Development of Machine Vision Based Workstation for Laser Micromachining

Today, laser based micromachining technologies enable the most advanced material manufacturing. Since it has wide range of applications in Microelectronics, medical device, aerospace etc., the accuracy of the process is of utmost significance. The current project proposes a machine vision assisted workstation for laser micromachining. The machine vision system not only has the ability to control the Laser path but also has the ability to locate the starting point of machining. The system was designed and developed from basic components, while MATLAB was used to control the laser direction, and to image the specimen. To analyse the limitations of the developed system, a rectangular shape was machined. Subsequently, known magnitudes of translational and rotational movements were given to the specimen. The images of machined area were captured before and after transformation. MATLAB algorithm was used to process the images to find the initial point of machined area on the transformed specimen. The laser beam is then guided to that point and the machining is repeated. The specimen was measured under microscope to find the error between the former and latter machined paths. Translational and angular errors were measured for various transformations. In this study, the challenges and corresponding possible solutions that are encountered in machining complex geometries are addressed. The study proposes mathematical function based and image processing based algorithms to find the machining coordinates and function-based approach was found to be more efficient for complex geometries. Furthermore, the effect of process parameters on the overall quality of the manufacturing are discussed. The COMSOL software was used to model all the effect of laser parameters on the roughness, depth and thickness of machined path. To validate the numerical model, experiments were conducted for different process parameters the results are in good agreement with a simulation results. The simulated model can be used to estimate the effect of the process parameters before the machining. Since the laser beam can be controlled on the geometry of the specimen and the study demonstrates the minimum possible error, this system can be applied to manufacture and repair wide range of microstructures

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

Microfluidic Large Scale Integration and its Application in Image Based Microflow Cytometry

An intelligent image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The development process used stationary cell images as training data. The images are enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracies of various machine-learning classification algorithms are compared. The linear support vector machine, distance-based classification, and k-nearest-neighbour algorithm were the classifiers used. The performance variations of the system under various illumination and focusing conditions are also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images. A micro fabricated cell sorter chip is then designed for the purpose of cell sorting using the above mentioned algorithm. A review of existing cytometry techniques is conducted to justify the choices of detection and flow control technologies. Then the chip structure is designed. Experiments are conducted with different channel dimensions and chip layouts to optimize the fabrication process and sample focusing performances, a sorting simulation is conducted using fluorescent beads to optimize the detection system parameters and verify the sorting accuracy. A cell counting experiment is also performed, the system was able to detect and classify cells with very high accuracy, with a throughput of 1.5 cells per second. Due to equipment and time limitation, cell sorting was not verified. This thesis project shows the goal of implementing mLSI at Waterloo Microfluidic Laboratory was successfully achieved, and imaging detection and mLSI can be used to produce a cell sorter capable of detecting and classifying yeast cells in different cell cycle phases. Recommendations are made at the end for improvements in the mLSI system, and the application of the cell sorter in detecting protein factors in budding yeast cells

Performance of compressed sensing for fluorine-19 magnetic resonance imaging at low signal-to-noise ratio conditions

PURPOSE: To examine the performance of compressed sensing (CS) in reconstructing low signal-to-noise ratio (SNR) (19)F MR signals that are close to the detection threshold and originate from small signal sources with no a priori known location. METHODS: Regularization strength was adjusted automatically based on noise level. As performance metrics, root-mean-square deviations, true positive rates (TPRs), and false discovery rates were computed. CS and conventional reconstructions were compared at equal measurement time and evaluated in relation to high-SNR reference data. (19)F MR data were generated from a purpose-built phantom and benchmarked against simulations, as well as from the experimental autoimmune encephalomyelitis mouse model. We quantified the signal intensity bias and introduced an intensity calibration for in vivo data using high-SNR ex vivo data. RESULTS: Low-SNR (19)F MR data could be reliably reconstructed. Detection sensitivity was consistently improved and data fidelity was preserved for undersampling and averaging factors of α = 2 or = 3. Higher α led to signal blurring in the mouse model. The improved TPRs at α = 3 were comparable to a 2.5-fold increase in measurement time. Whereas CS resulted in a downward bias of the (19)F MR signal, Fourier reconstructions resulted in an unexpected upward bias of similar magnitude. The calibration corrected signal-intensity deviations for all reconstructions. CONCLUSION: CS is advantageous whenever image features are close to the detection threshold. It is a powerful tool, even for low-SNR data with sparsely distributed (19)F signals, to improve spatial and temporal resolution in (19)F MR applications

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Miniaturized Transistors

What is the future of CMOS? Sustaining increased transistor densities along the path of Moore's Law has become increasingly challenging with limited power budgets, interconnect bandwidths, and fabrication capabilities. In the last decade alone, transistors have undergone significant design makeovers; from planar transistors of ten years ago, technological advancements have accelerated to today's FinFETs, which hardly resemble their bulky ancestors. FinFETs could potentially take us to the 5-nm node, but what comes after it? From gate-all-around devices to single electron transistors and two-dimensional semiconductors, a torrent of research is being carried out in order to design the next transistor generation, engineer the optimal materials, improve the fabrication technology, and properly model future devices. We invite insight from investigators and scientists in the field to showcase their work in this Special Issue with research papers, short communications, and review articles that focus on trends in micro- and nanotechnology from fundamental research to applications