From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

Diagnosis of Bearing Fault Using Morphological Features Extraction and Entropy Deconvolution Method

It is observed that the bearing failure of rotating machinery is a pulse in the vibration signal, but it is mostly immersed in noise. In order to effectively eliminate this noise and detect pulses, a novel an image fusion technology based on morphological operators inference is proposed. The correctness of morphological operators lies in the correct selection of structural elements (SE). This report presents an effective algorithm for SE selection based on kurtosis, which makes the analysis free empirical method. When analyzing three different groups of faults, the results show that this method effectively and robustly generates impulse. It enables the algorithm to detect early faults too. Recently, minimum entropy deconvolution (MED) was introduced to the machine in the field of condition monitoring, to enhance the detection of rolling bearing and gear failures. MED analysis helps to extract these pulses and diagnose their source, namely defects bearing components. In this research, MED will be reviewed and reintroduced, Application in fault detection and diagnosis of rolling bearings. MED parameters are selected and its combination with pre-whitening. Test cases are presented to illustrate benefits of MED technology. The simulation has been done on MATLAB and a graphical user interface has been created for analysis of bearing and detection of bearing faults using morphological features

Tool Wear Measurement Using Machine Vision.

Tool wear has been extensively studied in the past due to its effect on the surface quality of the finished product.Vision-based systems using a CCD camera are increasingly being used for measurement of tool wear due to their numerous advantages compared to indirect methods

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154104/1/kampfner1996.pd

Correlative Framework of Techniques for the Inspection, Evaluation, and Design of Micro-electronic Devices

Trillions of micro- and nano-electronic devices are manufactured every year. They service countless electronic systems across a diverse range of applications ranging from civilian, military, and medical sectors. Examples of these devices include: packaged and board-mounted semiconductor devices such as ceramic capacitors, CPUs, GPUs, DSPs, etc., biomedical implantable electrochemical devices such as pacemakers, defibrillators, and neural stimulators, electromechanical sensors such as MEMS/NEMS accelerometers and positioning systems and many others. Though a diverse collection of devices, they are unified by their length scale. Particularly, with respect to the ever-present objectives of device miniaturization and performance improvement. Pressures to meet these objectives have left significant room for the development of widely applicable inspection and evaluation techniques to accurately and reliably probe new and failed devices on an ever-shrinking length scale. Presented in this study is a framework of correlative, cross-modality microscopy workflows coupled with novel in-situ experimentation and testing, and computational reverse engineering and modeling methods, aimed at addressing the current and future challenges of evaluating micro- and nano-electronic devices. The current challenges are presented through a unique series of micro- and nano-electronic devices from a wide range of applications with ties to industrial relevance. Solutions were reached for the challenges and through the development of these workflows, they were successfully expanded to areas outside the immediate area of the original project. Limitations on techniques and capabilities were noted to contextualize the applicability of these workflows to other current and future challenges

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique

Image processing algorithm for detection, quantification and classification of microdefects in wire electric discharge machined precision finish cut surfaces

This study aims to create an image processing algorithm that categorises the wire electric discharge machine (WEDM) processed finish cut surfaces, based on surface microdefects. The algorithm also detects the defect locations and suggests alternate parameter settings for improving the surface integrity. The proposed automated analysis is more precise, efficient and repeatable compared to manual inspection. Also, the method can be used for automatic data generation to suggest parameter changes in closed loop systems. During the training phase, mean, standard deviation and defect area fraction of enhanced binary images are extracted and stored. The training dataset consists of 27 WEDM finish cut surface images with labels, 'coarse', 'average' and 'smooth'. The trained model is capable of categorising any machined surface by detecting the microdefects. If the machined surface image is not classified as a smooth image, then alternate input parameter settings will be suggested by the model to minimise the microdefects. This is done based on the Euclidean distance between the current image datapoint and the nearest 'smooth' class datapoint

Image Segmentation and Classification using Small Data: An Application on Friendly Statements - A Computer Vision and Document Intelligence Project

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceInsurance companies face significant challenges in managing numerous physical documents containing critical information, resulting in considerable time and cost expenditures. Although Deep Learning models offer a promising solution, their implementation costs and data privacy concerns restrict widespread adoption, especially when dealing with confidential documents. This internship report presents a novel approach to address these challenges by developing a lightweight computer vision solution for accurately detecting and processing checkboxes from Portuguese friendly statements. The key objective was to demonstrate the feasibility of achieving high accuracy without relying on advanced Deep Learning techniques. By leveraging a small set of examples, we successfully extracted checkbox information while mitigating the high computational requirements associated with traditional Deep Learning models. The results highlight the practicality and cost-effectiveness of our approach, offering insurance companies a viable solution to streamline document management, enhance data security, and improve overall efficiency. This research contributes to the computer vision field by providing valuable insights into alternative methodologies that can be adopted to overcome the limitations of Deep Learning, facilitating broader accessibility and utilization among insurance providers