Research Article  Open Access
Implementation of RealTime Machining Process Control Based on Fuzzy Logic in a New STEPNC Compatible System
Abstract
Implementing realtime machining process control at shop floor has great significance on raising the efficiency and quality of product manufacturing. A framework and implementation methods of realtime machining process control based on STEPNC are presented in this paper. Data model compatible with ISO 14649 standard is built to transfer highlevel realtime machining process control information between CAPP systems and CNC systems, in which EXPRESS language is used to define new STEPNC entities. Methods for implementing realtime machining process control at shop floor are studied and realized on an open STEPNC controller, which is developed using objectoriented, multithread, and shared memory technologies conjunctively. Cutting force at specific direction of machining feature in side mill is chosen to be controlled object, and a fuzzy control algorithm with selfadjusting factor is designed and embedded in the software CNC kernel of STEPNC controller. Experiments are carried out to verify the proposed framework, STEPNC data model, and implementation methods for realtime machining process control. The results of experiments prove that realtime machining process control tasks can be interpreted and executed correctly by the STEPNC controller at shop floor, in which actual cutting force is kept around ideal value, whether axial cutting depth changes suddenly or continuously.
1. Introduction
Machining efficiency and quality of finished parts can be improved by monitoring, analyzing, and diagnosing the process of product manufacturing. It is hard to model the machining process accurately due to its complexity and variability. Therefore, artificial intelligent algorithms are usually used to build the relational model of machining parameters, cutting tool wear, and quality of finished part, with the purpose of developing machining process controllers that shorten machining time, prevent damage of tools, and improve quality of finished part. Li et al. used back propagation neural network for multiobjective cutting parameters optimization in sculpture parts machining to increase surface quality [1]. Huang et al. proposed a fuzzy control strategy based on constraint of spindle power in end milling process for reducing machining time of complex shape machining [2]. Zuperl et al. proposed an adaptive control strategy based on neural network to maximize the feed rate subject to allowable cutting force on the milling tool [3]. Offline optimization and online adaptive adjustment based on neural control scheme (NCS) are combined to control the cutting force [4]. Research works have also been carried out to analyze and improve the performance of artificial intelligence algorithms. Osaba et al. presented a Standstill & Parade strategy for subpopulations communication in parallel genetic algorithms [5]. Precup et al. introduced an approach for analyzing the stability of nonlinear processes controlled by TakagiSugeno fuzzy logic controllers [6]. Qian proposed and studied the global attractivity of periodic solutions for nonlinear difference equation [7]. Guerra and Vermeiren analyzed the stabilization of nonlinear systems that can be modeled by Takagi and Sugeno (TS) discrete fuzzy models by using nonquadratic Lyapunov functions in [8]. From above publications, it could be concluded that the result of optimization or adjustment is greatly affected by the rapidity of machining process control, which is expected to be implemented as soon as possible, which means in realtime at shop floor in this case. However, two bottlenecks limit the rapidity of machining process control. One is to transfer highlevel product manufacturing data between CAPP systems and CNC systems. The other is to embed realtime adaptive control algorithms or methods in the kernel of implementation platform.
The standard of STEPNC supports bidirectional data transmission between CAD/CAM systems and CNC systems, which provides a possible way for solving the first bottleneck. Kumar et al. presented a STEPNC compliant process control framework for discrete components and relevant selflearning algorithms in order to compensate errors and improve the surface quality of finished part [9]. Laguionie et al. proposed a STEPNCcompliant manufacturing scenario to optimize the process routes in multiprocess manufacturing environment [10]. Campos and Hardwick proposed a featurebased traceability approach based on STEPNC in order to feed back the manufacturing data associated with the monitored processes [11]. Then the traceability interface for STEPNC is defined and associated with other standards such as ISA95 and MTConnect to support traceability activities in collaborative manufacturing scenario [12]. Wosnik et al. presented a STEPNCcompliant data model and process chain architecture for the optimization of machining process based on feedback process data [13]. Kumar et al. established the information models and implementation mechanism for machine tool process control based on STEPNC [14]. Ridwan et al. proposed the STEPNC data models and relevant optimization algorithm for realtime process control and monitoring, so that the highlevel machine condition monitoring can be used for optimizing machining process [15]. Machine condition monitoring (MCM) based on STEPNC standard is proposed, which enables optimization during machining in order to shorten machining time and increase product quality. The MCM system consists of three modules, namely, optiSTEPNC, AECopt, and knowledgebased evaluation (KBE) [16]. However, most of the machining process control methods based on STEPNC, implemented at machining process stage, are still offline. It is difficult for a few online adaptive control algorithms, which are implemented on external machining process controllers at shop floor, to realize realtime machining process control. The root of this problem is the above publications did not find suitable platform for solving the second bottleneck.
