Face Milling Operations Research Articles

Tool breakage diagnosis in face milling by support vector machine

This paper introduces a new diagnosis technique for tool breakage in face milling using a support vector machine (SVM). The features of spindle displacement signals are first fed into the kernel-based SVM decision function. After the SVM learning procedure, the SVM can respond in real-time to automatically diagnose tool fracture under varying cutting conditions. Experimental results show that this new approach can detect tool breakage in a wide range of face-milling operations.

Nonlinear Feed Effect in Machining Chatter Analysis

Regenerative chatter is a major limitation to the productivity and quality of machining operations due to the excessive rate of tool wear and scrap parts which are produced. Machining chatter analysis techniques examine the stability of the closed-loop model (force process and machine tool-part structure) of the machining operation to determine the stable process parameter space. Almost all chatter analysis techniques assume a linear force process and develop stability lobe diagrams (i.e., plots of the stable and unstable regions in the process parameter space) for a specific feed. It is well known that machining force processes inherently contain a nonlinear relationship between the force and the feed, which is typically described by a power law. In this paper, the linear chatter analysis technique developed by Budak and Altintas is extended to account for the force-feed nonlinearity. The analysis provides insight into the effect feed has on chatter in machining operations. Also, by directly including the force-feed nonlinearity in the chatter analysis, the need to calibrate the force process model at different feeds is alleviated. The analysis is developed for turning and face milling operations and is validated via time domain simulations for both operations and by experiments for a face milling operation. The analyses show excellent agreement with both the time domain simulations and the experiments. Further, several end milling experiments were conducted that illustrate the nonlinear effect feed has on chatter in machining operations.

Prediction of burr formation during face milling using an artificial neural network with optimized cutting conditions

Burrs formed during face milling operations are difficult to characterize because there are several parameters with complex interactions that affect the cutting process. In this paper, a combined artificial intelligence and optimization approach is introduced to predict burr types formed during face milling. The Taguchi method was selected for the optimization and an artificial neural network (ANN) was constructed for the machining of aluminium alloy 6061-T6. For the training of the ANN, the input was non-dimensionalized using the optimized results from the Taguchi method. The resulting ANN output was in agreement with experimental results, validating the proposed scheme.

Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

Abstract Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the “ANNs” models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension “D” and vertical scaling parameter “G”. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters “D” and “G” in face milling operations. Moreover, W–M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens.

A study of back cutting surface finish from tool errors and machine tool deviations during face milling

The surface finish of mechanical components produced by face milling is given by factors such as cutting conditions, workpiece material, cutting geometry, tool errors and machine tool deviations. The contribution of the different tool teeth to imperfections in the machined surface is strongly influenced by tool errors such as radial and axial runouts. The surface profile of milled parts is not only affected by chip removal due to front cutting, but also by back cutting, which must be taken into account when predicting surface roughness. In the present work, the influence of back cutting on the surface finish obtained by face milling operations is studied. Final part surface roughness is modelled from tool runouts and height deviations that affect the surface marks provoked by back cutting. Round insert cutting tools and surface positions defined by cutter axis trajectory are considered, and milling experiments are developed for a spindle speed of 750 rpm, depth of cut of 0.5 mm and feeds from 0.4 to 1.0 mm/rev. Experimental observations are compared with the theoretical predictions provided by the surface roughness model, and good agreement is found between both results. Surface imperfections caused by front and back cutting are analysed, and discrepancies between experiments and numerical predictions are explained by undeformed chip thickness variations along the tool tooth cutting edge, the tearing of the workpiece material, and fluctuations in the feedrate and height deviation during machine tool axis displacement.

Improvement of Dynamic Properties in Milling by Integrated Stepped Cutting

Die and mold machining usually necessitate employment of milling tools with long shanks. However, economic and technical design requirements often enforce low frequency structural vibration characteristics in work-pieces, especially in an area distanced from the tool holder. These structurally unstable designs pose a challenge for milling operations. The associated vibrations invariably reduce the quality of the work-piece produced and drastically increase average processing time and costs. This paper deals with cutting techniques used in face milling operations under semi-stable and unstable conditions. The strategy to use milling tools with integrated stepped cutting passes shows, that the use of smaller cutting ratios b/h, the chatter characteristics of the process could be significantly improved, thereby increasing the performance of the milling process.

