Experimental study of surface roughness in slot end milling AL2014-T6

The aim of this work was to analyze the influence of cutting condition and tool geometry on surface roughness when slot end milling AL2014-T6. The parameters considered were the cutting speed, feed, depth of cut, concavity and axial relief angles of the end cutting edge of the end mill. Surface roughness models for both dry cutting and coolant conditions were built using the response surface methodology (RSM) and the experimental results. The results showed that the dry-cut roughness was reduced by applying cutting fluid. The significant factors affecting the dry-cut model were the cutting speed, feed, concavity and axial relief angles; while for the coolant model, they were the feed and concavity angle. Surface roughness generally increases with the increase of feed, concavity and axial relief angles, while concavity angle is more than 2.5°.     

The effects of spindle vibration on surface generation in ultra-precision raster milling

Spindle vibration has a significant influence on surface quality of ultra-precision-machined components. However, relatively few studies on the particular spindle vibration under the excitation of intermittent cutting forces in ultra-precision raster milling (UPRM) have been reported. In this study, a specialized model for an aerostatic bearing spindle under the impulsive excitation from intermittent cutting forces of UPRM is developed and its derived mathematical solutions reveal that the spindle vibration is impulsive response. The theoretical and experimental results signify that the impulsive spindle vibration produces inhomogeneous scallops forming ribbon-stripe patterns and irregular patterns like run-out on a surface of UPRM. The potential benefits for UPRM are the theoretical supports for optimization and prediction of surface generation through the optimal selection of spindle speed.     

In-process prediction of cutting depths in end milling

In geometric adaptive control systems for the end milling process, the surface error is usually predicted from the cutting force owing to the close relationship between them, and the easiness of its measurement. Knowledge of the cutting depth improves the effectiveness of this approach, since different cutting depths result in different surface errors even if the measured cutting forces are the same. This work suggests an algorithm for estimating the cutting depth based on the pattern of cutting force. The cutting force pattern, rather than its magnitude, better reflects the change of the cutting depth, because while the magnitude is influenced by several cutting parameters, the pattern is affected mainly by the cutting depth. The proposed algorithm can be applied to extensive cutting circumstances, such as presence of tool wear, change of work material hardness, etc.     

Optimization of feedrate in a face milling operation using a surface roughness model

Optimization of feedrate is valuable in terms of providing high precision and efficient machining. The surface roughness is particularly sensitive to the feedrate and the runout errors of the inserts in a face-milling operation. This paper analyzes the effects of the insert runout errors and the variation of the feedrate on the surface roughness and the dimensional accuracy in a face-milling operation using a surface roughness model. The validity of the developed model was proved through cutting experiments, and the model was used to predict the machined surface roughness from the information of the insert runouts and the cutting parameters. From the estimated surface roughness value, the optimal feedrate that gave a maximum material removal rate under the given surface roughness constraint could be selected by a bisection method.     

An in-process surface recognition system based on neural networks in end milling cutting operations

An in-process based surface recognition system to predict the surface roughness of machined parts in the end milling process was developed in this research to assure product quality and increase production rate by predicting the surface finish parameters in real time. In this system, an accelerometer and a proximity sensor are employed as in-process surface recognition sensors during cutting to collect the vibration and rotation data, respectively. Using spindle speed, feed rate, depth of cut, and the vibration average per revolution (VAPR) as four input neurons, an artificial neural networks (ANN) model based on backpropagation was developed to predict the output neuron-surface roughness R_a values. The experimental results show that the proposed ANN surface recognition model has a high accuracy rate (96–99%) for predicting surface roughness under a variety of combinations of cutting conditions. This system is also economical, efficient, and able to be implemented to achieve the goal of in-process surface recognition by retrieving the weightings (which were generated from training and testing by the artificial neural networks), predicting the surface roughness R_a values while the part is being machined, and giving feedback to the operators when the necessary action has to be taken.     