In this paper, a framework and implementation methods of realtime machining process control based on STEPNC are studied. STEPNC data model for realtime machining process control is defined in order to transfer highlevel product information between CAD/CAM systems and CNC systems. A softwarebased STEPNC controller, which interprets and executes highlevel information in STEPNC files, such as manufacturing features, machining operations, and realtime machining process control functions, is designed and developed. An adaptive control algorithm based on fuzzy logic is also embedded within the software kernel of STEPNC controller in order to improve the realtime performance of machining process control. The STEPNC data model, software STEPNC controller, and implementation methods proposed in this paper can be used to realize realtime machining process control at shop floor. The rest of the paper is organized as follows. In Section 2, the architecture of different types of machining process controllers is analyzed and compared. In Section 3, the STEPNC data model and implementation methods for realtime machining process control are proposed, along with the design of STEPNC controller that is able to interpret the newly defined entities. In Section 4, the fuzzy control algorithm for realtime machining process control is introduced and validated. Finally, experiments are carried out to verify the proposed framework, STEPNC data model, and implementation methods for realtime machining process control.
2. Architecture of Machining Process Controller
Machining process control methods are usually designed based on automatic control theories such as classical control theory, modern control theory, information theory, system theory, and artificial intelligence theory. Machining process control systems become more integrated, interoperable, and intelligent with the development of computer and network technologies in recent years. The procedure of machining process control consists of three stages, namely, information collection, information processing, and control output. According to realtime property, machining process control can be classified into three categories.
(1) OffLine Machining Process Control. Machining process information is acquired and saved during or at a certain stage of machining process. Then the acquired data is saved and analyzed by external machining process controllers, which adjust machining parameters of later machining process plans.
(2) OnLine Machining Process Control. Machining process information is acquired in realtime or at a certain stage of machining process. Then the machining process is paused to analyze the data and evaluate the process condition. Cutting parameters of subsequent working steps are adjusted to fix or optimize the machining process.
(3) RealTime Machining Process Control. Machining system executes tasks of motion control, position control, data acquisition, data analyzing, and parameter adjusting in every interpolation period in order to keep the machining condition at desired and ideal state, which means all tasks of part machining, machining condition monitoring, and adaptive control are executed in realtime.
Machining process controllers (MPC), which analyze the process condition and adjust machining parameters, can be implemented at process planning stage or shop floor stage as shown in Figure 1. For MPC at process planning stage, CNC systems execute motion control command without adjustment even if the machining process is unstable. Optimization can only be done when the next part is being machined. For external MPC at shop floor, machining process data is firstly used for adjusting machining parameters online and then transferred to CAPP systems for further analysis in order to optimize process plan. MPC at shop floor is usually implemented on an external computer, which sends adjusted machining parameters to CNC systems. For integrated MPC at shop floor, CNC systems execute machining tasks by interpreting highlevel integrated machining process data directly and include dimension and position of manufacturing features, machining methods, and process parameters. Machining process data is acquired and analyzed by realtime machining process control algorithms simultaneously with interpolation computing. The data acquired during machining process is integrated with input data and sent to CAPP system in order to preserve manufacturing knowledge for later machining process planning. Separation of MPC and CNC systems will lead to the delay of machining process control, which means integrated MPC at shop floor is a possible solution for implementing realtime machining process control.
