Experimental investigation of the influence of the FSW plunge processing parameters on the maximum generated force and torque

The paper presents the results of an experimental investigation, done on the friction stir welding (FSW) plunging stage. Previous research works showed that the axial force and torque generated during this stage were characteristic for a static qualification of a FSW machine. Therefore, the investigation objectives are to better understand the relation between the processing parameters and the forces and torque generated. One of the goals is to find a way to reduce the maximum axial force and torque occurring at the end of the plunging stage in order to allow the use of a flexible FSW machine. Thus, the influence of the main plunge processing parameters on the maximum axial force and torque are analysed. In fact, forces and torque responses can be influenced by the processing parameter. At the end, a diagram presenting the maximum axial force and torque according to the processing parameters is presented. It is an interesting way to present the experimental results. This kind of representation can be useful for the processing parameters choice. They can be chosen according to the force and torque responses and consequently to the FSW machine capacities

In-process pokayoke development in multiple automatic manufacturing processes

In this dissertation, three in-process pokayoke systems were developed to prevent defects from occurring, so as to ensure product quality for three automated manufacturing processes.;The first pokayoke development resulted in an in-process, gap-caused flash monitoring (IGFM) system for injection-molding machines. An accelerometer sensor was integrated in the proposed system to detect the difference of the vibration signals between flash and non-flash products. By sub-grouping every two consecutive molded parts with the vibration signal, the online statistical process control (OLSPC) was able to monitor 100% of the molded products. The threshold of this system established by the SPC approach can determine if flash occurred when the machine was in process. The testing results indicated that the accuracy of this IGFM system was 94.7% when flash is caused by a mold-closing gap.;The second pokayoke development led to an in-process surface roughness adaptive control (ISRAC) system for CNC end milling operations. A multiple linear regression algorithm was successfully employed to generate the models for predicting surface roughness and adaptive feed rate change in real time. Not only were the machining parameters included in the ISRAC pokayoke system, but also the cutting force signals collected by a dynamometer sensor. The testing results showed this proposed ISRAC system was able to predict surface roughness in real time with an accuracy of 91.5%, and could successfully implement adaptive control 100% of the time during milling operations.;The third pokayoke development brought an in-process surface roughness adaptive control (ISRAC) system in CNC turning operations. This system employed a back-propagation (BP) neural network algorithm to train the models for in-process surface roughness prediction and adaptive parameter control. In addition to the machining parameters, vibration signals in the Z direction used as an input variable to the neural network system were included for training. The test runs showed this pokayoke system was able to predict surface roughness in real time with an accuracy of 92.5%. The 100% success rate for adaptive control proved that this proposed system could be implemented to adaptively control surface roughness during turning operations

Neural network modelling of Abbott-Firestone roughness parameters in honing processes

In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.Peer ReviewedPostprint (author's final draft

Energy efficient machine tools

The growing global energy demand from industry results in significant ecological and economical costs. Aiming to decrease the impact of machining operations, an increasing number of research activities and publications regarding energy efficient machine tools and machining processes can be found in the literature. This keynote paper provides an overview of current machine- and process-related measures to improve the energy efficiency of metal cutting machine tools. Based on an analysis of the energy requirements of machine tool components, design measures to reduce the energy demand of main and support units are introduced. Next, methods for an energy efficient operation of machine tools are reviewed. Furthermore, latest developments and already available energy efficiency options in the machine tool industry are discussed. The paper concludes with recommendations and future research questions for more energy efficient machine tools

An accuracy evolution method applied to five-axis machining of curved surfaces

Currently, some high-value-added applications involve the manufacturing of curved surfaces, where it is challenging to achieve surface accuracy, repeatability, and productivity simultaneously. Among free-form surfaces, curved surfaces are commonly used in blades and airfoils (with a teardrop-shaped cross-section) and optical systems (with axial symmetry). In both cases, multi-axis milling accuracy directly affects the subsequent process step. Therefore, reducing even insignificant errors during machining can improve the accuracy in the final production stages. This study proposes an “evolution” method to improve the machining accuracy of curved surfaces. The key is to include compensation for the machining error after the first part through profile error measurement. Thus, correction can be applied directly after the manufacturing programming is fully developed, achieving the product with the minimum number of iterations. Accordingly, this method measures the machining error and changes only one key parameter after the process. This study considered two cases. First, an airfoil in which the clamping force was corrected; the results were quite good with only one modification in the blade machining case. Second is an aspherical surface where tool path correction in the Z-axis was applied; the error was effectively compensated along the normal vector of the workpiece surface. The experimental results showed that the surface accuracy increased from 44.4 to 4.5 μm, and the error was reduced by 89.9%, confirming that the accuracy of the machine tool and process had achieved “evolution.” This technical study is expected to help improve the quality and productivity of manufacturing highly accurate curved surfaces.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by: Natural Science Foundation of Shaanxi Province (Grant number: 2021JM010) Natural Science Foundation of Suzhou City (Grant number: SYG202018) Spanish Ministry of science and innovation (Grant number: RTC2019-007,194–4) funded by MCIN/AEI/ 10.13039/501100011033 Basque government group IT 1573-22 Fundamental Research Funds for the Central Universities (Grant No. xzy012019007) Project ITENEO Grant PID2019-109340RB-I00 funded by MCIN/AEI/ 10.13039/501100011033 Project HCTM Grant PDC2021-121792-100 funded by 702 MCIN/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR The Basque Government Department of Education for the pre-doctoral grant PRE_2021_1_014

Nanosatellite fabrication and analysis

The advancements in technologies used in the aerospace industry have allowed universities to experiment with and develop small-scale satellites. Universities are taking advantage of the relatively low development costs of nanosatellite programs to give students experience in the field of spacecraft design. The purpose of Santa Clara University\u27s team, Nanosatellite Fabrication and Analysis, is to create a process to expedite the design, analysis, and fabrication phase of nanosatellite structures for students working on future satellite missions. The objective is to design four baseline nanosatellite structures to accommodate a range of potential missions where the designs are simple enough to be completely fabricated by students utilizing only the tools found in the Santa Clara University\u27s machine lab. Finite element analysis is conducted to ensure the designs meet NASA standards for natural frequency and that it can survive the forces it is subjected to during a launch. SatTherm, an easy to use thermal analysis tool for small spacecrafts, was used to conduct initial thermal simulations of the nanosatellite to determine the type of thermal components that will work for future missions. The success of team Nanosatellite Fabrication and Analysis proves that students can fabricate the structural frame of a nanosatellite using only the tools available in SCU\u27s machine lab

Optimization of machining characteristics during helical milling of AISI D2 steel considering chip geometry

Helical milling is one of the high-performance and high-quality hole manufacturing activities with strong prospects for the automotive and aerospace industries. Literature suggests chip geometry plays a significant role in optimizing machining operations. In the present study, a mechanistic approach is used to estimate the chip geometry, cutting force and power/energy consumption concerning the tool rotation angle. Experiments are conducted at different levels of spindle rotational speed, cutter orbital speed and axial depth of cuts using 8 and 10 mm diameter mill cutters. Experimental results for cutting speed in X, Y and Z directions are measured. A hybrid approach, which combines the Taguchi method and Graph theory and matrix approach (GTMA) technique is used and optimized process parameters. The highest aggregate utility process parameters are met by 2000 rpm spindle speed, 50 rpm orbital speed and 0.2 mm axial cutting depth during helical milling of AISI D2 steel. FEM simulation is used for predicting the chip thickness, cutting forces and power consumption and also validated the optimization

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