Flank wear prediction in drilling using back propagation neural network and radial basis function network

In the present work, two different types of artificial neural network (ANN) architectures viz. back propagation neural network (BPNN) and radial basis function network (RBFN) have been used in an attempt to predict flank wear in drills. Flank wear in drill depends upon speed, feed rate, drill diameter and hence these parameters along with other derived parameters such as thrust force, torque and vibration have been used to predict flank wear using ANN. Effect of using increasing number of sensors in the efficacy of predicting drill wear by using ANN has been studied. It has been observed that inclusion of vibration signal along with thrust force and torque leads to better prediction of drill wear. The results obtained from the two different ANN architectures have been compared and some useful conclusions have been made.     

APPLICATION OF ABDUCTIVE POLYNOMIAL NETWORK AND GREY THEORY TO DRILL FLANK WEAR PREDICTION

An abductive polynomial network for drill flank wear prediction was established, in which grey relational analysis was incorporated to explore the effect of various drilling parameters on flank wear. An abductive polynomial network usually includes multiple layers, each of which contains different polynomial functional nodes. It can automatically synthesize the optimal network structure, including the optimal number of layers and the optimal form of functional nodes. The correlation between the drilling input parameters, including the average thrust force, torque, cutting speed, feed and drill diameter, and drill flank wear can be achieved through this network model.

Based on experimental data, the developed network of this paper attained better accuracy in predicting drill flank wear, given the CPM of 0.1. The findings prove that the network is feasible and accurate in predicting flank wear.

In addition, grey relational analysis was used in this paper to investigate the effect of the aforementioned five drilling parameters on flank wear. According to the analytical results, the most influential factor on flank wear is drill diameter, followed by the average thrust force.     

人工神经网络预测刀具磨损和切削力

刀具磨损和切削力预测与控制是切削加工过程中需要考虑的重要问题.本文介绍了利用人工神经网络模型预测刀具磨损和切削力的步骤并且针对产生误差的因素进行分析.首先将切削速度、切削深度、切削时间、主轴转速和不同频带的能量值通过归一化法处理,作为输入特征值,对改进的神经网络模型进行训练.然后利用训练完成的神经网络模型预测刀具磨损和切削力.结果表明:神经网络模型能够综合考虑加工过程中更多的影响因素,与经验公式结果对比,具有更高的预测精度.研究结果表明神经网络模型预测刀具磨损和切削力具有可行性和准确性,为刀具结构的优化及加工参数的选择提供了依据.     

Computer numerical control for assuring the machining accuracy of contour milling

In this paper, a software technology for improving the machining accuracy in contour milling is discussed, in which the continuous path control is thoroughly investigated from the viewpoint of system synthesis, and the computer numerical control is effectively used. It is shown that the proposed “real-time cutter path rectification” offers an effective means to overcome the serious problem of the thermal deformation of workpieces. In this case, it is necessary to take many factors into consideration; the diversity of shapes, the change of cutting conditions, the unstable thermal situation, and so on. Therefore, the adaptive control is applied to compensate the thermal displacement of the contour during the cutting process. Relating to this subject, the effective cutter radius, which depends on cutter wear, is also evaluated in real-time operation; and the cutter diameter compensation is included in the “cutter path rectification”. In order to assure the machining accuracy, a new approach to contour measurement is proposed, in which the continuous path control by CNC system is used. It is certified through some experiments that the method proposed in this paper is useful to realize the flexible automation with high machining accuracy.     

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.     

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.     

Realization of STEP-NC enabled machining

A STEP-compliant CNC machine tool that demonstrated a G-code free machining scenario is presented. The aim of this research is to showcase the advantages of, and evaluate, STEP-NC—a new NC data model—by implementing it in a legacy CNC system. The work consists of two parts: retrofitting an existing CNC machine and the development of a STEP-compliant NC Converter called STEPcNC. The CompuCam's motion control system is used for retrofitting the machine, which is programmable using its own motion control language—6K Motion Control language and capable of interfacing with other CAPP/CAM programs through languages such as Visual Basic, Visual C++ and Delphi. STEPcNC can understand and process STEP-NC codes, and interface with the CNC controller through a Human Machine Interface. It makes use of STEP-NC information such as “Workplan”, “Workingstep”, machining strategy, machining features and cutting tools that is present in a STEP-NC file. Hence, the system is truly feature-based. The Application Interpreted Model of STEP-NC has been used.     

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.     

