Modeling and adaptive force control of milling by using artificial techniques

The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. On the basis of the hybrid process modeling, off-line optimization and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. This is an adaptive control system controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. In this way it compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter, etc. The basic control principle is based on the control scheme (UNKS) consisting of two neural identifiers of the process dynamics and primary regulator. An overall procedure of hybrid modeling of cutting process used for creating the CNC milling simulator has been prepared. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability, but also the machining efficiency of the milling system with the adaptive controller is 27% higher than for traditional CNC milling system.     

刀具状态在线监测与智能识别方法研究

切削加工中刀具状态是影响加工质量的关键因素,刀具的磨损直接影响工件的加工精度和表面粗糙度.选择加速度传感器监测切削加工中的振动信号,针对刀具状态变化时振动能量分布随之变化的特点,提取不同频段振动能量作为特征量,利用RBF神经网络进行聚类辨识.实验结果表明,该方法具有良好的识别效果和工程应用价值.     

Machine tool condition monitoring using workpiece surface texture analysis

Tool wear affects the surface roughness dramatically. There is a very close correspondence between the geometrical features imposed on the tool by wear and micro-fracture and the geometry imparted by the tool on to the workpiece surface. Since a machined surface is the negative replica of the shape of the cutting tool, and reflects the volumetric changes in cutting-edge shape, it is more suitable to analyze the machined surface than look at a certain portion of the cutting tool. This paper discusses our work that analyzes images of workpiece surfaces that have been subjected to machining operations and investigates the correlation between tool wear and quantities characterizing machined surfaces. Our results clearly indicate that tool condition monitoring (the distinction between a sharp, semi-dull, or a dull tool) can be successfully accomplished by analyzing surface image data. Received: 9 June 1998 / Accepted: 6 October 1999     

刀具参数对加工表面粗糙度影响的研究

通过车削加工试验和数据测量,研究了刀具参数及刀具磨损量对加工表面粗糙度的影响。应用Matlab计算得出刀具参数变化和刀具磨损量对加工表面粗糙度的定量关系,为加工时选用刀具参数及控制刀具磨损量提供理论依据。     

Digital twin-driven surface roughness prediction and process parameter adaptive optimization

In the process of parts machining, the real-time state of equipment such as tool wear will change dynamically with the cutting process, and then affect the surface roughness of parts. The traditional process parameter optimization method is difficult to take into account the uncertain factors in the machining process, and cannot meet the requirements of real-time and predictability of process parameter optimization in intelligent manufacturing. To solve this problem, a digital twin-driven surface roughness prediction and process parameter adaptive optimization method is proposed. Firstly, a digital twin containing machining elements is constructed to monitor the machining process in real-time and serve as a data source for process parameter optimization; Then IPSO-GRNN (Improved Particle Swarm Optimization-Generalized Regression Neural Networks) prediction model is constructed to realize tool wear prediction and surface roughness prediction based on data; Finally, when the surface roughness predicted based on the real-time data fails to meet the processing requirements, the digital twin system will warn and perform adaptive optimization of cutting parameters based on the currently predicted tool wear. Through the development of a process-optimized digital twin system and a large number of cutting tests, the effectiveness and advancement of the method proposed in this paper are verified. The organic combination of real-time monitoring, accurate prediction, and optimization decision-making in the machining process is realized which solves the problem of inconsistency between quality and efficiency of the machining process.     

Increasing CNC machine tool productivity by using tool-part touch control

In this paper a new system for increasing CNC machining productivity is described. The system is based on registering the moment when the cutting tool touches the workpiece during a machining operation. The cutting tool approaches the workpiece with rapid traverse and switches to work feed when it comes in contact with it. In this way, the time for ‘cutting air’ can significantly be reduced.     

The development of an in-process surface roughness adaptive control system in turning operations

This research shows the development of an in-process surface roughness adaptive control (ISRAC) system in turning operations. An artificial neural network (ANN) was employed to establish two subsystems: the neural network-based, in-process surface roughness prediction (INNSRP) subsystem and the neural network-based, in-process adaptive parameter control (INNAPC) subsystem. The two subsystems predicted surface roughness and adapted feed rate using data from not only cutting parameters (such as feed rate, spindle speed, and depth of cut), but also vibration signals detected by an accelerometer sensor. The INNSRP subsystem predicted surface roughness during the finish cutting process with an accuracy of 92.42%. The integration of the two subsystems led to the neural-networks-based surface roughness adaptive control (INNSRAC) system. The 100% success rate for adaptive control of the test runs proved that this proposed system could be implemented to adaptively control surface roughness during turning operations.     

Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.     

Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r² and root mean square error (RMSE), performed on training as well as checking datasets.     