3. RealTime Machining Process Control Based on STEPNC
3.1. Problem Definition and Analysis
Fluctuation of cutting force has great influence on machining system stability, cutting tool life, dimensional accuracy, and surface quality of finished part. It is necessary to select proper machining parameters with constraint of maximum or optimal cutting force. In this paper, the control of cutting force at specific direction is chosen as controlled object to implement realtime machining process control. As shown in Figure 2, side milling of planar face with end milling tool is taken as an example. The cutting force normal to the machined surface, which will cause the deformation of cutting tool, workpiece, and fixture, is selected as the controlled object. Optimal cutting force is calculated by CAD/CAM system and sent to CNC system at shop floor along with other process planning data. The prediction algorithm of differential tangential (), radial (), and axial () cutting forces is given by Engin and Altintas in [17] as
Edge cutting coefficients , , and and shear force coefficients , , and are various in different cutting conditions, which can only be identified from plenty of cutting tests. It is hard to build an accurate model for cutting process as the interrelation of machining parameters and cutting force is too complicated. As a result, the control algorithms based on empirical equation and offline optimization usually become inappropriate at real cutting process, because of tool wear, varying cutting conditions, and complex shape of part. In this paper, realtime optimization algorithm is used to calculate the ideal machining parameters for current cutting condition by analyzing actual cutting force signal, which is acquired by a dynamometer. There are four machining parameters that are mainly related to cutting force, namely, axial cutting depth (), radial cutting depth (), feed rate (), and spindle speed (). Axial cutting depth and radial cutting depth are determined by machining allowance and shape of part, which are usually hard to be adjusted in realtime during the cutting process. Feed rate , which has more influence on cutting force than spindle speed , is adjusted in realtime in order to keep actual cutting force equal to optimal value. Acquired cutting force data should also be preserved and sent to machining knowledge management system for further analysis. Therefore, a data model for describing and transferring information of realtime machining process control tasks and machining process condition parameters is needed.
3.2. STEPNC Data Model
STEPNC data transfer standard describes the machining process plan with entities such as project, workplan, workpiece, machining_workingstep, machining_feature, machining_operation, machining_function, and technology, which are defined by using EXPRESS language, a basic descriptive method of STEPNC standards [18]. Machining system gathers information of machining condition and associates it with entities of machining process plan. However, there is no data model for realtime machining process control and machining process condition information in STEPNC data transfer standard at present. To pursue the aim of this research, which controls the cutting force of side mill perpendicular to machined surface by adjusting feed rate in realtime, new STEPNC entities for describing constant force milling function and cutting force data are defined by using EXPRESS method in this paper. Figure 3 is the EXPRESSG diagram of the developed data model for realtime machining process control. The upper half gives a brief description of existing STEPNC data model, which is referenced from relevant ISO 14649 Part 10, Part 11, and Part 111 [19–21]. The lower half expresses the newly defined STEPNC data model, which is compatible with existing STEPNC standards of ISO 14649.
Entity const_force_milling is a subtype of adaptive_control, which is defined in ISO 14649 Part 11. Under this entity there are six attributes, _axis_const, _axis_const, _axis_const, _force, _force, and _force. The first three attributes represent the direction of milling force that is supposed to be constant. Then the last three attributes represent the ideal milling force in Newtons. The direction and magnitude of milling force, which is applied to machined surface by milling cutting tool, are defined as three components at , , or direction in feature coordinate system. The attributes of milling force are optional if the value of corresponding Boolean attribute is False. When the adaptive_control attribute of entity machining_technology in STEPNC file points to an instance of const_force_milling, the STEPNC controller will execute the corresponding working step with variable machining parameters in order to keep the milling force at optimal value. Meanwhile, realtime intelligent control algorithm is used to realize machining process control.
Entity milling_force_save is defined to preserve the cutting force data. There are six attributes, its_workingstep, start_point, end_point, sample_rate, local_save, and online_save. The cutting force data acquired during cutting process is used for optimizing machining parameters in realtime. Then the acquired data should be associated with corresponding working step and sent to other subsystems for further analysis. Attribute its_workingstep represents the corresponding machining_workingstep that is being executed while the cutting force data is being acquired. Attributes start_point and end_point represent the positions of milling cutting tool in feature coordinate system at the beginning and the end of data acquisition. Attribute sample_rate represents the sampling period of cutting force acquisition in microseconds. Total integration of product manufacturing information can be realized if the realtime machining process condition data is saved in STEPNC file. However, the quantity of cutting force data is usually too huge for STEPNC file, especially when the sampling frequency is very high. In this case, the cutting force data is saved in an individual file that is stored in local storage of web server, while the file path or URL is represented as character strings by attribute local_save or online_save. At least one out of the last two optional attributes must exist.
