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"Milling machines"
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CNC milling in the workshop
CNC control of milling machiens is now available to even the smallest of workshops. This allows designers to be more ambitious and machinists to be more confident of the production of parts, and thereby greatly increase the potential of milling at home. This guide takes a practical approach to software and techniques, and explains how you can make full use of your CNC mill to produce ambitious work of a high standard.
Book
Effect of minimum quantity lubrication on energy consumption in multi-axis milling of LY12 aluminum alloy
The impact of milling parameters on energy consumption was investigated during blade machining by a four-axis milling machine under different conditions. LY12 aluminum alloy was used as a workpiece, and the single-factor experiments of different milling parameters (milling speed, feed rate, milling depth, and milling width) under minimum quantity lubrication (MQL) were carried out in the four-axis vertical machining center, as well as a comparison experiment with dry milling. The milling power was recorded by the three-phase electrical parameter tester, and the specific power was calculated. The overall milling power shows an upward trend with the increase of each milling parameter, while the specific power shows a downward trend under MQL and dry milling. MQL can effectively reduce milling energy consumption. The milling power and specific power decrease with the increase of oil-air flow rate and oil feeding frequency. When the oil supply frequency is 20 cy/min and the oil-air flow rate is 400 ml/min, the lubrication effect is relatively good.
Journal Article
Reduction of noise pollution in CNC wood milling through multi-parameter optimization using response surface methodology
CNC (Computer Numerical Control) wood milling machines offer significant productivity advantages but are associated with excessive noise pollution, posing health risks to workers. This study investigates the influence of machining parameters on Noise Pollution Level (NPL) in CNC wood milling and aims to optimize these parameters to minimize noise emissions.
A Response Surface Methodology (RSM) based on Box-Behnken Design (BBD) was employed to model the effects of cutting speed, feed rate, depth of cut, and step over on NPL. A total of 27 experimental runs were conducted. Statistical analysis, including ANOVA and regression modeling, was performed to determine the significance of each parameter. The model was further optimized using a Genetic Algorithm (GA).
The NPL observed across experiments ranged from 97.4 dB to 103.8 dB, with all values exceeding the NIOSH recommended limit of 85 dB. ANOVA results revealed that cutting speed, cutting speed squared, feed rate, and depth of cut had a statistically significant effect on NPL (p < 0.05). The regression model showed a high degree of fit (R² = 0.945). Optimal parameters-cutting speed of 12,730 rpm, feed rate of 58 mm/s, depth of cut of 3.2 mm, and step over of 6.4 mm-were identified using GA, resulting in a predicted NPL of 96.2 dB, which closely matched the experimentally validated value of 95.8 dB.
The study confirms that NPL in CNC wood milling can be significantly reduced by optimizing machining parameters. The integration of RSM and GA provides a reliable framework for minimizing occupational noise exposure, thereby enhancing worker safety in woodworking environments.
Journal Article
Analysis of Mechanical Characteristics of the Swing Angle Milling Head of a Heavy Computer Numerical Control Milling Machine and Research on the Light Weight of a Gimbal
As the key component of a five-axis CNC planer-type milling machine, the integral mechanical property of the A/C swing angle milling head directly affects the machining accuracy and stability of the milling machine. Taking the mechanical A/C swing-angle milling head of a five-axis numerical-control gantry milling machine as the research object, the stress deformation characteristics and natural frequency of the swing-angle milling head under actual working conditions were studied using finite-element analysis. Based on the analytical results, it was determined that the cardan frame, with its large mass proportion and strong rigidity of the whole milling head, is the object to be optimized. The topological optimization of the cardan frame, in which achieving the minimum flexibility was the optimization objective, was carried out to determine the quality reduction area. By comparing the simulation results of the cardan frames of three different rib plate structures, it was shown that the cardan frame performance of the ten-type rib plate structure was optimal. The analytical results showed that, when the cardan frame met the design requirements for stiffness and strength, the mass after optimization was reduced by 13.67% compared with the mass before optimization, the first-order natural frequency was increased by 7.9%, and the maximum response amplitude was reduced in all directions to avoid resonance, which was beneficial to the improvement of the dynamic characteristics of the whole machine. At the same time, the rationality and effectiveness of the lightweight design method of the cardan frame were verified, which has strong engineering practicality. The research results provide an important theoretical basis for the optimization of other machine tool gimbals and have important practical significance and application value.
