Optimization of multi-task turning operations under minimal tool waste consideration

Most of the literatures on machining economics problems tend to focus on single cutting operations. However, in reality most parts that need to be machined require more than one operation. In addition, machining technology has been developed to the point that a single computer numerical control (CNC) machine is capable of performing multiple operations, even simultaneously, employing multiple spindles and cutting tools. When several operations are performed on a CNC turning machine, various tools are required for the cutting operations. Determining the life of these cutting tools under different machining conditions is an arduous task for the operators. They usually replace the tools based on their experience or according to the specific cutting tool handbook. Frequent tool replacements may result in wasted tools and tool utilization, while infrequent tool replacements may result in poorly machined parts. In this study we propose a mathematical model in which several different turning operations (turning, drilling, and parting) with proper constraints are performed. The issue of tool replacement is taken into account in the proposed cutting model. In addition, an evolutionary strategy (ES)-based optimization approach is developed to optimize the cutting conditions of the multiple turning-related operations while taking into account the minimizing unit cost criteria under the economical tool replacement strategy.     

Advanced process monitoring—a major step towards adaptive control

Adaptive Control (AC) of machine tools requires many kinds of measured input data. The more information about the complex metal cutting process that can be obtained, the better the process can be controlled.

The paper describes an Adaptive Control Optimization (ACO) system for turning operations. The system continuously chooses Optimal Cutting Data (OCD), taking into account both economical criteria and technical limitations.

The system operates at three different levels:

• • Advanced Process Monitoring

• • Adaptive Control Constraint (ACC)

• • Adaptive Control Optimization (ACO).

Two commercial monitoring systems perform process monitoring. In addition, five independent measurement systems have been developed.

A dedicated vision system has been installed in the lathe to measure the tool flank wear between cuts. The flank wear data are utilized to predict the tool life. Based upon these predictions economical optimum cutting data can be calculated at the ACO level.

To obtain in-process real-time control of the metal cutting process the cutting forces are measured during machining. The forces are measured with conventional piezoelectric force transducers which are located between the turret housing and the cross-slide. The measured force signals are processed by a dedicated microcontroller at the ACC level and cutting data adjustments are fed back to the machine control.

A vibration measurement system, which either can be connected to an accelerometer or use the dynamic force signal from the piezoelectric force transducer, is part of a vibration control module at the ACC level. An ultra-fast signal processor performs the signal analysis.

The remaining two measurement systems—a high frequency tool signal analysis system and a power spectra analysis system—are mentioned in the paper but not further discussed.

Finally, the paper deals with how the strategies at the three different levels will be combined, in order to form an AC system. The monitoring tasks will always reside in the background and be activated if any failure occurs. The ACO subsystem will act as a path-finder and suggest cutting data. The active control tasks will, however, be carried out at the ACC level.     

Modelling and implementing circular sawblade stone cutting processes in STEP-NC

The paper presents a STEP-NC compliant implementation of circular sawblade stone cutting machining processes. Although some stone machining processes has been already covered in the STEP-NC research and standardization initiatives (as for instance stone machining through stone milling machines), there have not been yet, however, any detailed model proposal to cover circular sawblade stone cutting operations. Sawblade cutting technology for stone parts have several specific parameters with no clear equivalent technologies as defined in milling, turning, etc. The paper reviews main characteristics of the circular sawblade stone cutting machining operations, and proposes a STEP-NC extended model based on the selection and definition of new features and on the modelling of these stone cutting operations. The resulting model is the base for the development of the STEP-NC stone cutting CAM and CNC machine. The machine architecture is designed to be able to react to changes in the machining conditions, very common in this technology. The system is based on the definition of features to be communicated to the controller. The controller has the objective of machining the features, and it is able to re planning, on real time, the work to get them despite changing conditions in the stone or in the disc.     

