A new approach for filtering of surface profiles using morphological operations

Among different filtering approaches used to remove waviness and obtain roughness, envelope and motif techniques are considered to be suitable for functional characterization. Morphological filtering being closely related to envelope technique can be used for filtering of surfaces. This paper discusses a new approach combining morphological operations namely closing and opening with circle as structuring element in a unique way to generate the reference profile representing waviness. The reference profile generated by this method is smooth without any slope discontinuities and robust against deep valleys present in the profile. Analysis of the proposed method for robustness and form-approximation capabilities is presented in this paper. Comparison is also made with envelope and morphological closing filters as well as Gaussian regression filter to bring out the effectiveness of this approach.     

A formal approach to specifying assembly operations

Experience has proved that exploiting robots for assembly tasks is much more difficult than manufacturing engineers had expected and many attempts at implementing robotic assembly have failed. Our research has led us to believe that a formal approach to specifying the steps required for assembly would be of great benefit in developing the required software for a specific task, and in adaptively controlling and monitoring the execution of robotic assembly steps. The US National Bureau of Standards has developed a formal system, called ABC (for Assembly By Constraints) for specifying the steps required for assembly. The system is based on the reduction in the degrees of freedom of objects as they are assembled. Using this basic concept, we have developed 14 primitive operations which can be used to completely specify assembly steps for a large class of problems. This paper initially outlines the historical development of the system, then describes two pieces of software developed to allow easy definition of assembly tasks using the ABC system, and finally presents two practical examples.     

A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations

A genetic algorithm (GA) based optimization procedure has been developed to optimize grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using multi-objective function model with a weighted approach for surface grinding process. The procedure evaluates the production cost and production rate for the optimum grinding condition, subjected to constraints such as thermal damage, wheel wear parameters, machine tool stiffness and surface finish. New GA procedure is illustrated with an example and optimum results such as production cost, surface finish, metal removal rate are compared with quadratic programming techniques.     

基于PLC控制的数控钻孔机的设计与实现

PLC对步进电机进行开环控制实现钻孔机的分度定位和进给定位,并用人机界面设置工艺参数。文中阐述了钻孔机的机械原理和组成以及其控制系统的硬件和软件。步进控制指令方便地解决了自动顺序控制程序,带加减功能的脉冲输出指令有效地解决了步进电机开环控制。     

An accurate,efficient envelope approach to modeling the geometric deviation of the machined surface for a specific five-axis CNC machine tool

Geometric deviation, defined as the difference between the nominal surface and the simulation model of the machined surface, is the fundamental concern of five-axis tool path planning. Since the machined surface is part of the cutter envelope surface generated by the cutter motion, it is necessary to calculate the envelope surface in order to obtain the geometric deviation. In the stage of tool path planning, current approaches calculate the cutter envelope surface by using the cutter motion along the given tool path. However, the cutter motion of practical machining on a specific five-axis CNC machine tool is different from the given tool path. Moreover, the computation is very challenging when the accurate cutter motion of practical machining is applied to calculate the envelope surface. To overcome these two problems, a geometric envelope approach with two major distinctions is proposed in this paper. First, the envelope surface of the cutter undergoing a general motion is efficiently obtained as a closed-form vector expression. Second, the accurate cutter motion, which is determined by machine kinematic and interpolation scheme in practical machining, can be easily applied to calculate the accurate envelope surface. With the envelope surface, the geometric deviation is calculated to estimate the overcut or undercut in five-axis milling. An example is given to demonstrate the validity of the proposed method.     