A review of surface roughness generation in ultra-precision machining

Ultra-precision machining (UPM) is capable of manufacturing a high quality surface at a nanometric surface roughness. For such high quality surface in a UPM process, due to the machining complexity any variable would be possible to deteriorate surface quality, consequently receiving much attention and interest. The general factors are summarized as machine tool, cutting conditions, tool geometry, environmental conditions, material property, chip formation, tool wear, vibration etc. This paper aims to review the current state of the art in studying the surface roughness formation and the factors influencing surface roughness in UPM. Firstly, the surface roughness characteristics in UPM is introduced. Then in UPM, a wide variety of factors for surface roughness are then reviewed in detail and the mechanism of surface roughness formation is concluded thoroughly. Finally, the challenges and opportunities faced by industry and academia are discussed and several principle conclusions are drawn.     

Predicting surface roughness in machining: a review

The general manufacturing problem can be described as the achievement of a predefined product quality with given equipment, cost and time constraints. Unfortunately, for some quality characteristics of a product such as surface roughness it is hard to ensure that these requirements will be met. This paper aims at presenting the various methodologies and practices that are being employed for the prediction of surface roughness.The resulting benefits allow for the manufacturing process to become more productive and competitive and at the same time to reduce any re-processing of the machined workpiece so as to satisfy the technical specifications. Each approach with its advantages and disadvantages is outlined and the present and future trends are discussed. The approaches are classified into those based on machining theory, experimental investigation, designed experiments and artificial intelligence (AI).     

Impact of the cutting dynamics of small radial immersion milling operations on machined surface roughness

This paper deals with a numerical and experimental study of the dynamics of flank milling operations at low cutting rates. It focuses on both properties of the cutting vibratory phenomena and their impacts on the roughness of the machined surface. The study is based on a one degree of freedom model of the mechanical machining system. The system is of the rigid cutter–flexible workpiece type. The cutting force model is based on the regenerative mechanism. The roughness of the surface machined at high speed revolutions has been studied for both forced vibrations occurring during stable cutting and self-excited vibrations occurring during unstable cutting. It is shown that forced vibrations have only a very slight impact (roughness remains quite similar to that obtained with a fully rigid mechanical system), while unstable cutting mainly impacts roughness. The stable milling zones can be shown on a roughness map. The study of the roughness shows that the boundary between stable and unstable cutting conditions, in the case of interrupted cutting, is a wide zone characterised by a doubling of the tooth passing period. In this zone, only one tooth over two is removing material due to the vibratory motion. A discussion explains the phenomenon.     

高速铣削表面粗糙度的研究

通过在HSM－700型高速铣床上的正交铣削试验，联系平时实际的生产加工情况，分析高速铣削的切削加工参数对零件表面粗糙度的影响。通过分析不同铣削参数下的零件表面粗糙度和切屑变形，为高速加工切削参数的选择和表面质量的控制提供依据。     

A theoretical and experimental study of surface generation under spindle vibration in ultra-precision raster milling

In ultra-precision raster milling (UPRM), the impulse spindle vibration induced by the impulse-like cutting forces is intrinsic and special mechanism majorly influencing surface topography. It is fundamentally distinctive with the step spindle vibration induced by the step-like cutting forces in turning. However, no work has been conducted to study surface generation under the impulse spindle vibration in UPRM in depth. Consequently, this paper theoretically and experimentally elaborates that in UPRM, (i) the impulse spindle vibration includes the axial, radial and coupled-tilting spindle vibration with damping; (ii) the excitation frequency of the impulse-like cutting forces, i.e. spindle speed, determines the spindle vibration characteristics, i.e. synchronous or asynchronous spindle vibration; (iii) the coupled-tilting spindle vibration is a predominant factor influencing surface generation; and (iv) the irregular spindle-vibration waves induced by the impulse spindle vibration produce one of the irregular, lattice-like and stripe patterns or their hybrids at a milled surface.     