Analysis of the Behaviour Effect of Face Cutting Edge Inserts on Surface Roughness when Milling Steels with MQL Lubrication

The main objectives of this work are the study of the obtained surface roughness on steels, using face cutting edge inserts milling tools in finishing face milling operations with microlubrication (MQL), and comparison of the results obtained with the widely-used radius inserts. This experimental study analyzes the roughness and surface appearance obtained with both sort of inserts. The interest about this study is to determine the steel types and the optimal cutting conditions for milling with this face cutting edge inserts. Another result analysed is the utility of the MQL implementation compared to the dry system.

Performance-based optimization of multi-pass face milling operations using Tribes

The paper proposes a new optimization technique based on Tribes for determination of the cutting parameters in multi-pass milling operations such as plain milling and face milling by simultaneously considering multi-pass rough machining and finish machining. The optimum milling parameters are determined by minimizing the maximum production rate criterion subject to several practical technological constraints. The cutting model formulated is a nonlinear, constrained programming problem. Experimental results show that the proposed Tribes-based approach is both effective and efficient.

Mechanics of machining of face-milling operation performed using a self-propelled round insert milling cutter

There has been a renewed interest in the technology of rotary tools because of their ability to perform more productive machining and the concurrent evolution of a number of new ‘difficult-to-machine’ materials. This paper presents an investigation into the application of rotary tools in a face-milling operation. The work involved analysis of cutting forces and chip characteristics, and the development of analytical as well as conceptual models to predict the cutting forces. It was evident that the proposed model predicts cutting force magnitude with a fair accuracy.

Development of a knowledge-based expert system for solving metal cutting problems

In metal cutting, the problems need to be well analyzed in order to take precautions before any unexpected results are encountered. This process plays a significant role in achieving consistent quality and in controlling the overall cost of manufacturing. However, it is a difficult task that needs an expert who has a great deal of information and experience in metal cutting. In the present paper, a knowledge-based expert system (COROSolve) that investigates problems that are encountered in three main metal cutting areas: turning, milling and drilling is developed. A great deal of metal cutting operations such as external/internal turning with negative/positive inserts, aluminum turning, parting bars/tubes, grooving, profiling, recessing and threading operations in turning; face milling, square shoulder milling, end milling, multi-purpose milling and side and face milling operations in milling; and drilling operations that use solid drills or drills with indexable inserts in drilling are taken into consideration. COROSolve gives recommendations for the cutting data (i.e., cutting speed, depth of cut, and feed) and updates the problem, cause and remedy database, thus the number of problems that the system can handle is increased.

The milling performances of TiC-based cermet tools with TiN nanopowders addition against normalized medium carbon steel AISI1045

The two milling tools obtained from nearly full dense TiC-based cermets with TiN nanopowders addition, with limited percentage of monophase nickel and biphase nickel–cobalt metal binders, were tested in face milling operations, in the dry cutting of a medium carbon steel (AISI1045). Microstructure and mechanical properties were studied. Wear mechanisms (mainly diffusion and oxidation) were investigated in detail and compared each other in order to better understand key aspects due to thermal wear mechanisms. Comparing tool A with B, under the adopted cutting conditions, the tool A has a better resistance to oxidation deformation in machining medium carbon steel, but lower impact breakage property on the same conditions.

Modeling of cutting forces in a face-milling operation with self-propelled round insert milling cutter

Rotary tools are being rediscovered for their applications in machining of ‘difficult-to-machine’ materials or for general improvement in the productivity of machining operations. While, a detailed analysis of application of rotary tools in turning operations has been done, their application in the generation of plain surfaces has received limited attention. This paper deals with the modeling of cutting forces in a face-milling operation performed using self-propelling inserts. The proposed model incorporates differences in the machining mechanics of self-propelling inserts due to the difference in their geometry and rotation in a static force prediction model in a face-milling operation with stationary inserts. The predicted values of cutting forces evaluated by the proposed model are in excellent agreement with the experimental value than those predicted using the static force model as it is.

Design of wind-driven generator made up of dynamos assembling : Valdès, L.-C. and Darque, J. Renewable Energy, 2003, 28, (3), 345–362

In the present study, a strain gauge-based low-priced dynamometer and a customized data acquisition system are developed to simultaneously measure torque and tri-axial cutting forces in end-milling and drilling operations. This design includes 24 strain gauges bonded on four octagonal rings. To achieve the utmost sensitivity and overcome the cross-talk among the torque and components with gauges located at similar locations, finite element analyses have been practiced. These analyses determined the best composition of the gauges to attain the desired results. A model is then used to describe the system mathematically, and modal analyses have been conducted to estimate the dynamometer's working range. Face milling operations have been run to test the dynamometer's efficiency. The designed dynamometer was accredited by a commercially available dynamometer as a reliable and affordable instrument to be used in low to medium-speed milling processes and can be adopted by industries and laboratories with limited resources.