A mechanistic approach to investigate drilling of UD-CFRP laminates with PCD drills

Carbon fiber reinforced plastics (CFRPs) possess desirable material properties that satisfy the aerospace industry's high strength to weight ratio objective. Therefore, CFRPs are commonly used in structural parts, either alone or together with aluminum and titanium alloys. Drilling of CFRPs has been studied extensively in the literature in recent years, with special emphasis on process parameters and delamination. This study identifies mechanical properties of uni-directional CFRPs through drilling tests. Drilling of uni-directional CFRP plates with and without pilot holes has been performed, and cutting and edge force coefficients are identified. A polycrystalline diamond (PCD) drill was used in tests since this type of drill is commonly used in practice. Finally, validation tests on multi directional CFRP laminates have been performed and good results have been obtained.     

A computer algorithm for flank and crater wear estimation in CNC turning operations

In order to increase the productivity of turning processes, several attempts have been made in the recent past for tool wear estimation and classification in turning operations. The tool flank and crater wear can be predicted by a number of models including statistical, pattern recognition, quantitative and neural network models. In this paper, a computer algorithm of new quantitative models for flank and crater wear estimation is presented. First, a quantitative model based on a correlation between increases in feed and radial forces and the average width of flank wear is developed. Then another model which relates acoustic emission (AE_rms) in the turning operation with the flank and crater wear developed on the tool is presented. The flank wear estimated by the first model is then employed in the second model to predict the crater wear on the tool insert. The influence of flank and crater wear on AE_rms generated during the turning operation has also been investigated. Additionally, chip-flow direction and tool–chip rake face interfacing area are also examined. The experimental results indicate that the computer program developed, based on the algorithm mentioned above, has a high accuracy for estimation of tool flank wear.     

On-line tool wear estimation in CNC turning operations using fuzzy neural network model

In recent past, several neural network models which employ cutting forces and AErms or their derivatives for estimation as well as classification of flank wear have been developed. However, a significant variation in mean cutting forces and AErms at the start of cutting operation for similar new tools can result in estimation and classification error. In order to deal with this problem, a new on-line fuzzy neural network (FNN) model is presented in this paper. This model has four parts. The first part of the model is developed to classify tool wear by using fuzzy logic. The second part of this model is designed for normalizing the inputs for the next part. The third part consisting of modified least-square backpropagation neural network is built to estimate flank and crater wear. The development of forth part was done in order to adjust the results of the third part. Several basic and derived parameters including forces, AErms, skew and kurtosis of force bands, as well as the total energy of forces were employed as inputs in order to enhance the accuracy of tool wear prediction. The experimental results indicate that the proposed on-line FNN model has a high accuracy for estimating progressive flank and crater wear with small computational time.     

Diagnosis of tapping operations using an AI approach

On-line diagnosis of the machining operations is essential because it can prevent potential problems caused by abnormal conditions. So far, little research has been done for the on-line diagnosis of tapping operations. However, tapping is a very important machining operation. And the on-line diagnosis is essential. In this work, an artificial neural network was utilized for the diagnosis of the tapping operation. The input vector for the neural network is obtained by processing the signals of the thrust, torque, and lateral forces during the tapping operations. A total of ten indices were used in the input layer of the network. The output of the artificial neural network provides the tapping states. Five different tapping states were investigated: normal operation, tap wear, misalignment, oversize hole, and undersize hole. The weights and thresholds of the artificial neural network can be modified by the generalized delta rule. Experimental results showed that diagnosis of tapping operations through an artificial neural network can reach a success rate of over 95%.     

Optimization of hole-making operations: a tabu-search approach

This paper reports a tabu-search approach to minimize the total processing cost for hole-making operations. Four issues, namely, tool travel scheduling, tool switch scheduling, tool selection, and machining speed specification have been simultaneously addressed in this study. The total processing cost consists of tooling cost, machining cost, non-productive tool travelling cost, and tool switching cost. This problem has a structure similar to the Travelling Salesman Problem (TSP) and hence is NP-complete. In addition, the problem under consideration is more complex since the cost associated with each operation is both sequence-dependent and position-dependent. To provide an efficient solution procedure, a tabu search approach is used. To improve the search performance two new neighbourhood generation and move selection policies have been proposed and tested. The decisions on the above issues can be made simultaneously based on the output of the proposed algorithm. The results obtained from computational experiments show that the total processing cost can be significantly reduced within a reasonable search time. The effects of some search parameters and diversification strategies on the search performance have also been investigated.     

A human modelling and monitoring approach to support the execution of manufacturing operations

Human workers have a vital role in manufacturing given their adaptability to varying environmental conditions, their capability of judgment and understanding of the context. Nevertheless, the increasing complexity and variety of manufacturing operations ask for the exploitation of digital technologies to support human workers and/or facilitate their interaction with automation equipment. The proposed approach uses artificial intelligence for image processing to identify the actions of the workers and exploits the knowledge related to the processes through hidden-Markov models to identify possible errors, deviations from the planned execution or dangerous situations. An application case is provided for assembly operations to assess the viability of the proposed approach in realistic conditions.     

RG-150系列数控液压折弯机床身动态响应分析

根据板料折弯时的工作情况分析，确定了数控液压折弯机的动载荷谱，并通过傅立叶展开，获得折弯同的幅频谱。最后通过MARC软件采用模态叠加法计算了典型折弯时床身的动态响应，分析了其动态特性。     

Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)

Monitoring of tool wear condition for drilling is a very important economical consideration in automated manufacturing. Two techniques are proposed in this paper for the on-line identification of tool wear based on the measurement of cutting forces and power signals. These techniques use hidden Markov models (HMMs), commonly used in speech recognition. In the first method, bargraph monitoring of the HMM probabilities is used to track the progress of tool wear during the drilling operation. In the second method, sensor signals that correspond to various types of wear status, e.g., sharp, workable and dull, are classified using a multiple modeling method. Experimental results demonstrate the effectiveness of the proposed methods. Although this work focuses on on-line tool wear condition monitoring for drilling operations, the HMM monitoring techniques introduced in this paper can be applied to other cutting processes.     

A real-time NURBS surface interpolator for precision three-axis CNC machining

Due to the fact that the cutting occurs around the cutter contact (CC) point, the efficiency and quality of CNC machining can be improved significantly if the CC velocity along the surface is kept costant. Conventional approaches to machining mainly maintain a constant cutter location (CL) velocity, so that the CC velocity along the surface is often not constant and usually results in non-uniform machining and unsatisfactory quality. To overcome this difficulty, this paper presents a novel NURBS surface interpolator that is capable of real-time generation of CL motion command for ball-end milling of NURBS surfaces and maintaining a constant CC velocity along the CC path and its intervals. For performance evaluation, a three-axis servomechanism driven by three servomotors is controlled to track segments represented by NURBS surfaces. Experimental results verify the effectiveness of the proposed method.     

Sculptured surface machining of spiral bevel gears with CNC milling

Gears are crucial components for modern precision machinery as a means for the power transmission mechanism. Due to their complexity and unique characteristics, gears have been designed and manufactured by a special type of machine tools, such as gear hobbing and shaping machines. In this paper, we attempt to manufacture the spiral bevel gear (SBG: the most complex type among the gear products) by a three-axis CNC milling machine interfaced with a rotary table. This consists of (a) geometric modeling of the spiral bevel gears, (b) process planning for NC machining, (c) a tool path planning and execution algorithm for both 4-axis and 3/4-axis (three out of four axes) controls. Experimental cuts were made to ascertain the validity and effectiveness of the presented method with a CNC milling machine controlled by the 3/4-axis control mode.     

A probabilistic approach to predict surface roughness in ceramic grinding

The quality of the surface produced during ceramic grinding is important as it influences the performance of the finished part to great extent. Hence, the estimation of surface roughness can cater to the requirements of performance evaluation. But, the surface finish is governed by many factors and its experimental determination is laborious and time consuming. So the establishment of a model for the reliable prediction of surface roughness is still a key issue for ceramic grinding. In this study, a new analytical surface roughness model is developed on the basis of stochastic nature of the grinding process, governed mainly by the random geometry and the random distribution of cutting edges. This model has been validated by the experimental results of silicon carbide grinding. The theoretical analysis yielded values which agree reasonably well with the experimental results.     