Intelligent Programming of CNC Turning Operations using Genetic Algorithm

CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In the next step, GA operations such as reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement, collision, clamping and minimum machining time, which represent the fitness function, were taken into account. A case study was made for the turning operation of a rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16%. The demand for a high skilled worker on CAD/CAM systems and CNC machine tools was also reduced. Received: September 2004 / Accepted: September 2005     

中国微米纳米-三维集成车削力传感器数据采集与分析系统设计

切削力与切屑形成、切削热、刀具磨损和切削振动等现象有着密切联系,是影响加工精度、刀具寿命和切削效率的重要因素。通过实时测量切削力,及时调整切削参数、优化切削工艺,对于保证加工质量、延长刀具寿命、提高切削效率等有着重要意义。切削力的准确测量和处理离不开优良的数据采集与分析系统,本文针对基于MEMS压阻式芯片的三维集成车削力传感器,以微处理器STM32为控制核心研制了一种三维集成车削力传感器数据采集与分析系统,实现了三维车削力的标定、实时采集和数据分析功能。     

Hierarchical optimal force–position–contour control of machining processes

There has been a tremendous amount of research in machine tool servomechanism control, contour control, and machining force control; however, to date these technologies have not been tightly integrated. This paper develops a hierarchical optimal control methodology for the simultaneous regulation of servomechanism positions, contour error, and machining forces. The contour error and machining force process reside in the top level of the hierarchy where the goals are to (1) drive the contour error to zero to maximize quality and (2) maintain a constant cutting force to maximize productivity. These goals are systematically propagated to the bottom level, via aggregation relationships between the top and bottom-level states, and combined with the bottom-level goals of tracking reference servomechanism positions. A single controller is designed at the bottom level, where the physical control signals reside, that simultaneously meets both the top and bottom-level goals. The hierarchical optimal control methodology is extended to account for variations in force process model parameters and process parameters. Simulations are conducted for four machining operations that validate the developed methodology. The results illustrate the controller can simultaneously achieve both the top and bottom-level goals.     

Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation

Although machine tool can meet the specifications while it is new, after a long period of cutting operations, the abrasion of contact surfaces and deformation of structures will degrade the accuracy of machine tool due to the increase of the geometric errors in six freedoms. Therefore, how to maintain its accuracy for quality control of products is of crucial importance to machine tool. In this paper, machining accuracy reliability is defined as the ability to perform its specified machining accuracy under the stated conditions for a given period of time, and a new method to analyze the sensitivity of geometric errors to the machining accuracy reliability is proposed. By applying Multi-body system theory, a comprehensive volumetric model explains how individual geometric errors affect the machining accuracy (the coupling relationship) was established. Based on Monte Carlo mathematic simulation method, the models of the machining accuracy reliability and sensitivity analysis of machine tools were developed. By taking the machining accuracy reliability as a measure of the ability of machine tool and reliability sensitivity as a reference of optimizing the basic parameters of machine tools, an illustrative example of a three-axis machine tool was selected to demonstrate the effectiveness of the proposed method.     

Condition monitoring of CNC machining using adaptive control

In this work, an adaptive control constraint system has been developed for computer numerical control (CNC) turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint (ACC) machining systems for turning. AISI4140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.     

Estimation of cutting forces and surface roughness for hard turning using neural networks

Metal cutting mechanics is quite complicated and it is very difficult to develop a comprehensive model which involves all cutting parameters affecting machining variables. In this study, machining variables such as cutting forces and surface roughness are measured during turning at different cutting parameters such as approaching angle, speed, feed and depth of cut. The data obtained by experimentation is analyzed and used to construct model using neural networks. The model obtained is then tested with the experimental data and results are indicated.     

Micromechanical machining of high aspect ratio prototypes

 Micromechanical machining uses physical cutting tools in high precision machines to fabricate parts with micrometers features and sub-micrometer tolerances. An advantage of this process is the ability to use any machinable material, quick process planning and material removal, and three-dimensional geometry only limited by the machine and tools used. Disadvantages are that forces are placed on micro cutting tools causing deflection and possible breaking. Deflection reduces process precision and tool breakage results in repeated set up, slower production, and poorer tolerances. Nevertheless, these processes have created many diverse prototypes ranging from biomedical to space applications. Received: 10 August 2001/Accepted: 24 September 2001     

Cutting parameters analysis for the development of a milling process monitoring system based on audible energy sound

