This research aims to establish a methodology for machining of toric lenses, using fast tool servo-assisted single point diamond turning and to assess the generated surface for its characteristics. Using the established mathematical model, toric surface is explained to understand the geometry and to generate the parameters required for fast tool servo machining. A toric surface with a major diameter of 18.93 mm and a minor diameter of 15.12 mm has been cut on the intelligent ultra-precision turning machine (iUPTM). The surface profile and surface roughness were measured. After analysing the measurement data of the machined surface, on two perpendicular axes of the toric lens, form accuracy of 0.49 µm peak-to-valley (PV), and surface roughness of 12 nm in Ra, 4–8 nm in Sa are obtained. From the experimental results obtained, it can be concluded that the proposed method is a reasonable alternative for manufacturing toric lens mould.
Accuracy & precision are the main requirements for ultra precision machine tools. Many factors affect the performance of the system that in turns affect the product quality. Among all sources of errors, the thermo mechanical deformation errors are the main contributor for the overall geometrical errors. This paper mainly aims at establishment of methodology to compensate thermal deformation errors in real-time for ultra precision machine tools. The real-time thermal error compensation module has been developed and integrated to intelligent Ultra Precision Turning machine. The module includes temperatures as inputs, neural network algorithm for computing the thermal deformations errors, 'C' programming for real-time calculations and integration with open architecture CNC controller. The module runs in silent mode which avoids human intervention for correction of thermal deformation errors.
The detection and characterization of cracks prior to damage is a technologically and economically highly significant task and is of very importance when it comes to safety-relevant structures. The evaluation of a components life is closely related to the presence of cracks in it. Laser thermography has already high capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples. Crack detection in metallic samples at high temperature is highly significant in present manufacturing scenario. During the casting process of billets, surface cracks form, due to the suboptimal cooling rates. These cracks reduce value of the billet and must be removed using machining process after cooling. This secondary process increases cost of manufacturing. In this work we developed a heat transfer model for laser thermography to study the thermal contrast variation with increase in surface temperature using finite element method (FEM). Here we are mainly concentrating the capability of the scanning laser thermography in crack detection which are in elevated temperature and numerical modeling study of thermal contrast variation of crack with respect increase in metal surface temperature. This study is important to prove the capability of laser thermography for crack detection in elevated temperature. Since we are using High power CW Laser to local heating of the metal surface which can give relatively high thermal contrast even at elevated temperature compare to other heating source. Here we are modeled and simulated 2D laser scanning across a surface breaking crack and developed an algorithm to produce the vicinity of crack. The algorithm we developed applied for various surface temperature data. And validated the credibility of the algorithm with experimental data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.