A Study of Cellular Decay in Austenite Alloys Using an Applied Eddy-Current Transducer

The eddy-current parameter f ₀ of the N36K10T3 invar has been studied in the range of aging temperatures from 600 to 900°C. The maximal drop in f ₀ has been observed at the temperature T _ag = 800°C, and the drop in this parameter was the larger, the longer the aging process. The drop in this parameter is caused by the cellular decay process in the solid solution, which depletes the austenite of nickel and titanium. The parameter f ₀ increases notably (from 4 to 46 kHz) when crystals of lowtemperature martensite (-phase) are generated in samples of the N26T3 steel with 100% cellular decay. This high value (f ₀ = 46 kHz) persists at T _ag < 400°C and drops by a factor of 4.5 over the interval 400 < T _ag < 600°C because the ferromagnetic -phase transforms to the paramagnetic phase-hardened austenite ( _ph). Aging of the phase-hardened austenite in the steel with cellular decay at T _ag = 700°C increases the parameter f ₀ by a factor of two (from 10 to 20 kHz) because the ferromagnetic -phase is generated when the aged phase-hardened austenite transforms to the martensite (_ph ) as a result of cooling the steel from the aging to room temperature.     

An Eddy-Current Study of Decay of Oversaturated Solid Solution in N32T3 Invar

The eddy-current parameter f ₀ has been studied as a function of temperature and aging time for the N32T3 Invar. Two initial states have been involved: those after quenching and after cold plastic deformation. The rise in the temperature and time of aging reduces the parameter f ₀ owing to the transition of the Invar from the ferromagnetic to paramagnetic state because the oversaturated solid solution decays, and the austenite is depleted of nickel. The parameter f ₀ drops monotonically with the applied strain. In the strained Invar the decay of the oversaturated solid solution reduces f ₀ in the temperature interval of 400 to 600°C.     

Effect of plastic strain and aging of N36K10T3 Invar on the output of an applied eddy-current transducer

Eddy-current parametersf ₀ andx ₀ as functions of the plastic strain in the N36K10T3 Invar have been studied. It has been proven that parametersf ₀ andx ₀ decrease monotonically as the strain degree rises to ∈=50%. Higher temperatures and longer times of aging (annealing) of the strained Invar lead to higherf ₀ andx ₀, whereas no changes in the eddy-current parameters have been detected in the case of an unstrained (quenched) Invar. Feasibility of deriving the strain, the temperature, and duration of isothermal aging of strained Invars with fcc lattices from the eddy-current parametersf ₀ andx ₀ has been demonstrated.     

Determining cutting speeds for the CO2 laser machining of decorative ceramic tile

This paper presents a comparison of theoretically predicted optimum cutting speeds for decorative ceramic tile with experimentally derived data. Four well-established theoretical analyses are considered and applied to the laser cutting of ceramic tile, i.e. Rosenthal's moving point heat-source model, and the heat-balance approaches of Powell, Steen and Chryssolouris. The theoretical results are subsequently compared and contrasted with actual cutting data taken from an existing laser machining database. Empirical models developed by the author are described which have been successfully used to predict cutting speeds for various thicknesses of ceramic tile.Notation A absorptivity - a thermal diffusivity (m²/s) - C specific heat (J/kgK) - d cutting depth (mm) - E _cut specific cutting energy (J/kg) - k thermal conductivity (W/mK) - J laser beam intensity (W/ m²) - L latent heat of vaporisation (J/kg) - l length of cut (mm) - n coordinate normal to cutting front - P laser power (W) - P _b laser power not interacting with the cutting front (W) - q heat input (J/s) - R radial distance (mm) - r beam radius (mm) - s substrate thickness (mm) - S _crit critical substrate thickness (mm) - T temperature (°C) - T _o ambient temperature (°C) - T _p peak temperature (°C) - T _s temperature at top surface (°C) - t time (s) - V cutting speed (mm/min) - V _opt optimum cutting speed (mm/min) - w kerf width (mm) - X, Y, Z coordinate location - x, y, z coordinate distance (mm) - conductive loss function - radiative loss function - convective loss function - angle between -coordinate andx-coordinate (rad) - coordinate parallel to bottom surface - angle of inclination of control surface w. r. t.X-axis (rad) - coupling coefficient - translated coordinate distance (mm) - density (kg/m³) - angle of inclination of control surface w.r.t.Y-axis (rad)     

