Improved calculation of the cutting force in end milling

Determination of the cutting force in end milling on the basis of the Johnson–Cook phenomenological model is described. The numerical results obtained by this method are compared with experimental data.     

A new layered manufacturing machine with cutting force analysis in tapered end milling

We introduce a new layered manufacturing machine with its associated geometric cutting force model. This machine is designed to build metal parts and is a modified type of commercial layered object manufacturing (LOM) machine. This apparatus uses tapered end mills with different angles instead of laser to cut the layer edges. The proposed machine needs higher stiffness, because cutting action would regularly create more inaccuracy. Consequently, a new force model is considered that takes into account cutting conditions, cutter geometry and combination of tool-workpiece materials. Then, by force and stress analysis cutting-force induced inaccuracy has been evaluated for different components of the machine. Finally, the effectiveness of the machine and its capability to build functional metal parts with different geometries is clearly shown.     

Chatter and dynamic cutting force prediction in high-speed ball end milling

Machine tool chatter is a serious problem which deteriorates surface quality of machined parts and increases tool wear, noise, and even causes tool failure. In the present paper, machine tool chatter has been studied and a stability lobe diagram (SLD) has been developed for a two degrees of freedom system to identify stable and unstable zones using zeroth order approximation method. A dynamic cutting force model has been modeled in tangential and radial directions using regenerative uncut chip thickness. Uncut chip thickness has been modeled using trochoidal path traced by the cutting edge of the tool. Dynamic cutting force coefficients have been determined based on the average force method. Several experiments have been performed at different feed rates and axial depths of cut to determine the dynamic cutting force coefficients and have been used for predicting SLD. Several other experiments have been performed to validate the feasibility and effectiveness of the developed SLD. It is found that the proposed method is quite efficient in predicting the SLD. The cutting forces in stable and unstable cutting zone are in well agreement with the experimental cutting forces.     

Specific cutting force coefficients modeling of end milling by neural network

In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on. The cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling processes for various cutting conditions, their mathematical model is important and the model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging forces of cutting tests. In this paper the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in the learning stage as the omitted number of experimental data increase the average errors increase as well.     

Analysis of cutting force coefficients in high-speed ball end milling at varying rotational speeds

In high-speed ball end milling, cutting forces influence machinability, dimensional accuracy, tool failure, tool deflection, machine tool chatter, vibration, etc. Thus, an accurate prediction of cutting forces before actual machining is essential for a good insight into the process to produce good quality machined parts. In this article, an attempt has been made to determine specific cutting force coefficients in ball end milling based on a linear mechanistic model at a higher range of rotational speeds. The force coefficients have been determined based on average cutting force. Cutting force in one revolution of the cutter was recorded to avoid the cutter run-out condition (radial). Milling experiments have been conducted on aluminum alloy of grade Al2014-T6 at different spindle speeds and feeds. Thus, the dependence of specific cutting force coefficients on cutting speeds has been studied and analyzed. It is found that specific cutting force coefficients change with change in rotational speed while keeping other cutting parameters unchanged. Hence, simulated cutting forces at higher range of rotational speed might have considerable errors if specific cutting force coefficients evaluated at lower rotational speed are used. The specific cutting force coefficients obtained analytically have been validated through experiments.     

Speed-varying cutting force coefficient identification in milling

Accurate simulation of the machining process is crucial to improve milling performance, especially in High-Speed Milling, where cutting parameters are pushed to the limit.Various milling critical issues can be analyzed based on accurate prediction of cutting forces, such as chatter stability, dimensional error and surface finish. Cutting force models are based on coefficients that could change with spindle speed. The evaluation of these specific coefficients at higher speed is challenging due to the frequency bandwidth of commercial force sensors. On account of this, coefficients are generally evaluated at low speed and then employed in models for different spindle speeds, possibly reducing accuracy of results.In this paper a deep investigation of cutting force coefficient at different spindle speeds has been carried out, analyzing a wide range of spindle speeds: to overcome transducer dynamics issues, dynamometer signals have been compensated thanks to an improved technique based on Kalman filter estimator. Two different coefficients identification methods have been implemented: the traditional average force method and a proposed instantaneous method based on genetic algorithm and capable of estimating cutting coefficients and tool run-out at the same time.Results show that instantaneous method is more accurate and efficient compared to the average one. On the other hand, the average method does not require compensation since it is based on average signals. Furthermore a significant change of coefficients over spindle speed is highlighted, suggesting that speed-varying coefficient should be useful to improve reliability of simulated forces.     