A manufacturing XML schema definition and its application to a data management system on the shop floor

Digitization for sharing knowledge on the shop floor in the machinery industry has been given much attention recently. To help engineers use digitization practically and efficiently, this paper proposes a method based on manufacturing case data that has a direct relation to manufacturing operations. The data are represented in XML schema, as it can be easily applied to Web-based systems on the shop floor. The definitions were made for eight manufacturing methods including machining and welding. The derived definitions consist of four divisions of metadata, work-piece, process and evaluation. Three divisions except for the “process” division are common to the manufacturing methods. The average number of elements for a manufacturing method is about 200. The represented schema is also used to convey knowledge such as operation standards and manufacturing troubleshooting on the shop floor. Using the definitions, a data management system is developed. It is a Web-based Q&A system, in which the engineers specify the manufacturing case data mainly by selecting from the candidates. Then, the system fills in the blank portions and/or shows messages to help complete the case data. The proposed method is evaluated through practical scenarios of arc welding and machining.     

Neural control strategy of constant cutting force system in end milling

This paper discusses the application of neural adaptive control strategy to the problem of cutting force control in high speed end milling operations. The research is concerned with integrating adaptive control and a standard computer numerical controller (CNC) for optimizing a metal-cutting process. It is designed to adaptively maximize the feed rate subject to allowable cutting force on the tool, which is very beneficial for a time consuming complex shape machining. The purpose is to present a reliable, robust neural controller aimed at adaptively adjusting feed rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. Numerous simulations and experiments are conducted to confirm the efficiency of this architecture.     

Burr size reduction in drilling by ultrasonic assistance

Accuracy and surface finish play an important role in modern industry. Undesired projections of materials, known as burrs, reduce the part quality and negatively affect the assembly process. A recent and promising method for reducing burr size in metal cutting is the use of ultrasonic assistance, where high-frequency and low-amplitude vibrations are added in the feed direction during cutting. Note that this cutting process is distinct from ultrasonic machining. This paper presents the design of an ultrasonically vibrated workpiece holder, and a two-stage experimental investigation of ultrasonically assisted drilling of A1100-0 aluminum workpieces. The results of 175 drilling experiments with uncoated and TiN-coated drills are reported and analyzed. The effect of ultrasonic assistance on burr size, chip formation, thrust forces and tool wear is studied. The results demonstrate that under suitable ultrasonic vibration conditions, the burr height and width can be reduced in comparison to conventional drilling.     

Comfort evaluation of hearing protection

Hearing protection is very important for workers in noisy work environments, although the willingness of workers to wear hearing protectors depends heavily on their comfort. This paper evaluates and recommends improvements for the comfort of hearing protection. Workers’ experience and comfort needs for hearing protection were investigated through a questionnaire that established the “comfort indices” for hearing protection. An earmuff “comfort tester” was designed to measure the comfort indices, and an experiment was conducted to measure workers’ perceived comfort into quantitative data. From the data, the range of these comfort indices in which workers will feel comfortable was determined. Finally, guidelines to improve the design of current hearing protection based on these “comfort indices” are proposed, which may help increase workers’ willingness to wear hearing protection.

Relevance to industry

Using these proposed guidelines may help improve the comfort of hearing protection and increase workers’ willingness to wear hearing protection in noisy work environments.     

A hybrid method for robust car plate character recognition

Image-based car plate recognition is an indispensable part of an intelligent traffic system. The quality of the images taken for car plates, especially for Chinese car plates, however, may sometimes be very poor, due to the operating conditions and distortion because of poor photographical environments. Furthermore, there exist some “similar” characters, such as “8” and “B”, “7” and “T” and so on. They are less distinguishable because of noises and/or distortions. To achieve robust and high recognition performance, in this paper, a two-stage hybrid recognition system combining statistical and structural recognition methods is proposed. Car plate images are skew corrected and normalized before recognition. In the first stage, four statistical sub-classifiers recognize the input character independently, and the recognition results are combined using the Bayes method. If the output of the first stage contains characters that belong to prescribed sets of similarity characters, structure recognition method is used to further classify these character images: they are preprocessed once more, structure features are obtained from them and these structure features are fed into a decision tree classifier. Finally, genetic algorithm is employed to achieve optimum system parameters. Experiments show that our recognition system is very efficient and robust. As part of an intelligent traffic system, the system has been in successful commercial use.     

“Digi-code” - A micro computer program to digitize two dimensional figures and convert the data to CNC (Computer Numerical Control) Program

Engraving or machining free-form non mathematically defined shapes can be a complex task. “Digi-code” is a program to read and interpret coordinate location data describing parts or shape outlines. The coordinate data is converted directly to NC (Numerical Control) code to control a CNC (Computer Numerical Control) machine tool. This paper describes a technique to digitize contoured shapes and process the data directly to machine control code.     

Applying CIM technology to image factories

Image processing allows the automation of the business transaction, turning “paper factories” into “image factories.” This burgeoning new area of computerization needs new paradigms, theories, and methods to maximize its effectiveness at cutting time and costs. This paper investigates what this new area of computerization can gain from CIM technology. It examines the similarities between the processing of computer images in a paperless business environment and the processing of materials into products in the manufacturing plant. These similarities will allow us to apply mature, well-tested CIM techniques to emerging image factories.     

On the almost-sure sample stability of systems with randomly time-varying delays

In a large class of multi-loop control systems, many feedback loops are “closed” through a time-shared digital computer, by means of algorithms which require information from sources which are sampled at a rate which is not synchronized with the sampling of the individual “plants”. This mis-synchronization, coupled with variations in the computer's task load caused by “interrupts”, results in a randomly time-varying delay in the closing of the various feedback loops. Consequently, the dynamics of each controlled “plant” in such a system may be modeled by means of a stochastic delay-differential equation. This paper presents some new research results concerning the sample stability, as opposed to statistical, or ensemble stability, of linear stochastic delay-differential equations.     

General structure and characteristics of quick response production system

The diversification and shorter production cycles urge manufacturers to shift their production systems from “make to stock” to “make to order” or an intermediate production system between them. The present paper deals with a production system developed by some manufacturers to meet requirements in the last decade. The system, which we call “Quick response to orders production system” or in brief “quick response production system (QRPS)”, is characterized by two key factors, i.e., “acceptable response time” and “semifinished product.” An acceptable response time is decided through the explicit or implicit approval of customers beforehand and manufacturers guarantee to ship products within an acceptable response time in a stochastic sense by processing the semifinished product according to customer order. As a result, manufacturers are able to reduce the inventory of finished products, while customers are able to enjoy the diversification of products and a stable supply from manufacturers. The general structure and characteristics of QRPS are discussed primarily from a quantitative point of view and a numerical example is shown to assist in understanding the design and operation of QRPS.     

Some issues of structure and control for linear time-invariant systems

The objective of this paper is to emphasise the role played by particular structures in the solution of some control problems. The so-called “structural approach” relies on various indicators of dynamical systems such as, for instance, finite and infinite zeros, kernel indices, …. The fundamental invariance properties of these structures under the action of some transformations groups (e.g. feedback) are at the origin of their key role. Structural solutions to “classical” control problems, such as disturbance rejection, model matching and non-interaction are now rather well known: zeros at infinity play a role in the existence of “proper” solutions, while finite (invariant) zeros allow for the characterisation of “fixed poles”, whose location in the complex plane gives answer to pole placement limitations (including stability). Among the recent contributions to this structural approach, a particular attention is here devoted to:

- “Partial” versions of some of these control problems: The control objective only concerns a finite number of (and not necessarily all) the first Markov parameters of the transfer function matrix of the controlled system (e.g. to be zero for disturbance rejection or model matching, to be diagonal for non-interaction). Some interesting new issues in the dual context of failure detection are also sketched.

- Generalised solutions: Based on proportional and derivative feedback laws, with new issues in the context of systems with variable internal structures, and also for systems with delays.

Geometric concepts, such as invariant and almost invariant subspaces, and algebraic counterparts, such as factorisations on some special rings, are intermediary tools which support the characterisations of those particular structures and which allow for a structural treatment of the considered control and/or observation problems.

The results are here presented without proof: references are given to previous published results (in most cases in books and journals which are easily available), and some simple examples are used to illustrate non-standard notions (among which systems with variable internal structure, and time domain left invertibility).

Most of the results here presented rely on long and intensive collaborations between the author and various colleagues.     

Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

In a high speed milling operation the cutting tool acts as a backbone of machining process, which requires timely replacement to avoid loss of costly workpiece or machine downtime. To this aim, prognostics is applied for predicting tool wear and estimating its life span to replace the cutting tool before failure. However, the life span of cutting tools varies between minutes or hours, therefore time is critical for tool condition monitoring. Moreover, complex nature of manufacturing process requires models that can accurately predict tool degradation and provide confidence for decisions. In this context, a data-driven connectionist approach is proposed for tool condition monitoring application. In brief, an ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme. The proposed approach is validated on cutting force measurements data from Computer Numerical Control machine. Results clearly show the significance of our proposition.     

A human-assisted knowledge extraction method for machining operations

This paper deals with a human-assisted knowledge extraction method to extract “if…then…” rules from a small set of machining data. The presented method utilizes both probabilistic reasoning and fuzzy logical reasoning to benefit from the machining data and from the judgment and preference of a machinist. Using the extracted rules, one can determine the values of operational parameters (feed, cutting velocity, etc.) to ensure the desired machining performance (keep surface roughness within the stipulated range (e.g., moderate)). Applying the presented method in a real-life machining knowledge extraction situation and comparing it with the inductive learning based knowledge extraction method (i.e., ID3), the usefulness of the method is demonstrated. As the concept of manufacturing automation is shifting toward “how to support humans by computers”, the presented method provides some valuable hints to the developers of futuristic computer integrated manufacturing systems.