Optimisation of cutting parameters for burr minimization in face-milling operations

Burrs formed during face milling operations can be very difficult to characterize since there exist several parameters which have complex combined effects that affect the cutting process. Many researchers have attempted to predict burr characteristics including burr size and shape, using various experimental parameters such as cutting speed, feed rate, in-plane exit angle, and number of inserts. However, the results of these studies tend to be limited to a specific process parameter range and to certain materials. In this paper, the Taguchi method--which is a systematic optimisation method for design and analysis of experiments--is introduced to acquire optimum cutting conditions for burr minimization. In addition, analysis of variance (ANOVA) is employed to study more detailed performance characteristics. Experimental verifications are provided to show the effectiveness of this approach.

Characteristic Variations of Chip Morphology and Cutting Forces in Face Milling with Flat-Faced and Grooved Tool Inserts

The modeling of face milling operations has been traditionally focused on using flat faced tools with an assumption of perfectly straight chip formation. However, curled chip formation is a common phenomenon in practical milling operations, and it has a profound effect on machining performances. A new analytical model that takes into account the chip curling effect in milling operations is proposed in this paper. It is shown that chip morphology and machining parameters, such as the chip up-curl radius, the chip thickness, and the tool-chip contact length, simultaneously change with varying undeformed chip thickness during each tooth cycle. The proposed model is validated through milling tests. Good agreement between theory and experiments has been reached. A comparison of the average resultant force among three types of commercially available tool inserts is made to demonstrate the effect of tool insert chip-groove geometry on milling performances.

Flank Wear Estimation in Face Milling Based on Radial Basis Function Neural Networks

This paper presents an estimation of flank wear in face milling operations using radial basis function (RBF) networks. Various signals such as acoustic emission (AE), surface roughness, and cutting conditions (cutting speed and feed) have been used to estimate the flank wear. The hidden layer RBF units have been fixed randomly from the input data and using batch fuzzy C means algorithm, and a comparative study has been carried out. The results obtained from a fixed RBF network have been compared with those from a resource allocation network (RAN).

Fuzzy Logic Based In-Process Tool-Wear Monitoring System in Face Milling Operations

A fuzzy logic based in-process tool-wear monitoring (FL-ITWM) system is developed and presented in this paper. The fuzzy membership function and rule bank were based on observations during cutting experiments using artificial tool-wear inserts in face milling operations. Maximum resultant force and cutting parameters (feedrate and depth of cut) were used as independent variables, and the system was verified using 25 test experimental runs. A t-test using an alpha value of 0.05 demonstrated that the system has greater than 90% accuracy when predicting tool-wear magnitude on-line.

Experimental design and performance analysis of TiN-coated carbide tool in face milling stainless steel

A preliminary experiment and performance analysis of a TiN-coated carbide tool in the face milling of stainless steel is given first. Then, the experimental design of using the Taguchi method is employed to optimize the cutting parameters. An orthogonal array, the signal-to-noise ( S/ N) ratio, and the analysis of variance (ANOVA) are employed to study the performance characteristics in face milling operations. Through this study, not only can the range of cutting parameters for face milling stainless steel be obtained, but also the optimal cutting parameters can be found.

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.

Process modeling in machining. Part II: validation and applications of the determined flow stress data

The flow stress data, determined in Part I of the present study, is validated by using it as an input to the finite element method and analytical based computer programs to predict process variables in metal cutting. The predicted process variables in two-dimensional orthogonal turning and three-dimensional face milling operations, are compared with the published experimental data and the results of experiments conducted in the present work. The majority of the predictions have been found to be in reasonable agreement with the measured results. The comparisons have been discussed and, in the case of unsatisfactory agreement, the reasons for inaccurate predictions are reviewed. The flow stress data of AISI H13 tool steel (46 HRC), determined in Part I is used in this study to investigate the influence of edge preparation on forces in the cutting and feed directions, tool stresses and cutting temperatures. It has been concluded that the hone-radius edge with a hone radius of 0.1 mm provides the maximum resistance to chipping and the chamfered edge (20°×0.1 mm) has the minimum flank and crater wears for the conditions used in the present study.