Monitoring of machining processes is a critical requirement in the implementation of any unmanned operation in a shop floor and, particularly, in the establishment of Flexible Manufacturing Systems (FMS) and Computer Integrated Manufacturing (CIM) where most of the operations are carried out in an automated way. During the last years, notable efforts have been made to develop reliable and robust monitoring systems based on different types of sensors such as cutting force and torque, motor current and effective power, vibrations, acoustic emission or audible sound energy. This work is focused on this last sensor technology. The basic objective is to characterise the audible sound energy signals generated during different machining operations carried out on a milling machine. In order to achieve this, rotation speed, feed and depth of cut have been analysed separately. The main contributions of this work are, on the one hand, the application of a systematic methodology to set up the cutting tests and, on the other hand, the independent signal analysis of the noise generated by the milling machine used for the cutting tests in order to filter this noise out from the signals obtained during the actual material processing. The classification of audible sound signal features for process monitoring has been obtained by graphical analysis and parallel distributed data processing using a supervised neural network (NN) paradigm.     

Agile monitoring system for turning of difflicult-to-cut materials

This paper presents an agile monitoring system based on multiple sensors and fuzzy pattern recognition for the detection of tool cutting conditions in turning operations. Three piezoelectric force sensors and a thermocouple (NiCr-Nial) sensor were used to measure cutting force and temperature without altering the machine tool dynamics. Fuzzy C-means algorithm is used to improve the agility of selecting, clustering and classifying cutting tool conditions.     

Advanced CNC system with in-process feed-rate optimisation

Tight quality requirements and stringent customer demands are the main thrust behind the development of new generation machine tool controllers that are more universal, adaptable and interoperable. The development of some international standards such as STEP and STEP-NC presents a vision for intelligent CNC machining. Implementation of STEP-NC enabled Machine Condition Monitoring (MCM) is presented in this paper. The system allows optimisation during machining in order to shorten machining time and increase product quality. In the system, an optiSTEP-NC, an AECopt controller and a Knowledge-Based Evaluation (KBE) module have been developed. The aim of the optiSTEP-NC system is to perform initial feed-rate optimisation based on STEP-NC data to assist process planners in assigning appropriate machining parameters. AECopt acts as a connector between the process planner and machining environment with the intention to provide adaptive and automatic in-process machining optimisation. KBE based-MTConnect is responsible for obtaining machining know-how. Optimisation is performed before, during or after machining operations, based on the data collected and monitored such as machining vibration, acceleration and jerk, cutting power and feed-rate.     

Simulation study on the improvements of machining accuracy by using smart materials

This work utilizes smart material to counteract the radial disturbing cutting forces and reduce machining error in the turning process. The finite element method (FEM) is employed to explore the capability of such a method in controlling tool position. Toolpost dynamic response is investigated where the pulse width modulation (PWM) technique is launched for actuator voltage input. The result from tool response using dynamic absorber does not encourage the use of such a vibration attenuator in error elimination in the presence of the PWM voltage input. Even though increasing toolpost damping within a reasonable range shows a reduction in toolpost error, major improvement is noticed by modifying the PWM voltage level and its time duration. For error elimination, the estimate of static actuator voltage does not reflect the actual level of required dynamic applied voltage. This work also emphasizes the importance of tool bit to actuator stiffness and tool carrier (holder) to actuator stiffness in reducing tool positional error.     

Improved prediction of stability lobes in milling process using time series analysis

In this paper, a new method is presented for prediction of cutting forces, surface texture and stability lobes in end milling operation based on time series analysis. In the approach, an equivalent damping ratio is defined for the cutting zone while the damping ratio of non-cutting zone is determined by experimental modal analysis. Using correlation dimension criterion, the simulation and experimental force signals are compared to anticipate the value of process damping by assessing the variation of correlation dimension for both signals. The effect of cutter deflections and run out are taken into account. Moreover, the stability lobes are predicted by considering the variation of process damping with cutting conditions. The feasibility of the proposed algorithm is verified experimentally for machining of Aluminum 7075-T6. Comparison of experiment results against simulation results indicates that the improved model can accurately predict cutting forces, surface texture and stability lobes for low radial immersion.     

Prediction of stable cutting depths in turning operation using soft computing methods

This article suggests soft computing methods to predict stable cutting depths in turning operations without chatter vibrations. Chatter vibrations cause poor surface finish. Therefore, preventing these vibrations is an important area of research. Predicting stable cutting depths is vital to determine the stable cutting region. In this study, a set of cutting experiments has been used and the stable cutting depths are predicted as a function of cutting, modal and tool-working material parameters. Regression analyses, artificial neural networks (ANN) decision trees and heuristic optimization models are used to develop the generalization models. The purpose of the models is to estimate stable cutting depths with minimum error. ANN produces better results compared to the other models. This study helps operators and engineers to perform turning operations in an appropriate cutting region without chatter vibrations. It also helps to take precautions against chatter.     