Predictive framework for cutting force induced cylindricity error estimation in end milling of thin-walled components

The static deflections of cutting tool and workpiece are the primary source for the deviation of machined components from the design specifications during end milling of thin-walled geometries. The deviations are expressed as per the Geometric Dimensioning and Tolerancing (GD&T) principles using size, form, and orientation of the features. This paper proposes a computational framework to estimate cutting force induced cylindricity error during end milling of thin-walled circular components. The framework combines computational elements such as Mechanistic force model, Finite Element Analysis (FEA) based workpiece deflection model, Cantilever beam formulation based tool deflection model, and Particle Swarm Optimization (PSO) based cylindricity estimation algorithm. It has been observed that the static deflections of a cutting tool and thin-walled component influence the cylindricity error considerably. The inevitable aspects associated with the end milling of thin-walled circular components such as concave-convex side machining and workpiece rigidity are investigated subsequently. It was observed that the cylindricity error during concave side machining is considerably smaller due to geometric configuration imparting adequate stiffness to thin-walled components. The study also demonstrated that an appropriate combination of productive cutting conditions and the component thickness could reduce cylindricity error considerably. The outcomes of the present study are substantiated by conducting a set of computational simulations and end milling experiments over a wide range of cutting conditions. The computational framework proposed in the present study can assist process planners in selecting appropriate cutting conditions to manufacture thin-walled circular components within tolerance limits specified by the designer.     

Vibration analysis in milling titanium alloy based on signal processing of cutting force

In metal cutting processes, the chatter may cause fast wear of tools and poor surface quality of the processed parts; it can happen on different cutting parameters, but how do we identify the chatter and how do we select suitable cutting parameters to avoid chatter at high material removal rate (MRR). In this paper, the signal processing methods such as time domain, frequency domain, and time–frequency domain analysis are introduced. The signals of cutting force that were collected in milling titanium alloy Ti–6Al–4V at variable cutting speeds varied from 80 to 360 m/min; signal analysis methods such as time domain, frequency domain, and time–frequency domain were put forward. Further analysis results reveal that the chatter occur when cutting speeds are 240 and 360 m/min, when the maximum value of cutting forces increase by 61.9–66.8%, the standard deviation increase by 84.1–86.1%, and the surface roughness increase by 34.2–40.5% compared with that of at 80 m/min. Detail signal d2 is employed to monitor cutting stability state from the result of wavelet analysis.     

Tool wear monitoring system for CNC end milling using a hybrid approach to cutting force regulation

A tool wear monitoring system is indispensable for better machining productivity, with the guarantee of machining safety by informing of the time due for changing a tool in automated and unmanned CNC machining. Different from monitoring methods using other signals, the monitoring of the spindle current has been used without requiring additional sensors on the machine tools. For reliable tool wear monitoring, only the current signal from tool wear should be extracted from the other parameters to avoid exhaustive analyses on signals in which all of the parameters are fused together. In this paper, the influences of force components from different parameters on the measured spindle current are investigated, and a hybrid approach to cutting force regulation is employed for tool wear signal extraction from the spindle current. Finally, wear levels are verified with experimental results by means of real-time feedrate aspects, varied to regulate the force component from tool wear.     

The prediction of cutting force in milling Inconel-718

Due to the enormous engineering advancement in modern industries, the competition in manufacturing technologies has been increasingly intense, as can be seen in automobile and aerospace industries. Nickel-based superalloys are widely in the manufacture of components for aircraft turbine engines for cryogenic tankage, in liquid rockets, reciprocating engines, space vehicles, heat-treating equipment, chemical and petrochemical industries, because of their ability to retain high-strength at elevated temperatures. But, because of its characteristics of high-strength, poor thermal diffusion and work hardening, the cutting of nickel-based superalloys results in decreased tool life and poor efficiency of works. This is much more prominent than in other materials. AISI4340 are widely used in the manufacture of component parts for gear, pistons, and automobiles.     