Mechanical and Tribological Properties of Thin Remote Microwave Plasma CVD a-Si:N:C Films from a Single-Source Precursor

Silicon carbonitride (a-Si:N:C) films produced by remote plasma chemical vapor deposition (RP-CVD) were investigated. Tetramethyldisilazane as a single-source precursor and (H₂+N₂) upstream gas mixture for plasma generation were used. The influence of the upstream gas composition on the structure, density, mechanical and tribological properties of the films deposited on p-type Si (001) wafers (both heated—T _s =300°C and unheated—T _s =30°C) are reported. The H₂ RP-CVD process was found to result in the formation of outstanding low friction (0.04) and high hardness (H=27-31 GPa) a-Si:N:C films exhibiting promisingly high H/E values.     

A generalised model of milling forces

This paper presents the development of a generalised cutting force model for both end-milling and face-milling operations. The model specifies the interaction between workpiece and multiple cutter flutes by the convolution of cutting-edge geometry function with a train of impulses having the period equivalent to tooth spacing. Meanwhile, the effect of radial and axial depths of cut are represented by the modulation of the cutting-edge geometry function with a rectangular window function. This formulation leads to the development of an expression of end/face-milling forces in explicit terms of material properties, tool geometry, cutting parameters and process configuration. The explicitness of the resulting model provides a unique alternative to other studies in the literature commonly based on numerical integrations. The closed-form nature of the cutting force expression can facilitate the planning, optimisation, monitoring, and control of milling operations with complicated tool—work interactions. Experiments were performed over various cutting conditions and results are presented, in verification of the model fidelity, in both the angle and frequency domains.Notation * convolution operator - helix angle of an end mill - _A,_R axial and radial angles of a face mill - angular position of any cutting point in the cylindrical coordinate system - unit area impulse function - ^(i–1)(–T _o) (i–1)th derivative of (–T _o) with respect to - angular position of cutter in the negative Y-direction - _L, lead and inclination angles of a face mill - angular position of any cutting point in the negative Y-direction - ₁, ₂ entry and exit angles - upper limit of cutting edge function in terms of - as defined in equation (10) - A _xk ,A _yk ,A _zk kth harmonics of cutting forces in the X-, Y-, and Z-directions - d _a,d _r axial and radial depth of cut - dA instantaneous cut area - D diameter of cutter - f _o frequency of spindle - f _t,f _r,f _a local cutting forces in the tangential, radial, and axial directions - f _x ,f _y ,f _z local cutting forces in the X-, Y-, and Z-directions - F _x ,F _y ,F _z resultant cutting forces in the angle domain in the X-, Y-, and Z-directions - F as defined in equation (5) - h derivative of height function of cutting edge with respect to - h() height function of one cutting edge with respect to - H height of any cutting point - K _r,K _a radial-to-tangential and axial-to-tangential cutting force ratios - K _t tangential cutting pressure constant - K as defined in equation (6) - p as defined in equation (6) - N number of cutting edges - r() radius function of one cutting edge with respect to - R radius of any cutting point - T cutting engagement time function of any cutting point - T _o cutting engagement time of the cutting point at =0 - T th() tooth sequence function - t _c average cut thickness - t _x feed per tooth - W _A,W _W,W _C amplitude, width and centre of a window function - W(,) unit rectangular window function - y _min,y _max minimum and maximum positions of workpiece in the Y-direction - Z _min,Z _max integration limits in the Z-direction     

Effect of structure of aging metastable austenitic steels on the signal from an eddy-current transducer

