Yoder recommends printing the pattern on paper in between normal printer paper and cardstock in weight, making sure it folds in straight lines (not too thick), folds back and forth easily on the same line (not too thin), and is crisp enough to make a satisfying snapping noise when you shake it. Her favorite paper isSkytone, which is commonly used to print certificates and fancy envelopes. Once you have your crease pattern on a sheet of paper, cut out the hexagon that contains the pattern. Yoder recommends using a straightedge and blade on a cutting mat instead of scissors, whether that means an X-Acto knife and a ruler on a sheet of cardboard or a quilting ruler and rotary cutter on a fabric cutting mat. The next step is folding the background grid of black lines that the pattern uses as references. Assuming you've cut out your hexagon precisely, you can use the edge of the hexagon and the printed lines to make your creases, or you can fold as if there were no lines printed by folding the hexagon in half (edge to opposite edge) and then folding those edges in to the center to make quarter lines, first in one direction and then in the other two.

The applied research is the design and development of an automated folding and sewing machine for pleated pants. It represents a significant advancement in addressing the challenges associated with manual sewing processes. Traditional methods for creating pleats are labour-intensive, prone to inconsistencies, and require high levels of skill, making automation a critical need in the apparel industry. This research explores the technical feasibility and operational benefits of integrating advanced technologies into garment production, focusing on the creation of an automated machine capable of precise folding and sewing operations and eliminating the marking operation. The proposed machine incorporates key features such as a precision folding mechanism integrated into the automated sewing unit with real-time monitoring capabilities. The results demonstrate remarkable improvements: the standard labour time has been reduced by 93%, dropping from 117 seconds per piece to just 8 seconds with the automated system. Similarly, machinery time improved by 73%, and the total output rate increased by 72%. These enhancements translate into a cycle time reduction from 117 seconds per piece to an impressive 33 seconds, enabling manufacturers to meet customer demand more swiftly. By eliminating manual marking processes, the machine not only reduces labour costs but also minimizes waste through consistent pleat formation. This automation aligns with industry trends toward sustainability and efficiency, potentially reducing environmental impact by decreasing material waste and energy consumption.

Due to its rigid foldability and predictable kinematics, the reverse fold is the fundamental mechanism behind some of the most well known origami kinematic structures, including the Miura Ori, Yoshimura, and waterbomb patterns. However, the reverse fold only has one parameter to control its behavior: the starting fold angle. In this paper I introduce an alternative to the traditional reverse fold, based on the spring into action pattern, called the spring joint. This novel rigidly foldable mechanism is able to couple multiple reverse folds into a compact space to amplify the kinematic output of a traditional reverse fold by up to ten times, and to add one parameter for each reverse fold, giving more programmatic control of origami structures. Methods of parameterizing both the starting angle, the path of travel, and the axis of motion are also introduced. Unfortunately, this versatility comes at the cost of a large buildup of layers, making the spring joint impractical for thick origami mechanisms. To solve this problem, I also introduce a modular alternative to the spring joint that has no additional layers, with the same kinematic properties. Both of these mechanisms are tested as replacements for the reverse fold in both traditional and custom origami structures.

In the continuing theme of higher level tools to improve developing useful applications, today we'll visit feature engineering in a changing environment. Artificial intelligence (AI) is increasingly used to analyze data, and deep learning (DL) is one of the more complex aspects of AI. In multiple forums, I've discussed the need to move past heavy reliance on not just pure coding, but even past the basic frameworks discussed by DL programmers. One of the keys to the complexity is figuring out the right data attributes, or features, which matter to any system. As tricky as that is the first time, it needs to be a repeatable process, as environments change, and systems must change with them. Defining the initial feature set is important, but it's not the end of the game.

In the continuing theme of higher level tools to improve developing useful applications, today we'll visit feature engineering in a changing environment. Artificial intelligence (AI) is increasingly used to analyze data, and deep learning (DL) is one of the more complex aspects of AI. In multiple forums, I've discussed the need to move past heavy reliance on not just pure coding, but even past the basic frameworks discussed by DL programmers. One of the keys to the complexity is figuring out the right data attributes, or features, which matter to any system. As tricky as that is the first time, it needs to be a repeatable process, as environments change, and systems must change with them. Defining the initial feature set is important, but it's not the end of the game.