An improved cutting force and surface topography prediction model in end milling

During the milling operation, the cutting forces will induce vibration on the cutting tool, the workpiece, and the fixtures, which will affect the surface integrity of the final part and consequently the product's quality. In this paper, a generic and improved model is introduced to simultaneously predict the conventional cutting forces along with 3D surface topography during side milling operation. The model incorporates the effects of tool runout, tool deflection, system dynamics, flank face wear, and the tool tilting on the surface roughness. An improved technique to calculate the instantaneous chip thickness is also presented. The model predictions on cutting forces and surface roughness and topography agreed well with experimental results.     

Effect of grinding wheel spindle vibration on surface roughness and subsurface damage in brittle material grinding

The external interference and vibration can seriously affect the machining errors in brittle materials grinding process. This paper proposes a new model to analyze the relationship between surface roughness (SR) and subsurface damage (SSD) depth on the basis of grinding kinematics analysis and indentation fracture mechanics of brittle materials taking the wheel spindle vibration into account. The basic equations, for example, equations of grain trajectory and penetration depth are derived in new forms. Based on the basic equations above, the existing SR and SSD formulae are modified for further study. The effects of grinding and vibration parameters on SR and SSD are respectively analyzed in detail. Results show that both SR and SSD increase with the increase of table speed and vibration amplitude resulting in bad surface and subsurface quality. On the other hand, both the increasing grinding speed and decreasing vibration frequency can improve the quality of ground surface and subsurface with small SR and SSD. In addition, the increase of initial grinding depth and vibration initial phase increase the depth of SSD but have little effect on SR. The penetration depth and distance between grain's tip and finished surface are the two main factors considered to cause the different effect laws on SR and SSD among these parameters. Experiment is carried out to validate the rationality of proposed model. The effect trends of various grinding parameters on SR obtained by our model consist with measured experimental data. The typical subsurface crack system is clearly revealed through the experimental observation on SSD using SEM. Finally, the relationship between the two is fitted utilizing quadratic polynomial. Results show that the SSD depth is nonlinear monotone increasing with SR and the fitting accuracy is more or less affected by both grinding and vibration parameters.     

The effect of tool vibrations on the flank surface created by peripheral milling

Tool vibrations have a significant influence on the surface quality with respect to surface location error and roughness. Even chatter-free milling processes can produce a high surface location error since chatter-free does not necessarily mean vibration-free. This article describes a geometric model for predicting the surface formation resulting from peripheral milling processes when tool vibrations are present. This model enables one to predict and minimize the roughness and location error of the flank surface. Comparisons between simulations and experiments show the effectiveness of this modeling approach. An important result of this research is that it has shown that milling at a stability maximum does not generally yield the best surface quality.     

Predictive modeling of surface roughness in grinding

The surface roughness is a variable often used to describe the quality of ground surfaces as well as to evaluate the competitiveness of the overall grinding system. This paper presents the prediction of the arithmetic mean surface roughness based on a probabilistic undeformed chip thickness model. The model expresses the ground finish as a function of the wheel microstructure, the process kinematic conditions, and the material properties. The analysis includes a geometrical analysis of the grooves left on the surface by ideal conic grains. The material properties and the wheel microstructure are considered in the surface roughness prediction through the chip thickness model. A simple expression that relates the surface roughness with the chip thickness was found, which was verified using experimental data from cylindrical grinding.     

Modeling surface roughness in the stone polishing process

In this paper, a new method for modeling and predicting the surface roughness of the workpiece in the stone polishing process is developed. This method is based on the random distribution of the stone grain protrusion heights and the force balance by contact grains. To do so, first, the topography of a polishing stone is generated based on a Gaussian distribution with the mean value and standard deviation determined from a given stone grit number. Second, the plasticity theory is applied to determine the micro depth of cut of a single grain for a given workpiece hardness (Brinell number). Third, a search method is developed to determine the number of the contact grains and the micro depth of cut, based on the force balance principle between the force applied on the stone and the forces transmitted to the grains that are in contact with the workpiece. Fourth, a method is presented for predicting the surface roughness based on the micro depth of cut and contact grains. A good agreement of the prediction results with the experimental data proves the effectiveness of the proposed method.     

