cnc serendipity

<p>This thesis discusses the traditional use of (computer numerically controlled) CNC machining and the role of a designer to control the manipulation of (computer aided manufacturing) CAM software, CNC data and materials. The engaged designer has the capability to add qualities of digital tectonics onto a specified form through the process of working intimately with a CNC lathe. They experiment using abstract forms to find unique qualities that come from the cutting action of the tooling in a lathe. The designer takes on the role of the self-learner to become competent in the software, technology to apply complex textures and expressions. The designer can capitalise on unforeseen events, adds the action of craft to this industrial production method, creates beauty and provokes an emotional connection. Understanding the potential in the design possibility is to accept the serendipitous influences that can be controlled and the inevitable moments that cannot. The core of this research is to show how a designer claims authorship of their design at the making stage. They can define the margin of control and randomness, whether something has become too serendipitous, compromising the crafted form, or remained banal, repeating the precision machining, and releasing any character from the object. By finding the best design solution and replicating the same understanding a craftsperson has of their traditional tools. The designer observes, analyses, succeeds and fails, recognising the potential of their experimentation. Using Cross’s model of exploration, generation, evaluation and communication there is the strategy to see the unexpected, realise the potential and make it desirable. Learning the ability to manipulate digital surfaces and identify serendipitous qualities produced by the physical fingerprint of the machining process. Opposing the machines’ engineering, expressing the marks of the tool on an object, the imprints behaving as fingerprints left on a surface, is a unique characteristic. Something that makes the end user want to experience, feel, move and use it every day. These surprising results may influence the future of how design is conducted with digital technologies and adding digital complexities inspired by traditional craft to design more interesting artefacts.</p>

cnc serendipity

<p>This thesis discusses the traditional use of (computer numerically controlled) CNC machining and the role of a designer to control the manipulation of (computer aided manufacturing) CAM software, CNC data and materials. The engaged designer has the capability to add qualities of digital tectonics onto a specified form through the process of working intimately with a CNC lathe. They experiment using abstract forms to find unique qualities that come from the cutting action of the tooling in a lathe. The designer takes on the role of the self-learner to become competent in the software, technology to apply complex textures and expressions. The designer can capitalise on unforeseen events, adds the action of craft to this industrial production method, creates beauty and provokes an emotional connection. Understanding the potential in the design possibility is to accept the serendipitous influences that can be controlled and the inevitable moments that cannot. The core of this research is to show how a designer claims authorship of their design at the making stage. They can define the margin of control and randomness, whether something has become too serendipitous, compromising the crafted form, or remained banal, repeating the precision machining, and releasing any character from the object. By finding the best design solution and replicating the same understanding a craftsperson has of their traditional tools. The designer observes, analyses, succeeds and fails, recognising the potential of their experimentation. Using Cross’s model of exploration, generation, evaluation and communication there is the strategy to see the unexpected, realise the potential and make it desirable. Learning the ability to manipulate digital surfaces and identify serendipitous qualities produced by the physical fingerprint of the machining process. Opposing the machines’ engineering, expressing the marks of the tool on an object, the imprints behaving as fingerprints left on a surface, is a unique characteristic. Something that makes the end user want to experience, feel, move and use it every day. These surprising results may influence the future of how design is conducted with digital technologies and adding digital complexities inspired by traditional craft to design more interesting artefacts.</p>

Manufacturing Color-Blended Slub Yarn: Computer Numerically Controlled Ring-Spinning Frame and Kubelka-Munk Double Constant Theory

In the present work, we used the computer numerically controlled (CNC) ring-spinning frame to produce the color-blended slub yarns made from three different colored rovings with a 10% blending gradient. The blending ratio of rovings was determined by Kubelka-Munk (K-M) double constant theory. The as-manufactured yarns were made into knit fabrics with a circular knitting machine. The relative value method was used to solve the absorption and scattering coefficients of each colored fiber, and the least-square method was used to solve for the blending ratio of color-blended slub yarn. The results show that the average color difference between predicted and measured L*a*b* values was 1.74, and the error of the blending ratio was 7.38%, which are within the acceptable ranges for color matching of yarns.

Selecting the Safest CNC Machining Workshop Using AHP and TOPSIS Approaches

Machining operations on computer numerically controlled (CNC) machine tools are essential for the machining industry. Most of these operations take place in machining workshops. Safety issues in machining workshops shops can affect not only the health of the operators, which is extremely important, but also the productivity of the process and the accuracy of the parts. The research presented in this article addresses the issue of evaluating the safety of a CNC machining workshop, using a combined approach based on the analytic hierarchy process (AHP) and technique for order performance by similarity to ideal solution (TOPSIS) methods. A set of four evaluation criteria was proposed and the methods of processing the information for each criterion were used to extract the significant data needed for the evaluation. The proposed method was used to select the safest CNC machining workshop out of a total of three considered for assessment.

Technical Note: On the Production and Accuracy of CNC-Manufactured Hydraulic Scale Models

This Technical Note addresses the workflow for the production of hydraulic scale models using a Computer Numerically Controlled (CNC) production technique and investigates the possibilities to accurately reproduce topographical roughness features. Focusing on the construction of three scale models of unlined rock blasted tunnels, their accuracy is evaluated based on the comparison of differences between scaled prototype point clouds obtained by terrestrial laser scanning, spatially filtered meshes that served as input for the milling of the models, and digital twins of the constructed models that were created by Structure from Motion photogrammetry. The direct comparison between the point clouds and meshes as well as the comparison of derived statistical parameters show that the models could be reproduced with a high degree of accuracy. Observed deviations between the point clouds of the milled models and the milling meshes, as well as the scaled original point cloud, are identified and discussed in light of the production technique and the accuracy of the applied methods for the comparison.

Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling

The customized usage of tool inserts plays an imperative role in the economics of machining operations. Eventually, any in-process defects in the cutting tool lead to deterioration of complete machining activity. Such defects are untraceable by the conventional practices of condition monitoring. The characterization of such in-process tool defects needs to be addressed smartly. This would also assist the requirement of ‘self-monitoring’ in Industry 4.0. In this context, induction of supervised Machine Learning (ML) classifiers to design empirical classification models for tool condition monitoring is presented herein. The variation in faulty and fault-free tool condition is collected in terms of vibrations during the face milling process on CNC (Computer Numerically Controlled) machine tool. The statistical approach is incorporated to extract attributes and the dimensionality of the attributes is reduced using the J48 decision tree algorithm. The various conditions of tool inserts are then classified using two supervised algorithms viz. Bayes Net and Naïve Bayes from the Bayesian family.

Intelligent manufacturing of color blended yarn: Color matching algorithm and manufacturing process through computer numerically controlled ring spinning system

Yarn-dyed textiles complement digital printing textiles, which hold promise for high production and environmentally friendly energy efficiencies. However, the complicated structures of color-blended yarns lead to unpredictable colors in textile products and become a roadblock to developing nonpollution textile products. In the present work, we propose a framework of intelligent manufacturing of color blended yarn by combining the color prediction algorithm with a self-developed computer numerically controlled (CNC) ring spinning system. The S-N model is used for the prediction of the color blending effect of the ring-spun yarn. The optimized blending ratios of ring-spun yarn are obtained based on the proposed linear model of parameter W. Subsequently, the CNC ring-spinning frame is used to manufacture color-blended yarns, which can configure the constituent fibers in such a way that different sections of yarn exhibit different colors.