Eddy-current parameterf ₀ of the N26T3 steel has been studied as a function of both the aging temperatureT _ag=20–800°C and the time τ of exposure to a constant temperature of 550 and 600°C up to 6h. In the initial state, the steel had two phases: (1) cooling-induced martensite+austenite (α+γ) or (2) strain-induced martensite+austenite (α′+γ). The parameterf ₀ drops monotonically as τ increases, and this drop is the faster, the higherT _ag. The parameterf ₀ changes nonmonotonically with the aging temperature. In addition to the initial two-phase structures, the one-phase γ structure has also been studied. The parameterf ₀ grows monotonically with the plastic cold strain and changes nonmonotonically with the aging temperature (20–800°C). Observed changes inf ₀ have been explained.     

Computer-aided selection of optimum machining parameters in multipass turning

In this paper a model and the interactive program system MECCANO2 for multiple criteria selection of optimal machining conditions in multipass turning is presented. Optimisation is done for the most important machining conditions: cutting speed, feed and depth of cut, with respect to combinations of the criteria, minimum unit production cost, minimum unit production time and minimum number of passes. The user can specify values of model parameters, criterion weights and desired tool life. MECCANO2 provides graphical presentation of results which makes it very suitable for application in an educational environment.Nomenclature a _min,a _max minimum and maximum depth of cut for chipbreaking [mm] - a _w maximum stock to be machined [mm] - C _a, _a, _a coefficient and exponents in the axial cutting force equation - C _r, _r, _r coefficient and exponents in the radial cutting force equation - C _T, , , coefficient and exponents in the tool life equation - C _v, _v, _v coefficient and exponents in the tangential cutting force equation - D _w maximum permissible radial deflection of workpiece [mm] - F _a axial cutting force [N] - F _b design load on bearings [N] - F _c clamping force [N] - F _k ^/* minimum value of criterionk, k=1, ...,n, when considered separately - f _m rotational flexibility of the workpiece at the point where the cutting force is applied [mm Nm^–1] - f _r radial flexibility of the workpiece at the point where the cutting force is applied [mm N^–1] - F _r radial cutting force [N] - F _tmax maximum allowed tangential force to prevent tool breakage [N] - F _v tangential cutting force [N] - k slope angle of the line defining the minimum feed as a function of depth of cut [mm] - l length of workpiece in the chuck [mm] - L length of workpiece from the chuck [mm] - L _c insert cutting edge length [mm] - M _g cost of jigs, fixtures, etc. [$] - M _o cost of labour and overheads [$/min] - M _u tool cost per cutting edge [$] - n number of criteria considered simultaneously - N _q, N_p minimum and maximum spindle speed [rev/min] - N _s batch size - N _z spindle speed for maximum power [rev/min] - P _a maximum power at the point where the power-speed characteristic curve changes (constant power range) [kW] - R tool nose radius [mm] - r workpiece radius at the cutting point [mm] - r _c workpiece radius in the chuck [mm] - s _min,s _max minimum and maximum feed for chipbreaking [mm] - T tool life [min] - T _a process adjusting time [min] - T _b loading and unloading time [min] - T _d tool change time [min] - T _des desired tool life [min] - T _h total set-up time [min] - T _t machining time [min] - V _rt speed of rapid traverse [m/min] - W volume of material to be removed [mm³] - W _k weight of criterionk, k=1, ...,n - x=[x ₁,x ₂,x ₃ ] ^T vector of decision variables - x ₁ cutting speed [m/min] - x ₂ feed [mm/rev] - x ₃ depth of cut [mm] - approach angle [rad] - _a coefficient of friction in axial direction between workpiece and chuck - _c coefficient of friction in circumferential direction between workpiece and chuck     

Effect of niobium interlayer on high-temperature sliding friction and wear of silver films on alumina

In this study, we investigated the effect of a thin Nb bonding layer (15–20 nm thick) on the high-temperature sliding friction and wear performance of Ag films ( 1.5 m thick) produced on -alumina (Al₂O₃) substrates by ion-beam assisted deposition (IBAD). The friction coefficients of Al₂O₃ balls against the Ag-coated Al₂O₃ flats were 0.32 to 0.5 as opposed to 0.8 to 1.1 against the uncoated flats. Furthermore, these Ag films reduced the wear rates of Al₂O₃ balls by factors of 25 to 2000, depending on test temperature. Wear of Ag-coated Al₂O₃ flats was hard to measure after tests at temperatures up to 400°C. At much higher temperatures (e.g., 600°C), these Ag films (without a Nb layer) were removed from the sliding surfaces and lost their effectiveness; however, Ag films with the Nb bonding layer remained intact on the sliding surfaces of the Al₂O₃ substrates even at 600°C and continued to impart low friction and low wear.     

Features of Electromagnetic-to-Acoustic Conversion in Thermally Processed 30KhGSA Steel

The paper reports on an experimental investigation of EMAC parameters characterizing the 30KhGSA steel as functions of temperature in samples processed at various tempering temperatures T _tem. The EMAC efficiency varies with temperature in a different manner in samples processed at different tempering temperatures. The character of correlations among EMAC parameters as functions of T _tem remains largely the same over the temperature range between the room temperature and 200°C. Features of EMAC have been studied in the regions of domain wall displacement and domain rotation. We have found a parameter that is a derivative of the E-effect and correlates with the impact toughness.     

Development of web-based machining chip breaking prediction systems

The prediction of chip breaking in machining is an important task of automated manufacturing. A chip breaking predictive model has been developed to predict the chip breaking behaviour in steel turning with grooved tools. The model is based on the chip breaking limits theory. A web-based chip breaking prediction system has been developed and presented in this paper with industrial application examples. With the system, the chip breaking range in steel turning with grooved tools can be predicted under different cutting conditions. The experimental data for turning different steel material over a wide range of feed rates, cutting speeds and tool geometry showed agreements with the model prediction. The user-friendly system is accessible through the Internet for the purposes of cutting condition design and tool selection. Also, the system can easily be extended to contain new cutting tools and new workpiece materials with a small number of cutting tests.Nomenclature f Feed rate (mm/rev) - d Depth of cut (mm) - V Cutting speed (m/min) - f_cr The critical feed rate (mm/rev) - d_cr The critical depth of cut (mm) - r Insert Nose radius (mm) - W_n Insert chip breaking groove width (mm) - Cutting edge angle (deg) - ₀ Insert rake angle (deg) - ₀₁ Insert land rake angle (deg) - b₁ Insert/chip restricted contact length (mm) - _s Insert inclination angle (deg) - h Insert backwall height (mm) - WP Work piece - DB database     

Temperature measurement in orthogonal metal cutting

Orthogonal cutting experiments were carried out on steel at different feedrates and cutting speeds. During these experiments the chip temperatures were measured using an infrared camera. The applied technique allows us to determine the chip temperature distribution at the free side of the chip. From this distribution the shear plane temperature at the top of the chip as well as the uniform chip temperature can be found. A finite-difference model was developed to compute the interfacial temperature between chip and tool, using the temperature distribution measured at the top of the chip.Nomenclature contact length with sticking friction behaviour [m] - c specific heat [J kg^–1 K^–1] - contact length with sliding friction behaviour [m] - F _P feed force [N] - F _V main cutting force [N] - h undeformed chip thickness [m] - h _c deformed chip thickness [m] - i,j denote nodal position - k thermal conductivity [W m^–2 K^–1] - L chip-tool contact length [m] - p defines time—space grid, Eq. (11) [s m^–2] - Q _C heat rate entering chip per unit width due to friction at the rake face [W m^–1] - Q _T total heat rate due to friction at the rake face [W m^–1] - Q _% percentage of the friction energy that enters the chip - q ₀ peak value ofq(x) [W m^–2] - q _e heat rate by radiation [W] - q(x) heat flux entering chip [W m^–2] - t time [s] - T temperature [K] - T _C uniform chip temperature [°C] - T _max maximum chip—tool temperature [°C] - T _mean mean chip—tool temperature [°C] - T _S measured shear plane temperature [°C] - x,y Cartesian coordinates [m] - V cutting speed [m s^–1] - V _C chip speed [m/s] - rake angle - ,, control volume lumped thermal diffusivity [m² s^–1] - emmittance for radiation - exponent, Eq. (3) - density [kg m^–3] - Stefan-Boltzmann constant [W m^–2 K⁴] - (x) shear stress distribution [N m^–2] - shear angle     

Alternative optimisation strategies and CAM software for multipass rough turning operations

The development of constrained optimisation analyses and strategies for selecting optimum cutting conditions in multipass rough turning operations based on minimum time per component criterion is outlined and discussed. It is shown that a combination of theoretical economic trends of single and multipass turning as well as numerical search methods are needed to arrive at the optimum solution. Numerical case studies supported the developed solution strategies and demonstrated the economic superiority of multipass strategies over single pass. Alternative approximate multipass optimisation strategies involving equal depth of cut per pass, single pass optimisation strategies and limited search techniques have also been developed and compared with the rigorous optimisation strategies. The approximate strategies have been shown to be useful, preferably for on-line applications such as canned cycles on CNC machine controllers, but recourse to the rigorous multipass strategies should be regarded as the reference for use in assessing alternative approximate strategies or for CAM support usage.Nomenclature d _i depth of cut for theith pass - d _opt optimum depth of cut - d _T total depth of cut to be removed - D _i workpiece diameter before theith pass - D _o,D _m initial and final workpiece diameter (afterm passes) - f _i feed for theith pass - f _max,f _min machine tool maximum and minimum feed - f _opt optimum cutting feed - f _sj, V_sj available feed and speed steps in a conventional machine tool - f _sgl, f_rec optimum and handbook recommended single pass cutting feeds - F _pmax maximum permissible cutting force - L workpiece length of cut - m continuous number of passes - m _H next higher integer number of passes from a givenm - m _HW upper limit to the optimum integer number of passesm _opt - m _L next lower integer number of passes from a givenm - m _LW lower limit to the optimum integer number of passesm _opt - m _o optimum (continuous) number of passes - m _opt optimum integer number of passes - N _a machine tool critical rotational speed whenP _a=P _max - N _max,N _min machine tool maximum and minimum rotational speed - n,n ₁,n ₂,K speed, feed and depth of cut exponents and constant in the extended Taylor's tool-life equation - P _a,P _max machine tool low speed and maximum power constraints - T _i tool-life using the cutting conditions for theith pass - T _L loading and unloading time per component - T _R tool replacement time - T _s tool resetting time per pass - T _T production time per component - T _TDi multi-passT _T equation with workpiece diameter effect - T _TDm, T_TDo multi-passT _T equations with constant diameterD _m andD _o, respectively - T _Topt overall optimum time per component - T _Tsgl optimum time per component for single pass turning - T _{T2re c} handbook recommended time per component - V _i cutting speed for theith pass - V _max,V _min machine tool maximum and minimum cutting speed - V _sgl,V _rec optimum and handbook recommended single pass cutting speeds - V _opt optimum cutting speed - a, E, W empirical constants in theP _a/F _pmax/P _max equations - , , feed, depth and speed exponents inF _pmax andP _max equations     

Multiple-objective optimisation of machining operations based on neural networks

Metal cutting plays an important role in manufacturing industries. Optimisation of cutting parameters represents a key component in machining process planning. In this paper, a neural network based approach to multiple-objective optimization of cutting parameters is presented. First, the problem of determining the optimum machining parameters is formulated as a multiple-objective optimization problem. Then, neural networks are proposed to represent manufacturers' preference structures. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.Nomenclature v cutting speed (m/min) - f feed rate per revolution (mm/rev) - d depth of cut per pass (mm) - T _p total operation time per part (min) - T _i set-up time per part (min) - T _c tool change time (min) - T _i idle time per part (min) - C _p cost per part ($) - C _t cost of tool per piece ($) - C _l labor cost per unit time ($/min) - C _o overhead per unit time ($/min) - V volume to be removed per part (mm³) - MRR metal removal rate (mm³/min) - TL tool life (min) - SR surface roughness (m) - H _p arithmetic centre-line average (m) - P cutting power (kW) - F cutting force (kg) - interface temperature (°C)     

Optimising tolerance allocation for mechanical components correlated by selective assembly

Selective assembly can enlarge the tolerances of mechanical components for easier manufacturing. However, the non-independent dimensions of correlated components make it difficult to optimise tolerance allocation for an assembly. This paper proposes a solution for this constrained optimisation problem consisting of tolerances and non-independent dimensions as design variables. The approach is to develop a simplified algorithm applying a Lagrange multiplier method to evaluate the optimal tolerances efficiently. The solution is shown to be a global optimum at the given correlation coefficients. The correlation coefficients are key elements in determining the optimal solution, which is demonstrated in the given examples. The results are helpful in designing tolerances for selective assembly.Notation A _j coefficient matrix off _j - B _i coefficient of cost function - C total manufacturing cost function - C _i manufacturing cost function forx _i - F _j thejth dimensional constraint function - f _j thejth quadratic constraint function - f quadratic constraint vector - H _j thejth Hessian matrix - J _kj element ofn×m Jacobian matrix - L Lagrangian - m number of assembly dimensions - n number of component dimensions - p number of equality dimensional constraints - T tolerance vector of component dimensions [mm] or [°] - tolerance ofx _i [mm] or [°] - tolerance ofZ _j [mm] or [°] - x component dimension vector - x midpoint vector - x _i component dimension [mm] or [°] - x _i midpoint ofx _i [mm] or [°] - Z _j assembly dimension [mm] or [°] - _j confidence coefficient forZ _j - _i confidence coefficient forx _i> - _j given design value ofZ _j [mm] or [°] - Lagrange multiplier vector - _j thejth Lagrange multiplier - * Lagrange multiplier vector at the optimum solution - correlation coefficient forx _i andx _k - _x standard deviation vector - _x ^* standard deviation vector at the optimum solution - _x ⁰ candidate point satisfying the constraintsf( _x ^* )=0 - standard deviation ofx _i      

Estimation of the quality of strengthening frictional treatment and subsequent tempering of eutectoid steel by the eddy-current method

The possibilities of the electromagnetic eddy-current method for evaluating the structural state and depth of strengthened layers on the surface of hardened and low-tempered steel U8 subjected to plastic straining using a hard-alloy indenter and subsequent heat treatment (tempering at T = 100–600°C) have been studied.     

Toroidal versus ball nose and flat bottom end mills

This paper compares the surface roughness along and across the feed directions produced by toroidal, ball nose, and flat bottom end mills. The study is conducted numerically and by cutting tests of aluminium. The results show that the toroidal cutter inherits the merits of the other two cutters; it produces small scallops across the feed direction, and low roughness along the feed direction.Nomenclature h scallop height - R _s radius of curvature of surface - inclination angle - 2a _c cross-feed - 2 subtended angle between the point of contact on the tool profile and the surface - R _a surface roughness - e offset distance of insert from tool axes for toroidal cutter - r _c cutter radius - r _i radius of insert for toroidal cutter - f _t feed per tooth - h _u undercut height - y, , intermediate variables     

