Investigation of chip formation and fracture toughness in orthogonal cutting of UD-CFRP

Features of chip formation can inform the mechanism of a machining process. In this paper, a series of orthogonal cutting experiments were carried out on unidirectional carbon fiber reinforced polymer (UD-CFRP) under cutting speed of 0.5 m/min. The specially designed orthogonal cutting tools and high-speed camera were used in this paper. Two main factors are found to influence the chip morphology, namely the depth of cut (DOC) and the fiber orientation (angle 휃), and the latter of which plays a more dominant role. Based on the investigation of chip formation, a new approach is proposed for predicting fracture toughness of the newly machined surface and the total energy consumption during CFRP orthogonal cutting is introduced as a function of the surface energy of machined surface, the energy consumed to overcome friction, and the energy for chip fracture. The results show that the proportion of energy spent on tool-chip friction is the greatest, and the proportions of energy spent on creating new surface decrease with the increasing of fiber angle.

Cutting forces and wear analysis of Si3N4 diamond coated tools in high speed machining

Si3N4 tools were coated with a thin diamond film using a Hot-Filament Chemical Vapour Deposition (HFCVD) reactor, in order to machining a grey cast iron. Wear behaviour of these tools in high speed machining was the main subject of this work. Turning tests were performed with a combination of cutting speeds of 500, 700 and 900 m min−1, and feed rates of 0.1, 0.25 and 0.4 mm rot−1, remaining constant the depth of cut of 1 mm. In order to evaluate the tool behaviour during the turning tests, cutting forces were analyzed being verified a significant increase with feed rate. Diamond film removal occurred for the most severe set of cutting parameters. It was also observed the adhesion of iron and manganese from the workpiece to the tool. Tests were performed on a CNC lathe provided with a 3-axis dynamometer. Results were collected and registered by homemade software. Tool wear analysis was achieved by a Scanning Electron Microscope (SEM) provided with an X-ray Energy Dispersive Spectroscopy (EDS) system. Surface analysis was performed by a profilometer.

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

Recurrence Quantification Analysis of System Signals for Detecting Tool and Chatter in Turning

In this thesis, the applications of the recurrence quantification analysis in metal cutting operation in a lathe, with specific objective to detect tool wear and chatter, are presented.This study is based on the discovery that process dynamics in a lathe is low dimensional chaotic. It implies that the machine dynamics is controllable using principles of chaos theory. This understanding is to revolutionize the feature extraction methodologies used in condition monitoring systems as conventional linear methods or models are incapable of capturing the critical and strange behaviors associated with the metal cutting process.As sensor based approaches provide an automated and cost effective way to monitor and control, an efficient feature extraction methodology based on nonlinear time series analysis is much more demanding. The task here is more complex when the information has to be deduced solely from sensor signals since traditional methods do not address the issue of how to treat noise present in real-world processes and its non-stationarity. In an effort to get over these two issues to the maximum possible, this thesis adopts the recurrence quantification analysis methodology in the study since this feature extraction technique is found to be robust against noise and stationarity in the signals.The work consists of two different sets of experiments in a lathe; set-I and set-2. The experiment, set-I, study the influence of tool wear on the RQA variables whereas the set-2 is carried out to identify the sensitive RQA variables to machine tool chatter followed by its validation in actual cutting. To obtain the bounds of the spectrum of the significant RQA variable values, in set-i, a fresh tool and a worn tool are used for cutting. The first part of the set-2 experiments uses a stepped shaft in order to create chatter at a known location. And the second part uses a conical section having a uniform taper along the axis for creating chatter to onset at some distance from the smaller end by gradually increasing the depth of cut while keeping the spindle speed and feed rate constant.The study concludes by revealing the dependence of certain RQA variables; percent determinism, percent recurrence and entropy, to tool wear and chatter unambiguously. The performances of the results establish this methodology to be viable for detection of tool wear and chatter in metal cutting operation in a lathe. The key reason is that the dynamics of the system under study have been nonlinear and the recurrence quantification analysis can characterize them adequately.This work establishes that principles and practice of machining can be considerably benefited and advanced from using nonlinear dynamics and chaos theory.

Permutation Entropy Based Analysis of Complex Signals for Characterising Change in System Dynamics

