Development and Fabrication of an Innovative Smart Tool to Monitor the Impact Carving Process on Brittle Stones and Marble

The art of sculpting is related to the processing of brittle materials, such as granite, marble, and stone, and is implemented using percussive hand tools or rotational roughing tools. The outcome of percussion carving is still directly related to the technique, experience, and capacity of the sculptor. Any attempt to automate the art of sculpturing is exhausted in the subtraction method of brittle materials using a rotating tool. In the process of percussion carving, there is no equivalent expertise. In this work, we present the design, manufacturing (3D printing and CNC machining), and use of a smart, percussion carving tool, either manually by the hand of a sculptor, adjusted in a percussive pneumatic hammer, or guided by a digitally driven machine. The scope is to measure and record the technological variables and sizes that describe and document the carving process through the sensors and electronic devices that the smart tool incorporates, the development and programming of which was implemented for the purposes of this work. The smart carving tool was meticulously tested in various carving stones and stressing scenarios to test the functionality and efficacy of the tool. All the tests were successfully implemented according to the specifications set.

Multi-Stage Prediction of Feed System Time Series

In order to reduce the tracking error of the computer numerical control (CNC) feed system and improve the CNC machining accuracy, a novel prediction model is proposed based on fuzzy C-means robust variational echo state network. Firstly, the feed speed time series is clustered, and then reconstructed for different categories. The multi-stage robust prediction models are established to realize the multi-state robust prediction of the CNC machining feed velocity to reduce the tracking error of the feed system. Finally, the reference and actual time series with different feed speed are used to verify the established models. The results show that the proposed method can reduce the tracking error and realize the effective prediction of the time series of the feed system.

Investigation of non-imaging Fresnel lens prototyping with different manufacturing methods for solar energy application

Non-imaging Fresnel lenses have been playing an important role in improving the efficiency of the solar energy systems. Many researchers have been developing novel designs of Fresnel lenses to enhance the concentrator performance. To bring the complex design of a Fresnel lens from a conceptual theory to a real-life application while maintaining its efficiency, it is critical to find the optimum manufacturing method that achieves the best quality fabrication at low cost in the lab scale. This work will systematically investigate four advanced manufacturing methods for their lens-making capabilities, including pressure casting, hot embossing, 3D printing, and CNC machining. Six Fresnel lenses were fabricated by the four methods, which were tested in the lab by a solar simulator and a solar cell to demonstrate their performances. The CNC machining provides the best quality lab-scale Fresnel lens that enhances the solar cell efficiency by 118.3%. 3D printing and hot embossing methods are also promising for the fabrication of good performance lenses – increasing the solar cell efficiency by 40-70%. However, the 3D printed lens has the issue of material degradation on the long term. Although the pressure casting is the easiest manufacturing method, the performance of fabricated lens was the lowest.

The Application of Intelligent Manufacturing Technology in CNC Tools Design and Machining

CNC tool is a kind of cutting tools in industrial manufacturing. With the improvement of CNC machining accuracy and quality level, it puts forward more strict requirements for the performance of the cutting tool. Its manufacturing intelligence has become the inevitable choice for the development of the industry. In this paper, the key manufacturing technology of numerical control tools and the application of intelligence in numerical control tool manufacturing are described. the development trend of intelligent manufacturing of numerical control tools is analyzed.

Tribological Behavior and Analysis on Surface Roughness of CNC Milled Dual Heat Treated Al6061 Composites

Dual heat treatment (DHT) effect is analyzed using the machining of Al6061-T6 alloy, a readily available material for quickly finding the machining properties. The heat treatments are conducted twice over the specimen by the furnace heating before processing through CNC machining. The HSS and WC milling cutters are preferred for the diameter of 10 mm for the reviewed rotational speeds of 2000 rpm and 4000 rpm, and the constant depth of cut of 0.5 mm is chosen based on various reviews. Worthy roughness could be provided mostly by the influence of feed rates preferred here as 0.05 mm/rev and 0.1 mm/rev. The influencing factors are identified by the Taguchi, genetic algorithm (GA), and Artificial Neural Network (ANN) techniques and compared within it. The simulation finding also helps to clarify the relationship between influenced machining constraints and roughness outcomes of this project. The average values of heat treated and nonheat treated Al6061-T6 are compared and it is to be evaluated that 41% improvement is obtained with the lower surface roughness of 1.78975 µm and it shows good surface finish with the help of dual heat treatment process.

5-axis double-flank CNC machining of spiral bevel gears via custom-shaped tools—Part II: physical validations and experiments

Recently, a new methodology for 5-axis flank computer numerically controlled (CNC) machining, called double-flank machining, has been introduced (see “5-axis double-flank CNC machining of spiral bevel gears via custom-shaped milling tools—Part I: Modeling and simulation”). Certain geometries, such as curved teeth of spiral bevel gear, admit this approach where the machining tool has tangential contact with the material block on two sides, yielding a more efficient variant of flank machining. To achieve high machining accuracy, the path-planning algorithm, however, does not look only for the path of the tool, but also for the shape of the tool itself. The proposed approach is validated by series of physical experiments using an abrasive custom-shaped tool specifically designed for a particular type of a spiral bevel gear. The potential of this new methodology is shown in the semifinishing stage of gear manufacturing, where it outperforms traditional ball end milling by an order of magnitude in terms of machining time, while keeping, or even improving, the machining error.

Research on machining parameter optimization in finishing milling with multiple constraints

High efficiency and high precision milling, as the eternal goal of CNC machining, needs to balance many constraints for selecting the most reasonable processing parameters. This paper presents an efficient machining parameter optimization method for finishing milling operation with multiple constraints. Firstly, under the multiple constraints of parameter feasible region, milling force, milling stability, roughness, and machining contour accuracy, a multi-variable parameter optimization model with machining efficiency as the objective is established. A four level cycle optimization strategy has been detailly described for solving the optimization problem, in which the feed per tooth is optimized by using the golden section method, and with the aid of the random vector search method, the spindle speed, radial, and axial depth cuts are both numerically iterated. The optimal machining parameter combination of the tooth number, feed per tooth, spindle speed, radial, and axial depth of cuts are achieved at last. Finally, the experimental verification results show that the proposed method can greatly improve the machining efficiency under chatter free condition and achieve an efficient finishing milling with consideration of the multiple constraints.

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>

PREDICTION OF ENERGY CONSUMPTION IN THE LEADWELL V-40 IT CNC MACHINING CENTER THROUGH ARTIFICIAL NEURAL NETWORKS

Energy consumption in machining processes has become a problem for today's manufacturing industry. The use of neural networks and optimization algorithms for modeling and prediction of consumption as a function of the cut-off parameters in processes of this type has aroused the interest of research groups. The present work used artificial neural networks (ANN) to predict the energy consumption of a Leadwell V-40iT® five-axis CNC machining center, based on experimental data obtained through a factorial experimental design 53. ANN was programed in Matlab®. From the study was concluded that the depth per pass (Ap) is the variable that has the most influence on the prediction model of energy consumption with a 77% of relative importance, while the feed rate is the least relevant with 9% of importance.