Prediction of tool failure rate in turning hardened steels

This paper presents a stochastic model for predicting the tool failure rate in turning hardened steel with ceramic tools. This model is based on the assumption that gradual wear, chemical wear, and premature failure (i.e. chipping and breakage) are the main causes of ending the tool life. A statistical distribution is assumed for each cause of tool failure. General equations for representing tool-life distribution, reliability function, and failure rate are then derived. The assumed distributions are then verified experimentally. From the experimental results, the coefficients of these equations are determined. Further, the rate of failure is used as a characteristic signature for qualitative performance evaluation. The results obtained show that the predicted rate of ceramic tool failure is 20% (in the first few seconds of machining) and it increases with an increase in cutting speeds. These results indicate that there will always be a risk that the tool will fail at a very early stage of cutting. Such a possibility should not be overlooked when developing proper tool replacement strategies. Finally, the results also give the tool manufacturers information which can be used to modify the quality control procedures in order to broaden the use of ceramic tools.Nomenclature c constant - ch chamfer width of the tool, mm - d depth of cut, mm - h _i hardness value at theith location on the workpiece during machining - h mean ofh ₁,h ₂,h ₃, ...,h _nn - n hardness mean location - m Meyer exponent determined experimentally to define the nonlinear relation between the cutting force and the ratioh _i/h - f feedrate, mm rev^–1 - f(t) probability density function of tool failure - f ₁(t) probability density function of tool failure due to breakage caused by tool quality - f ₂(t) probability density function of tool failure due to breakage caused by workpiece condition - f ₃(t) probability density function of tool failure due to tool chipping caused by chemical wear - f ₄(t) probability density function of tool failure due to flank wear - f ₅(t) probability density function of tool failure due to crater wear - O() error - t cutting time, min - x ₁,x ₂,...,x _n independent variables - A _i instantaneous area of contact between the tool and the workpiece - C ₁ chip load, which can be determined as a function of the cutting conditions and tool geometry - K _I crater wear index - K _T maximum depth of crater wear on tool face, mm - K _M crater centre distance, mm - N number of failures - P(t) probability function of tool failure - P _j(t) corresponding probability of failure, such that 1j5 - R tool nose radius, mm - R(t) reliability function - R _j(t) corresponding reliability function, such that 1j5 - T _V estimate of tool life for a set value of average flank wear (V _B ^* ) - T _K estimate of tool life for a set value of maximum depth of crater wear (K _T ^* ) - V cutting speed, m/min - V _B average tool wear, mm - Z(t) instantaneous failure rate or hazard function - ₃ shape parameter in the Weibull probability density function - rake angle - ₃ scale parameter in the Weibull probability density function, min - failure rate of the cutting tool - mean of a logarithmic normal distribution function - standard deviation of a logarithmic normal distribution function - tool wear function - time corresponding to the occurrence of tool failure - (.) standard logarithmic normal distribution function     

Drill and clamping interface in high-performance drilling

The behaviour of a drill and a clamping unit was investigated in high-performance drilling. Some clamping units were characterised experimentally. In a series of experiments, the free-rotating drill behaviour, and the drilling events were investigated under high-performance conditions. A non-rotating measurement system, including proper procedures for signal processing, enabled the presentation of all measured values in terms and coordinates of the rotating tool. This led to a better understanding of the first-contact event, the penetration and the full drilling phases, as well as the influence of the clamping unit under different cutting conditions.Notation F impulse test exciting force [N] - Fz drilling axial force [N] - F _x F _y drilling lateral force components [N] - F _T drilling table speed (mm min^–1) - L drill overhang - T drilling torque [Nm] - X, Y, Z world coordinates [mm] - X _T,Y _T,Z _T rotating tool coordinates [mm] - _L hole location error [mm] - drill diameter [mm] - rotating angle [°] - R drill end circular movement fadius in world coordinates [mm] - X, Y drill end deflection in world coordinates [mm] - X _T, Y _T drill end deflection in world coordinates [mm] =2R     

Parameters for Roughness Pattern and Directionality

The average flow model is widely used on the derivations of average Reynolds type equation.There are arguments on the use of Peklenik number ( ^P ), or Bhushan number ( ^B ). In this paper, the orientation angle ( _r ) of the representative asperities (the pattern directionality) as well as the Peklenik number defined in principal directions () is utilized as the parameters to define the texture of surface roughness. An experimental procedure based on the least square method is then proposed to identify the two parameters ( _r and ). The present procedure avoids the above argument on distinguishing the isotropic asperity with the anisotropic asperity oriented with 45°. Only one additional parameter ( _r ) is needed.