High Speed Machining of Inconel 718: Tool Wear and Surface Roughness Analysis ☆

Abstract Inconel 718 is a Nickel based Heat Resistance Super Alloy (HRSA) widely used in many aerospace applications. It possesses good properties like corrosion resistance, high strength and exceptional weldability. It is considered as one of the most difficult to cut alloy. Recently many researcher have focus in employing many machining strategies to improve machinability of Inconel 718. This paper presents High Speed Machining (HSM) of Inconel 718. Turning trials are conducted at various speed ranging from low to high (60 m/min, 90 m/min, 190 m/min, 255 m/min). Tool wear and surface roughness, which are two major aspects of machinability, have been discussed in this investigation

emergent methodology for solving tool inventory sizing problems in a complex production system

Abstract Based on recently established correlations between emergent synthesis classes, a Class III synthesis problem concerning tool inventory management in a complex make-to-order manufacturing environment is addressed. Such environment is shown to be affected by significant non-random uncertainty involving tool delivery time fluctuations and unpredictable tool demand. The trade-off typical of the inventory sizing dilemma is introduced with reference to reusable tools, such as grinding wheels, and a satisfactory solution is achieved by means of a dynamic purpose assignment approach. This leads to a global behavior, expressed by a recurrently oscillating pattern, affecting the inventory level trend in the nearby of a peculiar attraction band: the oscillation amplitude mainly depends on the attractor's bandwidth as well as on the peaks attained by the tool demand rate during the tool management period

Abrasive Grains Micro Geometry: A Comparison between Two Acquisition Methods

Abstract One of the aspects that makes difficult grinding processes modelling is the non-deterministic nature of the cutting tool, in particular the abrasive grains of the grinding wheel have a random distribution and an undefined geometry that influences the grinding forces. In order to develop a reliable 3D model of the grinding process the actual microgeometry of abrasive grains must be acquired. This paper compares the results of two different acquisition methods: the geometry acquired via a laser non-contact instrument is confronted with the one acquired using a computer tomography; the accuracy of the grain micro geometry provided by the two approaches is discussed

Cutting force sensor signals processing for chip form monitoring during turning of carbon steel

Cutting force sensor signals are used for chip form monitoring during longitudinal turning of carbon steel with coated carbide inserts, producing different chip forms. Advanced signal analysis was carried out by spectral valuation through a parametric method and feature extraction from the frequency spectrum. In this methodology, the signal spectrum is assumed to take on a specific functional form, the parameters of which are unknown. The spectral estimation problem becomes one of estimating the unknown parameters of the spectrum model, rather than the spectrum itself. A group of features characteristic of the spectrum model were obtained by linear predictive analysis from the cutting force signal. The analysis of these features was carried out by pairwise plotting in a 2D feature space and neural network processing in a higher dimension feature space in order to identify the chip form through a cutting force sensor monitoring methodology

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Grinding wheel wear, which is a very complex phenomenon, causes changes in most of the shapes and properties of the tool during machining, reducing the efficiency of the grinding operation and impairing workpiece quality. Therefore, monitoring the condition of the tool during the grinding process plays a key role in the quality of workpieces being manufactured. In this study, diamond tool wear was estimated during the grinding of advanced ceramics using intelligent systems composed of four types of neural networks. Experimental tests were performed on a surface grinding machine and tool wear was measured by the imprint method throughout the tests. Acoustic emission and cutting power signals were acquired during the tests and statistics were obtained from these signals. Training and validating algorithms were developed for the intelligent systems in order to automatically obtain the best estimation models. The combination of signals and statistics along with the intelligent systems brings an innovative aspect to the grinding process. The results indicate that the models are highly successful in estimating tool wear. © 2015 Elsevier Ltd. All rights reserved

Prediction of dressing in grinding operation via neural networks

In order to obtain a modelling and prediction of tool wear in grinding operations, a Cognitive System has been employed to observe the dressing need and its trend. This paper aims to find a methodology to characterize the condition of the wheel during grinding operations and, by the use of cognitive paradigms, to understand the need of dressing. The Acoustic Emission signal from the grinding operation has been employed to characterize the wheel condition and, by the feature extraction of such signal, a cognitive system, based on Artificial Neural Networks, has been implemented

Euclid. I. Overview of the Euclid mission

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance