An overview of fundamentals, best practices and a tutorial.
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In this post I give an overview of the theory, tools, frameworks and best practices I have found until now around the testing (and debugging) of machine learning applications. I will start by giving an overview of the specificities of testing machine learning applications.
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This white paper is the result of a research project by Hogeschool Utrecht, Floryn, Researchable, and De Volksbank in the period November 2021-November 2022. The research project was a KIEM project1 granted by the Taskforce for Applied Research SIA. The goal of the research project was to identify the aspects that play a role in the implementation of the explainability of artificial intelligence (AI) systems in the Dutch financial sector. In this white paper, we present a checklist of the aspects that we derived from this research. The checklist contains checkpoints and related questions that need consideration to make explainability-related choices in different stages of the AI lifecycle. The goal of the checklist is to give designers and developers of AI systems a tool to ensure the AI system will give proper and meaningful explanations to each stakeholder.
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Many lithographically created optical components, such as photonic crystals, require the creation of periodically repeated structures [1]. The optical properties depend critically on the consistency of the shape and periodicity of the repeated structure. At the same time, the structure and its period may be similar to, or substantially below that of the optical diffraction limit, making inspection with optical microscopy difficult. Inspection tools must be able to scan an entire wafer (300 mm diameter), and identify wafers that fail to meet specifications rapidly. However, high resolution, and high throughput are often difficult to achieve simultaneously, and a compromise must be made. TeraNova is developing an optical inspection tool that can rapidly image features on wafers. Their product relies on (a) knowledge of what the features should be, and (b) a detailed and accurate model of light diffraction from the wafer surface. This combination allows deviations from features to be identified by modifying the model of the surface features until the calculated diffraction pattern matches the observed pattern. This form of microscopy—known as Fourier microscopy—has the potential to be very rapid and highly accurate. However, the solver, which calculates the wafer features from the diffraction pattern, must be very rapid and precise. To achieve this, a hardware solver will be implemented. The hardware solver must be combined with mechatronic tracking of the absolute wafer position, requiring the automatic identification of fiduciary markers. Finally, the problem of computer obsolescence in instrumentation (resulting in security weaknesses) will also be addressed by combining the digital hardware and software into a system-on-a-chip (SoC) to provide a powerful, yet secure operating environment for the microscope software.
Currently, many novel innovative materials and manufacturing methods are developed in order to help businesses for improving their performance, developing new products, and also implement more sustainability into their current processes. For this purpose, additive manufacturing (AM) technology has been very successful in the fabrication of complex shape products, that cannot be manufactured by conventional approaches, and also using novel high-performance materials with more sustainable aspects. The application of bioplastics and biopolymers is growing fast in the 3D printing industry. Since they are good alternatives to petrochemical products that have negative impacts on environments, therefore, many research studies have been exploring and developing new biopolymers and 3D printing techniques for the fabrication of fully biobased products. In particular, 3D printing of smart biopolymers has attracted much attention due to the specific functionalities of the fabricated products. They have a unique ability to recover their original shape from a significant plastic deformation when a particular stimulus, like temperature, is applied. Therefore, the application of smart biopolymers in the 3D printing process gives an additional dimension (time) to this technology, called four-dimensional (4D) printing, and it highlights the promise for further development of 4D printing in the design and fabrication of smart structures and products. This performance in combination with specific complex designs, such as sandwich structures, allows the production of for example impact-resistant, stress-absorber panels, lightweight products for sporting goods, automotive, or many other applications. In this study, an experimental approach will be applied to fabricate a suitable biopolymer with a shape memory behavior and also investigate the impact of design and operational parameters on the functionality of 4D printed sandwich structures, especially, stress absorption rate and shape recovery behavior.
Nowadays, there is particular attention towards the additive manufacturing of medical devices and instruments. This is because of the unique capability of 3D printing technologies for designing and fabricating complex products like bone implants that can be highly customized for individual patients. NiTi shape memory alloys have gained significant attention in various medical applications due to their exceptional superelastic and shape memory properties, allowing them to recover their original shape after deformation. The integration of additive manufacturing technology has revolutionized the design possibilities for NiTi alloys, enabling the fabrication of intricately designed medical devices with precise geometries and tailored functionalities. The AM-SMART project is focused on exploring the suitability of NiTi architected structures for bone implants fabricated using laser powder bed fusion (LPBF) technology. This is because of the lower stiffness of NiTi alloys compared to Ti alloys, closely aligning with the stiffness of bone. Additionally, their unique functional performance enables them to dissipate energy and recover the original shape, presenting another advantage that makes them well-suited for bone implants. In this investigation, various NiTi-based architected structures will be developed, featuring diverse cellular designs, and their long-term thermo-mechanical performance will be thoroughly evaluated. The findings of this study underscore the significant potential of these structures for application as bone implants, showcasing their adaptability for use also beyond the medical sector.