Timely detection of sudden change in dynamics that adversely affect the performance of systems and quality of products has great scientific relevance. This work focuses on effective detection of dynamical changes of real time signals from mechanical as well as biological systems using a fast and robust technique of permutation entropy (PE). The results are used in detecting chatter onset in machine turning and identifying vocal disorders from speech signal.Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. Here we propose the use of permutation entropy (PE), to detect the dynamical changes in two non linear processes, turning under mechanical system and speech under biological system.Effectiveness of PE in detecting the change in dynamics in turning process from the time series generated with samples of audio and current signals is studied. Experiments are carried out on a lathe machine for sudden increase in depth of cut and continuous increase in depth of cut on mild steel work pieces keeping the speed and feed rate constant. The results are applied to detect chatter onset in machining. These results are verified using frequency spectra of the signals and the non linear measure, normalized coarse-grained information rate (NCIR).PE analysis is carried out to investigate the variation in surface texture caused by chatter on the machined work piece. Statistical parameter from the optical grey level intensity histogram of laser speckle pattern recorded using a charge coupled device (CCD) camera is used to generate the time series required for PE analysis. Standard optical roughness parameter is used to confirm the results.Application of PE in identifying the vocal disorders is studied from speech signal recorded using microphone. Here analysis is carried out using speech signals of subjects with different pathological conditions and normal subjects, and the results are used for identifying vocal disorders. Standard linear technique of FFT is used to substantiate thc results.The results of PE analysis in all three cases clearly indicate that this complexity measure is sensitive to change in regularity of a signal and hence can suitably be used for detection of dynamical changes in real world systems. This work establishes the application of the simple, inexpensive and fast algorithm of PE for the benefit of advanced manufacturing process as well as clinical diagnosis in vocal disorders.

Observations of the depth of ice particle evaporation beneath frontal cloud to improve NWP modelling

The evaporation (sublimation) of ice particles beneath frontal ice cloud can provide a significant source of diabatic cooling which can lead to enhanced slantwise descent below the frontal surface. The strength and vertical extent of the cooling play a role in determining the dynamic response of the atmosphere, and an adequate representation is required in numerical weather-prediction (NWP) models for accurate forecasts of frontal dynamics. In this paper, data from a vertically pointing 94 GHz radar are used to determine the characteristic depth-scale of ice particle sublimation beneath frontal ice cloud. A statistical comparison is made with equivalent data extracted from the NWP mesoscale model operational at the Met Office, defining the evaporation depth-scale as the distance for the ice water content to fall to 10% of its peak value in the cloud. The results show that the depth of the ice evaporation zone derived from observations is less than 1 km for 90% of the time. The model significantly overestimates the sublimation depth-scales by a factor of between two and three, and underestimates the local ice water content by a factor of between two and four. Consequently the results suggest the model significantly underestimates the strength of the evaporative cooling, with implications for the prediction of frontal dynamics. A number of reasons for the model discrepancy are suggested. A comparison with radiosonde relative humidity data suggests part of the overestimation in evaporation depth may be due to a high RH bias in the dry slot beneath the frontal cloud, but other possible reasons include poor vertical resolution and deficiencies in the evaporation rate or ice particle fall-speed parametrizations.

Operator performance evaluation of controlled depth of field in a stereographically displayed virtual environment

Retinal blurring resulting from the human eye's depth of focus has been shown to assist visual perception. Infinite focal depth within stereoscopically displayed virtual environments may cause undesirable effects, for instance, objects positioned at a distance in front of or behind the observer's fixation point will be perceived in sharp focus with large disparities thereby causing diplopia. Although published research on incorporation of synthetically generated Depth of Field (DoF) suggests that this might act as an enhancement to perceived image quality, no quantitative testimonies of perceptional performance gains exist. This may be due to the difficulty of dynamic generation of synthetic DoF where focal distance is actively linked to fixation distance. In this paper, such a system is described. A desktop stereographic display is used to project a virtual scene in which synthetically generated DoF is actively controlled from vergence-derived distance. A performance evaluation experiment on this system which involved subjects carrying out observations in a spatially complex virtual environment was undertaken. The virtual environment consisted of components interconnected by pipes on a distractive background. The subject was tasked with making an observation based on the connectivity of the components. The effects of focal depth variation in static and actively controlled focal distance conditions were investigated. The results and analysis are presented which show that performance gains may be achieved by addition of synthetic DoF. The merits of the application of synthetic DoF are discussed.

High-power diode laser in the disinfection in depth of the root canal dentin

Objective. The objective of this study was to evaluate the disinfection degree of dentine caused by the use of diode laser after biomechanical procedures. Study design. Thirty teeth were sectioned and roots were autoclaved and incubated for 4 weeks with a suspension of Enterococcus faecalis. The specimens were randomly divided into 3 groups (n = 10): G1, instrumented with rotary files, irrigated with 0.5% sodium hypochlorite and 17% EDTA-T, and then irradiated by 830-nm diode laser at 3 W; G2, the same procedures as G1 but without laser irradiation; and G3, irrigation with saline solution (control). Dentin samples of each third were collected with carbide burs and aliquots were sowed to count viable cells. Results. The disinfection degree achieved was 100% in G1 and 98.39% in G2, when compared to the control group (G3). Conclusion. Diode laser irradiation provided increased disinfection of the deep radicular dentin in the parameters and samples tested.

