Comparative analysis of cutting properties and nature of wear of carbide cutting tools with multi-layered nano-structured and gradient coatings produced by using of various deposition methods

The aim of this work was to investigate mechanical and cutting properties, as well as the nature of wear and failure of carbide cutting tools with modifying coatings of two types: nano-structured multi-layered coating Zr-ZrN-(ZrCrAl)N, applied through the use of the technology of filtered cathodic vacuum arc deposition, and multi-layered nano-structured and gradient coating Ti-(TiAl)N-(TiAl)N, applied through the use of the technology of LARC® (lateral rotating cathodes). It is found out that the both types of coatings under test significantly improve tool life of a carbide cutting tool. The studies of mechanisms of wear and failure of carbide tools with coatings under test, conducted at macro and micro levels, have identified their major differences and revealed their most preferable field of application. The carbide tools, equipped with cutting inserts with the nano-structured multi-layered coating under study, provided a significant increase in cutting properties (tool life) of the tool in comparison with the uncoated carbide tool and in comparison with the reference carbide tool with TiN coating. The tool with the coating Ti-(TiAl)N-(TiAl)N under study demonstrated the increased wear resistance during 30–35 min of cutting, and then, the process of coating failure and tool wear was sharply intensified. For the tool with coating Zr-ZrN-(ZrCrAl)N, the tests revealed more evenly balanced wear during the whole operating time between failures. It should be noted that NMCC Zr-ZrN-(ZrCrAl)N are substantially thinner, and that fact predetermines their better resistance to failure because of crack formation, and the technology of its generation is more cost-effective. © 2016 Springer-Verlag Londo

Considerations on the Castrop formula for calculation of intraocular lens power

Background: To explain the concept of the Castrop lens power calculation formula and show the application and results from a large dataset compared to classical formulae. Methods: The Castrop vergence formula is based on a pseudophakic model eye with 4 refractive surfaces. This was compared against the SRKT, Hoffer-Q, Holladay1, simplified Haigis with 1 optimized constant and Haigis formula with 3 optimized constants. A large dataset of preoperative biometric values, lens power data and postoperative refraction data was split into training and test sets. The training data were used for formula constant optimization, and the test data for cross-validation. Constant optimization was performed for all formulae using nonlinear optimization, minimising root mean squared prediction error. Results: The constants for all formulae were derived with the Levenberg-Marquardt algorithm. Applying these constants to the test data, the Castrop formula showed a slightly better performance compared to the classical formulae in terms of prediction error and absolute prediction error. Using the Castrop formula, the standard deviation of the prediction error was lowest at 0.45 dpt, and 95% of all eyes in the test data were within the limit of 0.9 dpt of prediction error. Conclusion: The calculation concept of the Castrop formula and one potential option for optimization of the 3 Castrop formula constants (C, H, and R) are presented. In a large dataset of 1452 data points the performance of the Castrop formula was slightly superior to the respective results of the classical formulae such as SRKT, Hoffer-Q, Holladay1 or Haigis

Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions

Today, cataract surgery is the most frequently performed ophthalmic surgery in the world. The cataract, a developing opacity of the human eye lens, constitutes the world's most frequent cause for blindness. During surgery, the lens is removed and replaced by an artificial intraocular lens (IOL). To prevent patients from needing strong visual aids after surgery, a precise prediction of the optical properties of the inserted IOL is crucial. There has been lots of activity towards developing methods to predict these properties from biometric eye data obtained by OCT devices, recently also by employing machine learning. They consider either only biometric data or physical models, but rarely both, and often neglect the IOL geometry. In this work, we propose OpticNet, a novel optical refraction network, loss function, and training scheme which is unsupervised, domain-specific, and physically motivated. We derive a precise light propagation eye model using single-ray raytracing and formulate a differentiable loss function that back-propagates physical gradients into the network. Further, we propose a new transfer learning procedure, which allows unsupervised training on the physical model and fine-tuning of the network on a cohort of real IOL patient cases. We show that our network is not only superior to systems trained with standard procedures but also that our method outperforms the current state of the art in IOL calculation when compared on two biometric data sets.Comment: 11 pages, 2 figure