Identification of common sensory features for the control of CNC milling operations under varying cutting conditions

The main focus of this study is to identify the most influential and common sensory features for the process quality characteristics in CNC milling operations—dimensional accuracy (bore size tolerance) and surface roughness—using three different material types (6061-T6 aluminum, 7075-T6 aluminum, and ANSI-4140 steel). The materials were machined on a vertical CNC mill, retrofitted with multiple sensors and data acquisition systems, to investigate the effects of variations in material types and machining parameters. The sensor data include cutting force measurements, spindle quill vibration, and acoustic emission, each of which further divided into measurable components, such as x, y, and z components in cutting force, x and y spindle quill vibration, DC, AC, and Count Rate for acoustic emission signals. Those components were filtered and analyzed to determine the sensory features that best correlate with process quality characteristics. Tool wear rate and machining characteristics appeared differently, depending on the material types, yet some components of the sensory data were found to be significant with relation to the variations in bore size and surface roughness for all three types of materials. This suggests that even under the varying cutting conditions involving different materials, the identified sensory features can be used for the reliable and accurate control of milling operations.     

Transformation of the EFQM approach from business towards operations excellence

The purpose of this paper is to provide an enabler-based approach for a supervised self-assessment of operations excellence (OsE). Based on the latest approaches of excellence in operations from the literature as well as on the philosophy of the EFQM model, an OsE working definition was developed. In contrast to operational excellence (OE), which deals exclusively with the optimization of result driven processes, OsE promotes the enhancement of operation-specific enablers and linked results. The evaluation of crucial cause–effect relationships of relevant enabler and result criteria supports the determination of a company’s capability to achieve sustainability and excellence in terms of its operations. To foster a methodical integration of OsE in organizations, a phase model for a systematic assessment process was designed and verified with 24 companies in the Austrian machinery and metalware industries.     

A new approach to identifying the dynamic behavior of CNC machine tools with respect to different worktable feed speeds

The dynamics of the machine tool structure are important in high precision machining. Some researchers have studied that the dynamics are expected to change under different machining conditions. However, the dynamic behaviors of the machine tool at different worktable feed speeds are rarely studied. In this paper, an output-only modal identification available to predict the dynamics of the machine tool at different feed speeds is proposed. The excitation of this method uses the inertia force sequence caused by random idle running of the worktable. The first six modes of the entire machine tool structure are estimated using the proposed method. The results indicate that the running state of the worktable can influence the modes in which the worktable vibrates. The estimated natural frequencies and damping ratios decrease obviously as the feed speed increases. Furthermore, because this method enable to determine modal parameters by measuring the response of machine tool structure without using any artificial excitation, it can be used to predict the dynamic behaviors of the machine tool in entire working space effectively.     

Response surface approach to optimize the pulsed current gas tungsten arc welding parameters of Ti−6Al−4V titanium alloy

Titanium alloy (Ti−6Al-−4V) is very widely used in the fabrication of advanced industrial equipment, combat vehicles, gas turbines, spacecraft and so on. The preferred welding process for titanium alloy is gas tungsten arc (GTA) welding due to its comparatively easier applicability and better economy. However, welding of titanium alloy leads to grain coarsening at the fusion zone and the heat-affected zone, and this often results in inferior weld mechanical properties and poor resistance to hot cracking. Hence, in this investigation an attempt has been made to refine the fusion zone microstructure of titanium alloy by using a pulsed current GTA welding process instead of a constant current GTA welding process. Further mathematical models were developed by means of a response surface method, which enabled the process parameters to be optimized to achieve a minimum grain size and maximum hardness in GTA welding of the alloy under study. The parameter optimization involved the use of a response surface, contour plots and Kuhn-Tucker conditions.