A Smart Tool Holder Calibrated by Machine Learning for Measuring Cutting Force in Fine Turning and Its Application to the Specific Cutting Force of Low Carbon Steel S15C

Real-time monitoring of the cutting force in the machining process is critical for improving machining accuracy, optimizing the machining process, and optimizing tool lifetime; however, the dynamometers are too expensive to be widely used by machine tool users. Therefore, this paper presents a simple and cheap apparatus—a smart tool holder—to measure the cutting force of turning tools in the finishing turning. The apparatus does not change the structure of the turning tool. It consists of a tool holder and a piezoresistive force sensor foil, and transmits the signal through Bluetooth wireless communication. Instead of dealing with the circuit hardware, this paper uses the Artificial Neural Network (ANN) model to successfully calibrate the warm-up shift problem of the piezoresistive force sensor. Such a software method is simple, and considerably cheaper than the hardware method. For the force measurement capability of the smart tool holder, the cross-interference between orthogonal forces are very small and thus can be ignored. The force reading of the smart tool holder possesses high repeatability for the same turning parameters and high accuracy within the experiment groups. The authors apply the smart tool holder to cut the low carbon steel S15C, and to determine its specific cutting force in fine turning. The resulting fine turning force model agrees very well with the measurement. Its mean absolute deviation is 3.87% and its standard deviation is 1.55%, which reveals that the accuracy and precision of the smart tool holder and the fine turning force model are both good.

Application of methods of ion-vacuum modification of working surfaces of carbide tools to improve their performance

В статье описаны механизмы разрушения кромок твердосплавных инструментов под влиянием сил резания в процессе обработки заготовок и воздействия окружающей среды. Рассмотрены варианты разрушения твердосплавных инструментов при обработке без применения СОТС в зоне резания. Приведен анализ основных причин потери работоспособности инструментов из металлокерамических твердых сплавов на операциях чистового точения и растачивания. Приведен анализ многокомпонентных покрытий, в состав которых входят различные комбинации химических элементов, с точки зрения их применимости в различных условиях обработки с учетом имеющихся недостатков таких покрытий. Представлена структура многослойного покрытия, позволяющая увеличить работоспособность металлокерамических твердосплавных инструментов в процессе механической обработки заготовок. Приведены результаты комплексных теоретических и экспериментальных исследований с применением современных методов компьютерного моделирования, рентгеноструктурного анализа, оптической и просвечивающей электронной микроскопии, а так же механических испытаний. The article describes the mechanisms of destruction of the edges of carbide tools under the influence of cutting forces in the processing of workpieces and environmental influences. The options for the destruction of carbide tools during processing without the use of SOTS in the cutting zone are considered. The analysis of the main reasons for the loss of operability of tools made of cermet carbides in the operations of fine turning and boring is given. The analysis of multicomponent coatings, which include various combinations of chemical elements, from the point of view of their applicability in various processing conditions, taking into account the existing disadvantages of such coatings, is given. The structure of a multilayer coating is presented, which allows to increase the performance of cermet carbide tools in the process of machining workpieces. The results of complex theoretical and experimental studies using modern methods of computer simulation, X-ray diffraction analysis, optical and transmission electron microscopy, as well as mechanical tests are presented.

DEVELOPING LATHES POSTPROCESSOR GENERATING CYCLES OF PROCESSING GROOVES

This article explores the possibility of developing a post-processor generating circles for radial and facsimile grooves in PTC Creo environments using a generalized postprocessor G-POST and a specialized language FIL (Factory Interface Language). The proposed development approach can also be applied to post-processor generating cycles for rough and fine turning (G71, G72 and G70).

Image-Based Comprehensive Maintenance and Inspection Method for Bridges Using Deep Learning

Bridge management and maintenance work is an important part for the assessment the health state of bridge. The conventional management and maintenance work mainly relied on experienced engineering staffs by visual inspection and filling in survey forms. However, the human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. To address the drawbacks of human-based visual inspection method, this paper proposes an image-based comprehensive maintenance and inspection method for bridges using deep learning. To classify the types of bridges, a convolutional neural network (CNN) classifier established by fine-turning the AlexNet is trained, validated and tested using 3832 images with three types of bridges (arch, suspension and cable-stayed bridge). For the recognition of bridge components (tower and deck of bridges), a Faster Region-based Convolutional Neural Network (Faster R-CNN) based on modified ZF-net is trained, validated and tested by utilizing 600 bridge images. To implement the strategy of a sliding window technique for the crack detection, another CNN from fine-turning the GoogLeNet is trained, validated and tested by employing a databank with cropping 1455 raw concrete images into 60000 intact and cracked images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results substantiate the proposed method can indeed recognize the types and components and detect cracks for a bridges.

Effect of heating rates on the microstructure and gas permeation properties of carbon membranes

High performance tubular carbon membrane (TCM’s) for CO2 separation were prepared by controlling the carbonization heating rates in range of 1-7 oC/min carbonized at 800 oC under Argon environment. A single permeation apparatus was used to determine the gas permeation properties of the membrane at room temperature. Fine turning of the carbonization condition was necessary to obtain the desired permeation properties. The preparation of PI/NCC-based TCM at low heating rate caused the gas permeance for the examined gas N2 and CO2 decreased whereas the selectivity of CO2/N2 increased. It was also identified that the gas permeation properties of the resultant TCM and its structure was highly affected by the heating rate. The best carbonization heating rate was found at 3oC/min for the fabrication of TCM derived via polymer blending of PI/NCC for CO2/N2 separation.