PCBN tool wear modes and mechanisms in finish hard turning

The use of polycrystalline cubic boron nitride (PCBN) cutting tools in hard turning applications is continuously growing with the number of commercially available grades increasing, allowing new application areas to be explored. In order to take full advantage of the benefits offered by PCBN it is necessary to understand the behaviour of the material in application. Tool behaviour is influenced by many factors which include the composition of the PCBN material, the steel workpiece, the nature of the cutting operation, the cutting conditions and the tool geometry.

The focus of this paper is the continuous turning of hardened steels. A significant amount of research has been carried out in this area and a literature review of the relevant work is presented. This identifies the primary wear modes and discusses the many theories proposed to explain the mechanisms contributing to PCBN tool wear and failure. The final section of the paper considers the critical factors that influence the behaviour of PCBN tools in continuous hard turning and how this knowledge can be applied to optimise tool performance.     

Modelling machining processes with a view to their optimization and to the adaptive control of metal cutting machine tools

This paper describes an orthogonal machining theory which can be used to determine the stresses, temperatures etc. involved in chip formation from a knowledge of the work material flow stress and thermal properties and cutting conditions. It is shown how these can be used to predict machinability factors such as power consumption, built-up edge range, tool wear rates (tool life) and those cutting conditions which cause plastic deformation of the cutting edge. An oblique machining theory which is more representative of practical machining processes than the orthogonal theory is then described, taking into account machining on more than one cutting edge as in bar turning. Throughout the paper comparisons are made between predicted and experimental results.     

Tool wear modeling in rotary turning modified by ultrasonic vibration

Modification of conventional turning operation is carried out by using different methods to improve machinability conditions. In this study, rotary turning is modified by adding ultrasonic vibrations to cutting tool. Accordingly, the effect of this method on output parameters namely, tool wear and temperature, cutting force, and surface roughness, is investigated. Having detailed analysis, finite element method is used beside the experiments. As a result, it was revealed that tool-chip engagement time during rotary motion of cutting tool significantly reduced wear propagation on tool faces. This was explained by heat analysis in which disengagement time resulted in lower heat transfer from chip to tool. Moreover, the result of surface roughness produced in vibratory-rotary turning was compared by rotary one.     

Investigation and FEA-based simulation of tool wear geometry and metal oxide effect on cutting process variables

In machining, it is clearly noticed that the cutting tool wear influences the cutting process. However, it is difficult with experimental methods to study the effects of tool wear on several machining variables. Thus, in the literature, some earlier studies are performed separately on the effect of tool flank wear and crater wear on cutting process variables (such as cutting forces and temperature). Furthermore when the workpiece material adheres in cutting tool, it affects considerably the heat transfer phenomena. Accordingly, in this work the finite element analysis (FEA) is performed to investigate the influence of combination of tool flank and crater wear on the local or global variables such as cutting forces, tool temperature, chip formation on the one hand and the effects of the oxidized adhesion layer considered as oxide (Fe₂O₃/Fe₃O₄/FeO) on the heat transfer in cutting insert on the other hand. In this investigation, an uncoated cutting insert WC–6Co and medium carbon steel grade AISI 1045 are used. The factorial experimental design technique with three parameters (cutting speed Vc, flank wear land VB, crater wear depth KT) is used for the first investigation without adhesion layer. Then, only linear investigation is performed. The analysis has shown the influence of the different configurations of the tool wear geometry on the local or global cutting process variables, mainly on temperature and cutting. The simulation’s results show also, the highly influence of the oxidized adhesion layer (oxide Fe₂O₃/Fe₃O₄/FeO) on the heat transfer.     

Optimization studies in high speed turning of Ti-6Al-4V

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters R_a and R_t have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.     

Hybrid multiobjective optimization algorithm based on multivariate mean square error and fuzzy decision maker

This work presents the turning process of AISI H13 hardened steel with the PCBN 7025 tool, considering six output variables: tool life, machining total cost, surface roughness, machining force, sound pressure level, and specific cutting energy. Several problems are encountered in engineering processes that have adverse effects on the reliability of complex engineering systems. Hence, the aim of this work is to optimize the hardened steel turning process by applying mathematical methods to reduce dimensionality and eliminate the correlation between the multiple responses. The resultant latent response surfaces and their respective targets constitute the normalized multivariate mean square error (MMSE) function that is minimized by the normal boundary intersection (NBI) method. Furthermore, a fuzzy algorithm is applied to identify the best solution from several feasible solutions of the Pareto frontier that is compared with the performances of normalized normal constraint, arc homotopy length, global criterion method, and desirability method. The results show that NBI-MMSE has a higher performance than the other methods. In addition, NBI-MMSE is tested with benchmark functions to evaluate its effectiveness and robustness. Therefore, NBI-MMSE identifies the dynamics of the turning process of AISI H13 steel by revealing the optimal solutions for the input process parameters.     

Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Tool wear is a detrimental factor that affects the quality and tolerance of machined parts. Having an accurate prediction of tool wear is important for machining industries to maintain the machined surface quality and can consequently reduce inspection costs and increase productivity. Online and real-time tool wear prediction is possible due to developments in sensor technology. Recently, various sensors and methods have been proposed for the development of tool wear monitoring systems. In this study, an online tool wear monitoring system was proposed using a strain gauge-type sensor due to its simplicity and low cost. A model, based on the adaptive network-based fuzzy inference system (ANFIS), and a new statistical signal analysis method, the I-kaz method, were used to predict tool wear during a turning process. In order to develop the ANFIS model, the cutting speed, depth of cut, feed rate and I-kaz coefficient from the signals of each turning process were taken as inputs, and the flank wear value for the cutting edge was an output of the model. It was found that the prediction usually accurate if the correlation of coefficients and the average errors were in the range of 0.989–0.995 and 2.30–5.08% respectively for the developed model. The proposed model is efficient and low-cost which can be used in the machining industry for online prediction of the cutting tool wear progression, but the accuracy of the model depends upon the training and testing data.     

Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission

In modern manufacturing industry, developing automated tool condition monitoring system become more and more import in order to transform manufacturing systems from manually operated production machines to highly automated machining centres. This paper presents a nouvelle cutting tool wear assessment in high precision turning process using type-2 fuzzy uncertainty estimation on acoustic Emission. Without understanding the exact physics of the machining process, type-2 fuzzy logic system identifies acoustic emission signal during the process and its interval set of output assesses the uncertainty information in the signal. The experimental study shows that the development trend of uncertainty in acoustic emission signal corresponds to that of cutting tool wear. The estimation of uncertainties can be used for proving the conformance with specifications for products or auto-controlling of machine system, which has great meaning for continuously improvement in product quality, reliability and manufacturing efficiency in machining industry.     

Advanced adaptive feed control for CNC machining

In computer numerical control (CNC) machining, the tool feed rate is crucial for determining the machining time. It also affects the degree of tool wear and the final product quality. In a mass production line, the feed rate guides the production cycle. On the other hand, in single-time machining, such as for molds and dies, the tool wear and product quality are influenced by the length of machining time. Accordingly, optimizing the CNC program in terms of the feed rate is critical and should account for various factors, such as the cutting depth, width, spindle speed, and cutting oil. Determining the optimal tool feed rate, however, can be challenging given the various machine tools, machining paths, and cutting conditions involved. It is important to balance the machining load by equalizing the tool's load, reducing the machining time during no-load segments, and controlling the feed rate during high load segments. In this study, an advanced adaptive control method was designed that adjusts the tool feed rate in real time during rough machining. By predicting both the current and future machining load based on the tool position and time stamp, the proposed method combines reference load control curves and cutting characteristics, unlike existing passive adaptive control methods. Four different feed control methods were tested including conventional and proposed adaptive feed control. The results of the comparative analysis was presented with respect to the average machining load and tool wear, the machining time, and the average tool feed speed. When the proposed adaptive control method was used, the production time was reduced up to 12.8% in the test machining while the tool life was increased.     

An expert system approach for die and mold making operations

In the modern manufacturing of sophisticated parts with 3D sculptured surfaces, die and mold making operations are the most widely used machining processes to remove unwanted material. To manufacture a die or a mold, many different cutting tools are involved, from deep hole drills to the smallest ball nose end mills. Since the specification of each tool is very different from each other, each mold or die is specific with their complicated shapes and many machining rules exist to consider, a great deal of expertise is needed in planning the machining operations. An expert system (DieEX) developed for this purpose is described in the present work. The geometry and the material of the workpiece, tool material, tool condition and operation type are considered as input values and various recommendations about the tool type, tool specifications, work holding method, type of milling operation, direction of feed and offset values are provided.     

A model-based adaptive controller for chatter mitigation and productivity enhancement in CNC milling machines

Seeking a higher level of automation, according to Intelligent Manufacturing paradigm, an optimal process control for milling process has been developed, aiming at optimizing a multi-objective target function defined in order to mitigate vibration level and surface quality, while preserving production times and decreasing tool wear rate. The control architecture relies on a real-time process model able to capture the most significant phenomena ongoing during the machining, such as cutting forces and tool vibration (both forced and self-excited). For a given tool path and workpiece material, an optimal sequence of feedrate and spindle speed is calculated both for the initial setup of the machining process and for the continuous, in-process adaptation of process parameters to changes the current machining behavior. For the first time in the literature, following a Model-Predictive-Control (MPC) approach, the controller is able to adapt its actions taking into account process and axes dynamics on the basis of Optimal Control theory. The developed controller has been implemented in a commercial CNC of a 3-axes milling machine manufactured by Alesamonti; the effectiveness of the approach is demonstrated on a real industrial application and the performance enhancement is evaluated and discussed.