Prediction of cutting forces in 3-axes milling of sculptured surfaces directly from CAM tool path

In the present work a new approach for the modelling of milling is described. The cutting forces are calculated for milling operations directly from the tool path provided by a Computer Assisted Manufacturing program. The main idea consists in using tool position points coming from CAM data in order to calculate the local inclination angle of the generated surface and then the tool engagement in the machined material. A good approximation for global and local cutting forces can be obtained when an analytical model able to predict the cutting forces for 3-axes milling is used. Two approaches are proposed to calculate the local cutting forces to show the versatility of the method. The first method uses a thermomechanical approach using a Johnson & Cook constitutive law while the second is based on classical cutting coefficients. Some results are presented for wavelike form and free form machining tests and are compared with experimental data obtained in roughing and finishing of 42CrMo4 steel. Results are satisfactory and the capability of the method to predict the resultant surface roughness is shown.     

Development of a new meso-scale machine tool for fabricating micro V-grooves

This work presents the development of a meso-scale machine tool with a nanometer resolution. The newly developed meso-scale machine tool consists of a pagoda structure for Z-axis, four HR8 ultrasonic motors, three linear encoders with a resolution of 2 nm, a coaxial counter-balance system, a XY coplanar positioning stage, a rotary stage, a Galil 4-axis motion control card, an industrial PC and a CCD camera system. The optimal geometrical dimensions of the pagoda structure have been determined by ANSYS software. The designed meso-scale machine tool is equipped with an X–Y coplanar positioning stage with nanometer resolution. The coplanar stage developed by National Taiwan University was integrated with two linear encoders, so that a two-axis closed-loop control was possible. A circular positioning test with the radius of 1 mm using the developed stage was tested, and the overall circular positioning error was about 83 nm based on the test results. The micro V-grooves and the micro pyramid cutting tests of the polished oxygen free copper using a single crystal diamond tool on the developed meso-scale machine tool have been performed. The cutting tests under various combination of the depth of cut and cutting speed have been carried out. It revealed that the cutting speed had no great influence on the cutting force. The measured cutting forces for the depth of cut of 5, 10, 15 μm were 1.2, 1.6 and 2.4 N, respectively. The results showed the meso-scale machining tool can be used in micro pyramid structures manufacturing.     

A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information

This paper presents a neural network approach to multiple-objective cutting parameter optimization for planning turning operations. Productivity, operation cost, and cutting quality are considered as criteria for optimizing machining operations. A feedforward neural network and a dynamic training procedure are proposed for modeling manufacturers' preferences using sampled fuzzy preferential data. Optimum cutting parameters are determined based on neural network representations of manufacturers' fuzzy preference structures.     

Prediction of workpiece elastic deflections under cutting forces in turning

One of the problems faced in turning processes is the elastic deformation of the workpiece due to the cutting forces resulting in the actual depth of cut being different than the desirable one. In this paper, a cutting mechanism is described suggesting that the above problem results in an over-dimensioned part. Consequently, the problem of determining the workpiece elastic deflection is addressed from two different points of view. The first approach is based on solving the analytical equations of the elastic line, in discretized segments of the workpiece, by considering a stored modal energy formulation due to the cutting forces. Given the mechanical properties of the workpiece material, the geometry of the final part and the cutting force values, this numerical method can predict the elastic deflection. The whole approach is implemented through a Microsoft Excel^© workbook. The second approach involves the use of artificial neural networks (ANNs) in order to develop a model that can predict the dimensional deviation of the final part by correlating the cutting parameters and certain workpiece geometrical characteristics with the deviations of the depth of cut. These deviations are calculated with reference to final diameter values measured with precision micrometers or on a CMM. The verification of the numerical method and the development of the ANN model were based on data gathered from turning experiments conducted on a CNC lathe. The results support the proposed cutting mechanism. The numerical method qualitatively agrees with the experimental data while the ANN model is accurate and consistent in its predictions.