EPR in the Characterization of the Shade Effect on Translucence, Remaining Free Radicals, and Polymerization Depth of Commercially Available Resin Composites

The aim of this study was to assess the relation between the number of free radicals generated and the polymerization depth in two different commercial brands of resin composites with different colors and translucence. Electron paramagnetic resonance quantified the radical populations through relative intensity (I (r)) of free radicals generated, and radical decay was monitored. Sample translucence and the classical polymerization depth were measured. The analysis indicated that resin with more color pigments (MA4, I (r) = 0.73 a.u) or more opacity components (ODA2, I (r) = 0.84 a.u) generated smaller populations of free radicals and have the lower polymerization depth than clearer (M, I (r) = 1.20 a.u and MA2, I (r) = 1.02) or more translucent (OEA2, I (r) = 1.00 a.u) composites for the same light-curing time. It seems that irradiation doses have to be adequate to more colored and less translucent resins.

A depth of data : research messages on the state of social work education in England

In the light of extensive media coverage of social work education, this article uses information from the Department of Health funded three-years multi-method evaluation of the social work degree qualification in England to discuss areas in which qualifying education might be improved. It argues that too great a concern with the 'A' level performance of social work applicants risks not paying enough attention to the non-academic qualities that they will need to work in the changing world of children's and adult services. Better partnership working between employers and universities will help students make the transition into the workplace. This includes greater opportunities for employers and practitioners to be involved in candidate selection and teaching on qualifying programmes.

Wear characteristics of ultra-hard cutting tools when machining austempered ductile iron

Nodularised Ductile Cast Iron, when subjected to heat treatment processes - austenitising and austempering produces Austempered Ductile Iron (ADI). The microstructure of ADI also known as "ausferrite" consists of ferrite, austenite and graphite nodules. Machining ADI using conventional techniques is often a problematic issue due to the microstructural phase transformation from austenite to martensite during machining. This paper evaluates the wear characteristics of ultra hard cutting tools when machining ADI and its effect on machinability. Machining trials consist of turning ADI (ASTMGrade3) using two sets of PCBN tools with 90% and 50% CBN content and two sets of ceramics tools; Aluminium Oxide Titanium Carbide and Silicon Carbide - whisker reinforced Ceramic. The cutting parameters chosen are categorized as roughing and finishing conditions; the roughing condition comprises of constant cutting speed (425 m/min) and depth of cut (2mm) combined with variable feed rates of 0.1, 0.2, 0.3 and 0.4mm/rev. The finishing condition comprises of constant cutting speed (700 m/min) and depth of cut (0.5mm) combined with variable feed rates of 0.1, 0.2, 0.3 and 0.4mm/rev. The benchmark condition to evaluate the performance of the cutting tools was tool wear evaluation, surface texture analysis and cutting force analysis. The paper analyses thermal softening of the workpiece by the tool and its effect on the shearing mechanism under rough and finish machining conditions in term of lower cutting forces and enhanced surface texture of the machined part.

Compute inclusion depth of a pattern

We define a concept of inclusion depth (see Definition 1) to capture mind-change complexity [3,1] of pattern identification problems [2]. Our basic question is whether the inclusion depth for any pattern is computable. We conjecture a combinatorial characterization that, if true, leads to a linear time algorithm to compute inclusion depth.

A case study on effect of feed rate on machinability of austempered ductile iron

Austempered Ductile Iron (ADI) is a type of nodular, ductile cast iron subjected to heat treatments - austenitising and austempering. Whilst machining is conducted prior to heat treatment and offers no significant difficulty, machining post heat treatment is demanding and often avoided. Phase transformation of retained austenite to martensite leading to poor machinability characteristics is a common problem experienced during machining. This case study explains the effect of feed rate on machinability of ADI using cutting force analysis and tool failure analysis. The experimental design consists of conducting drilling trials on grade 1200 and 1400 at constant depth of cut, 25mm; constant speed, 45m/min; no coolant and variable feed rates from 0.2 to 0.35 mm/rev (increment of 0.025mm/rev). Metallography and X-ray diffraction technique was carried out in order to identify and quantify the microstructural phases before and after drilling. The results from the trial infer that the best way to machine ADI efficiently without tool failure is using low feeds and high speeds and without coolant.

Machinability and phase transformation study of nanobainite steels

There is an increasing demand for high strength materials with the development of technology and critical applications. Nano materials are newly developed materials with extremely high strength for this purpose. Nanobainite is a dual phase material containing alternate layers of bainitic ferrite in nano dimensions and retained austenite. Nanobainite is produced by isothermally holding austenitized steel at a temperature of 200°C or less, depending on the chemical composition, for 6 10 days until bainite forms and then cooling to room temperature using austempering. The experimental design consisted of face milling under 12 combinations of Depth of Cut (DOC)-1, 2 and 3mm; cutting speed-100 and 150m/min; constant feed-0.15mm/rev and coolant on/off. The machinability of the material is assessed by means of analysis, such as surface texture and microhardness. The assessment also involves microstructural comparisons before and after milling. Future work involves quantifying the microstructural phase before and after milling using XRD. The results obtained are used to assess the most favorable condition to cut this new variety of steel.