절삭 깊이 및 너비에 따른 공작물의 진동을 측정하였다. 마지막으로 제조된 센서를 진동이 제품 품질에 큰 영향을 미치는 터빈 동익 제조 공정의 디지털 트윈으로 적용하여 실시간으로 공정 결함을 예측하였다. 유선 데이터 수집 및 전송 시스템을 극복하기 위해 칩리스 무선 주파수 식별 기술을 사용하여 직접 인쇄된 무선 통신 센서를 개발하였다. 칩리스 무선 주파수 식별 기술은 저비용, 인쇄성 및 공정의 평이성으로 인해 사물 인터넷 장치에 널리 사용되는 기술 중 하나이다. 개발된 유연한 칩리스 센서는 0.6 이상의 게이지 팩터와 0.2 이상의 측정 가능 범위를 나타냈다. 또한 제작된 센서는 한 방향의 변위만 독립적으로 측정할 수 있는 특성을 가지고 있기 때문에, 모든 방향의 수직 및 전단 변형을 측정할 수 있는 다양한 공진 주파수로 구성된 센서 패치가 개발되었다. 기계 및 장비의 센서는 실제 값이 공차 범위에 속하는지 여부에 대한 중요한 단서를 제공할 수 있다. 이러한 측면에서, 정확하고 신뢰할 수 있는 실시간 공정 모니터링은 지능형 제조 공정 및 디지털 트윈으로의 응용을 위한 기본적이고 결정적인 요소이다. 이 학위 논문에서 개발된 센서는 낮은 제조 비용과 높은 민감도 및 신축성을 가지고 있기 때문에 성형 공정, 절삭 공정, 무선 통신을 포함한 다양한 제조 공정에서 응용되었다. 뿐만 아니라 제작된 센서는 디지털 트윈 및 공정 결함의 실시간 예측을 위해 다양하게 사용될 수 있을 것으로 예상된다.Chapter 1. Introduction 1 1.1. Toward smart manufacturing 1 1.2. Sensor in manufacturing 4 1.3. Research objective 11 Chapter 2. Background 16 2.1. Aerodynamically focused nanomaterial printing 16 2.2. Printing system envelope 26 2.3. Highly sensitive sensor printing 34 Chapter 3. Sensor fabrication and evaluation 42 3.1. Highly sensitive and wide measuring sensor printing 42 3.2. Sensing performance evaluation 59 3.3. Environmental and industrial evaluation 87 Chapter 4. Sensing mechanism analysis 97 4.1. Theoretical background 97 4.2. Statistical regression anaylsis 101 4.3. Monte Carlo simulation 104 Chapter 5. Application to process monitoring 126 5.1. Forming process monitoring 126 5.2. Milling process monitoring 133 5.3. Wireless communication monitoring 149 Chapter 6. Conclusion 185 Bibliography 192 Abstract in Korean 211Docto

Optimal sensor placement in structural health monitoring (SHM) with a field application on a RC bridge

Structural health monitoring (SHM) is a research field that targets detecting and locating damage in structures. The main objective of SHM is to detect damage at its onset and inform authorities about the type, nature and location of the damage in the structure. Successful SHM requires deploying optimal sensor networks. We present a probabilistic approach to identify optimal location of sensors based on a priori knowledge on damage locations while considering the need for redundancy in sensor networks. The optimal number of sensors is identified using a multi-objective optimization approach incorporating information entropy and cost of the sensor network. As the size of the structure grows, the advantage of the optimal sensor network in damage detection becomes obvious. We also present an innovative field application of SHM using Field Programmable Gate Array (FPGA) and wireless communication technologies. The new SHM system was installed to monitor a reinforced concrete (RC) bridge on interstate I-40 in Tucumcari, New Mexico. The new monitoring system is powered with renewable solar energy. The integration of FPGA and photovoltaic technologies make it possible to remotely monitor infrastructure with limited access to power. Using calibrated finite element (FE) model of the bridge with real data collected from the sensors installed on the bridge, we establish fuzzy sets describing different damage states of the bridge. Unknown states of the bridge performance are then identified using degree of similarity between these fuzzy sets. The proposed SHM system will reduce human intervention significantly and can save millions of dollars currently spent on prescheduled inspection by enabling performance based monitoring