Journal Article
Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method
Surface roughness
Ra
is a parameter normally used to indicate the level of surface irregularities during machining operations. This work aims to model the cutting process, correlate and optimise the critical process parameters using the Taguchi method during the milling operation of AISI P20 in order to reduce surface roughness. The Autodesk Fusion 360 (2.0.5357) was employed for modelling the stress, displacement and thermal behaviour of the cutting tool and work piece under different cutting conditions. The experimental plan was based on Taguchi’s technique including L9 orthogonal array with three factors and three levels for each variable and studying the contribution of each factor on surface roughness. The Taguchi method was used to study the effect of process parameters and establish correlation among the cutting speed, feed and depth of cut with respect to the major machinability factor, surface finish. The machining parameters evaluated in this study are the depth of cut (
d
), spindle speed (N) and cutting feed (
f
m
) while the response factor measured is surface roughness. The physical experiments were conducted on M200 TS material on a DMC 635 V DMG ECOLINE, Deckel Maho Germany, Siemens 810D, 3-Axis, CNC vertical milling machine using carbide inserts and the surface roughness was measured using the Mitutoyo SJ–201, surface roughness Machine. The statistical analysis of both the numerical and physical experiments brought about the development of a mathematical model and optimum solutions for the evaluation of surface roughness during the milling process with high degree of correlation with experimental values thus validating the developed model.
Journal Article
The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process
Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the
z
-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics.
Journal Article
Examination of temperature distribution in turning and milling machines during specified machining operations using Taguchi-FEM with ANOVA-driven search
Background
Turning and milling machines (TMMs) have diverse and complex heat sources, which pose challenges to understanding their thermal behavior and thermal critical regions.
Methods
A comprehensive procedure was proposed to explore the thermal characteristics of TMMs and identify thermally critical components. This study employed a Taguchi-FEM approach along with sensitivity analysis to evaluate the significant contributing boundary conditions of TMMs to tool center point (TCP) deformation in the X-, Y-, and Z-directions. An ANOVA-driven search was further proposed to identify thermal key points significantly impacting TCP deformation across directions and time intervals. Integrating both methods facilitated the identification of the TMM thermal critical regions.
Results
The spindle TCP’s thermal critical regions were most significantly influenced by the spindle component, followed by the milling cutter shaft component and then the right side of the base bed. Similarly, for the sub-spindle TCP, the highest impact came from the sub-spindle components, followed by the right side of the base bed and the milling cutter shaft components.
Conclusions
The proposed procedure identified the significant regions for assisting evaluation in TMMs and reduced the number of required temperature monitoring points while optimizing their placements to minimize measurement costs.
Journal Article
Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions
The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.
Journal Article
Compound efficient and powerful milling machine tool of blisk
In present, five-axis numerical control (NC) machining center is used to process blisk, and the technology of plunge-milling and side-milling are widely adopted, which results in lower processing efficiency and high total cost. In the present study, first, a new machining technology of blisk is put forward, which is known as multi-milling, namely, disc-milling is used to remove the large margin of blisk’s channel, plunge-milling can expand the size of groove, further forming the surface of channel, and the corner and angle of the channel can be eliminated by side-milling. Second, with one-stage fan blisk of a certain type aircraft engine as an example, the processing efficiency of three different technologies (multi-milling, plunge-milling+side-milling, and side-milling) are studied by means of UG (Unigraphics NX) software. Third, based on the technology of multi-milling, the specific structure of multi-milling machine tool is designed. Last, the verification test is conducted, test results show that the processing efficiency of blisk can be enhanced drastically, finish cost can be reduced greatly with the multi-milling machine tool, which can give a great push to the development of aviation industry.
Journal Article
A self-adaptive machining parameters adjustment method for stabilizing the machining-induced surface roughness
To maintain a qualified product, it is necessary to control the final machined quality approximately. To this end, massive research has been devoted to modeling and controlling the machining-induced surface roughness. However, a generalized surface roughness prediction model is hard to develop due to the complex modeling process and insufficient data. And a feasible surface roughness stabilization method is often missing in the existing studies. To this end, this paper proposed a novel self-adaptive machining parameters adjustment method for stabilizing the machining-induced surface roughness. In the proposed method, a physical surface roughness prediction model is developed at first. Then, a CNN-LSTM is employed to realize spatial–temporal feature extraction. Next, the MMD-MSE-based method is employed to realize the transfer learning process. Finally, a self-adaptive process parameter tuning system using the gradient descent method is developed, based on the surface prediction method. Experiments are conducted on a milling machine, and results indicate that the proposed method can realize a high accuracy and generalization prediction of surface roughness. In terms of the machined surface roughness, the proposed method effectively maintains the surface roughness under 1.6 μm.
Journal Article