The statistical modeling of surface roughness in high-speed flat end milling

Surface roughness is one of the most important requirements in machining process. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process obtained by means of both minimum surface roughness values and long tool life. The statistical models were developed to predict the surface roughness.This paper presents the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions, using machining variables such as spindle speed, feed rate, depth of cut, and step over. First- and second-order models were developed using experimental results of a rotatable central composite design, and assessed by means of various statistical tests. The highest coefficient of correlation (R_adj²) (88%) was obtained with a 10-parameter second-order model. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting the probable effect of the tool wear on surface roughness. Thus, in order to enhance the estimation capability of the model, another independent variable was included into the model to account for the effect of the tool wear, and the total operating time of the tool was selected as the most suitable variable for this purpose. By inserting this new variable as a linear term into the model, R_adj² was increased to 94% and a good fit was observed between the model predictions and supplementary experimental data.In this study, it was observed that, the order of significance of the main variables is as X₅>X₃>X₄>X₁>X₂ (total machining time, depth of cut, step over, spindle speed and feed rate, respectively).     

晶体材料车削表面粗糙度的实验研究

本文探讨了晶体材料的切削加工性，重点分析了切削条件对表面粗糙度的影响。     

A vision system for surface roughness characterization using the gray level co-occurrence matrix

Computer vision technology has maintained tremendous vitality in many fields. Several investigations have been performed to inspect surface roughness based on computer vision technology. This work presents a new approach for surface roughness characterization using computer vision and image processing techniques. A vision system has been introduced to capture images for surfaces to be characterized and a software has been developed to analyze the captured images based on the gray level co-occurrence matrix (GLCM).Three standard specimens and 10 machined samples with different roughness values have been characterized by the presented approach. Three-dimensional plots of the GLCMs for various captured images have been introduced, compared and discussed. In addition, some statistical parameters (maximum occurrence of the matrix, maximum occurrence position and standard deviation of the matrix) have been calculated from the GLCMs and compared with the arithmetic average roughness R_a. Furthermore, a new parameter called maximum width of the matrix is introduced to be used as an indicator for surface roughness.     

Study on surface roughness model and surface forming mechanism of ceramics in quick point grinding

The quick-point grinding experiment of fluorophlogopite was conducted by using a MK9025A profile grinder which considered the simple single factor, such as the grinding wheel and table feed speed, grinding depth, inclining angle and deflection angle. The experimental results indicated that the surface roughness was mainly influenced on inclining angle and deflecting angle. Moreover, the modified model of the quick-point grinding process was proposed in the paper, which based on Malkin kinematics model, Snoeys empirical model and grinding thickness empirical model. The inclining angle and deflecting angle was introduced in the modified model. Comparison of the predicted results of these models and experimental ones indicated that the modified model was in well agreement with the experimental data. Further, standard deviation of these models and experiment was studied in the paper, it is found that the modified model was the more ideal. In order to study the effect of various technology factors on the sensitivity of surface roughness, “Relative extremum error” concept was first proposed in the paper. It was found that simple single factor in the modified model were relatively sensitive to surface roughness than other models.     

Surface roughness prediction of flow-formed AA6061 alloy by design of experiments

Design of experiments has been used to study the effects of the main flow-forming parameters such as the speed of the mandrel, the longitudinal feed, and the amount of coolant used on the surface roughness of flow-formed AA6061 tube. A mathematical prediction model of the surface roughness has been developed in terms of the above parameters. The effect of these parameters on the surface roughness has been investigated using response surface methodology (RSM). Response surface contours were constructed for determining the optimum forming conditions for a required surface roughness. The developed prediction equation shows that the longitudinal feed rate is the most important factor that influences the surface roughness. The surface roughness was found to increase with increase in the longitudinal feed and it decreased with decrease in the amount of the coolant used. The verification experiment carried out to check the validity of the developed model predicted surface roughness within 6% error.