Counterbalance of cutting force for advanced milling operations

The goal of this work is to concurrently counterbalance the dynamic cutting force and regulate the spindle position deviation under various milling conditions by integrating active magnetic bearing (AMB) technique, fuzzy logic algorithm and an adaptive self-tuning feedback loop. Since the dynamics of milling system is highly determined by a few operation conditions, such as speed of spindle, cut depth and feedrate, therefore the dynamic model for cutting process is more appropriate to be constructed by experiments, instead of using theoretical approach. The experimental data, either for idle or cutting, are utilized to establish the database of milling dynamics so that the system parameters can be on-line estimated by employing the proposed fuzzy logic algorithm as the cutting mission is engaged. Based on the estimated milling system model and preset operation conditions, i.e., spindle speed, cut depth and feedrate, the current cutting force can be numerically estimated. Once the current cutting force can be real-time estimated, the corresponding compensation force can be exerted by the equipped AMB to counterbalance the cutting force, in addition to the spindle position regulation by feedback of spindle position. On the other hand, for the magnetic force is nonlinear with respect to the applied electric current and air gap, the characteristics of the employed AMB is investigated also by experiments and a nonlinear mathematic model, in terms of air gap between spindle and electromagnetic pole and coil current, is developed. At the end, the experimental simulations on realistic milling are presented to verify the efficacy of the fuzzy controller for spindle position regulation and the capability of the dynamic cutting force counterbalance.     

Time-frequency-analysis-based minor cutting edge fracture detection during end milling

Successful application of tool condition detection during end milling can ensure high-quality parts and safeguard the machining system. This paper proposes an effective algorithm that consists of wavelet-based de-noising, discrete time-frequency analysis, FFT and second differencing for the detection of minor cutting edge fracture during end milling. The algorithm can be successfully applied to extract marked features from the feed-motor current signals to indicate the minor cutting edge fracture. Some typical experiments, the cutter run-out, entry/exit cuts and cutting parameters-variation, have been performed to confirm the robustness of the algorithm. The results show that the new approach has an excellent potential for practical and real-time application at low cost for the detection of minor cutting edge fracture during end milling.     

An enhanced method for cutting force estimation in peripheral milling

A new model for cutting force estimation is presented in this paper. It is based on the specific cutting force coefficient, which is defined as a function of chip thickness. The distinguishing feature of the proposed cutting force model is the use of average chip thickness for cutting force calculation on each position of the cutting tool, in such a way that only one iteration is needed on every angular position of the tool. This model is based on the actual workpiece–tool interaction which provides information about the real position of the cutting edge. It provides an alternative to other studies in scientific literature commonly based on numerical integrations. With this model, it is possible to estimate the cutting forces not only under steady-state conditions but also under variable machining conditions of axial and radial depth of cut.     

Three degrees of freedom stability analysis in the milling with bull-nosed end mills

Automated packaging is becoming more and more interesting for production sites. However, in many companies, the packaging process can only handle a limited amount of products. Most of the time for new products, the packaging system needs to be modified. One of the main components in an automated packaging process is the grasping of the objects that need to be packed. For different products, mostly a different gripping system is required. In this paper, solutions from a research project are presented which enables the flexible grasping of different products. These solutions include flexible pose recognition for different objects, path planning for the recognized objects and finally, a prototypical design for a flexible gripper, which enables handling of various objects of different weight and size.     

Optimal cutting condition determination for desired surface roughness in end milling

CNC end milling is a widely used cutting operation to produce surfaces with various profiles. The manufactured parts’ quality not only depends on their geometries but also on their surface texture, such as roughness. To meet the roughness specification, the selection of values for cutting conditions, such as feed rate, spindle speed, and depth of cut, is traditionally conducted by trial and error, experience, and machining handbooks. Such empirical processing is time consuming and laborious. Therefore, a combined approach for determining optimal cutting conditions for the desired surface roughness in end milling is clearly needed. The proposed methodology consists of two parts: roughness modeling and optimal cutting parameters selection. First, a machine learning technique called support vector machines (SVMs) is proposed for the first time to capture characteristics of roughness and its factors. This is possible due to the superior properties of well generalization and global optimum of SVMs. Next, they are incorporated in an optimization problem so that a relatively new, effective, and efficient optimization algorithm, particle swarm optimization (PSO), can be applied to find optimum process parameters. The cooperation between both techniques can achieve the desired surface roughness and also maximize productivity simultaneously.