STEPNC file contains highlevel information of product manufacturing without lowlevel tool path, in which entity project is the root node of tree structure. CNC systems should interpret all instances in an input file in order to get necessary information for online tool path generation and execution. The first step of interpreting a STEPNC file is to map the information of input file to internal data model. C++ classes are defined according to the EXPRESS definition of newly developed STEPNC entities and added to the ISO 14649 class library, which is originally designed by NIST for offline STEPNC interpreting [22]. It is modified and embedded to the software CNC kernel of STEPNC controller developed in this paper to interpret STEPNC files online. Figure 4 presents the C++ classes for STEPNC realtime machining process control information. Class iso14649CppBase is the base class for all other classes in the class library. Class instance is the base class for C++ classes that maps STEPNC instances in STEPNC files. Member variable iId of instance represents the number of instances. Member variables of C++ classes are pointers to instances of other classes, which represent STEPNC types or entities. By this way, nodes in tree structure of STEPNC file are linked to each other.
3.3. Open STEPNC Controller
CNC systems should be able to interpret the STEPNC files that contain machining process control operations directly, acquire machining process condition data in realtime, and compute interpolation points and optimized machining parameters simultaneously. Most commercial CNC systems have limited interfaces for STEPNC interpretation and realtime adaptive control. In this paper, A STEPNC controller based on open architecture software CNC kernel is proposed and developed in order to implement realtime machining process control at shop floor. An integrated data model based on STEPNC is used to describe the information of geometry, technology, process planning, and machining process condition, which makes all stages of product manufacturing process traceable. The STEPNC controller interprets STEPNC files directly while communicating with sensors without external data acquisition and analysis system. Adaptive control algorithms can be embedded into interpolation calculation procedure in order to optimize machining parameters. The architecture of machining process control system that consists of three subsystems is shown in Figure 5, in which ISO 14649 is used as data transfer standard for realtime machining process control. The first subsystem is CAD/CAM system, which is responsible for making the machining process plan that contains realtime machining process control functions. Then the machining process plan is sent to other subsystems in form of STEPNC files. Machining knowledge got from former product manufacturing process, machine tool capability, and cutting tool condition are considered by CAD/CAM system to optimize the process routes and parameters offline in order to get better quality of finished part or higher production efficiency. The second subsystem is machining knowledge management system, which is responsible for gathering, fusing, and managing product manufacturing data. The third subsystem is intelligent machining system, which is responsible for interpreting and executing STEPNC file and optimizing machining process in realtime. At the core of this subsystem is an open STEPNC controller developed by Research Division of Numeric Control Technology in Harbin Institute of Technology based on Open Modular Architecture Controller (OMAC). The function of STEPNC controller is realized by a software CNC kernel that consists of four software modules, namely, STEPNC interpretation module, task generation module, system coordination module, and interpolation calculation module. This paper focuses on the implementation of realtime machining process control at shop floor, and the realization of CAD/CAM system and machining knowledge management system goes beyond the scope of this study.
STEPNC interpretation module is responsible for interpreting and executing STEPNC files that contains realtime machining process control functions directly. Most of the manufacturing features derived from entity machining_feature in ISO 14649 Part 10, process data for milling in ISO 14649 Part 11, and milling cutting tool data in ISO 14649 Part 111 are supported by this module [19–21]. Tool paths are generated online by STEPNC interpretation module and compiled by task generation module to generate task segments such as line feed, arc feed, and rapid move, which are transmitted to system coordination module via temporary storage. System coordination module creates several realtime threads for automatic operation, jog operation, and machining process condition monitoring, which are managed and synchronized by the host process according to scheduled clock period. System coordination module monitors the task segment queue in temporary storage in realtime, extracts one task segment each time when the task segment queue is not empty, and executes it by calling the functions of interpolation calculation module. Interpolation calculation module is responsible for optimizing machining process parameters and calculating the position of interpolation points, which consists of algorithms for speed control, position calculation, and MPC control. Under the interoperation of the four modules in software CNC kernel, the STEPNC controller can parse and map a STEPNC project into tree structure of C++ class instances, execute the main work plan, generate and execute tool paths for every working step, and control the machining process in realtime. Besides the software CNC kernel, a software Human Machine Interface (HMI) is developed for receiving instructions from user or communicating with other subsystems, and the hardware interface is used to communicate with servo system and sensors.
3.4. Procedure of RealTime Machining Process Control Based on STEPNC
Realization methods of realtime machining process condition monitor along with algorithms for realtime machining process control are studied and developed based on the open STEPNC controller proposed and built in Section 3.3. A realtime thread is created by system coordination module to acquire machining process data from sensors via hardware interface, and the realtime adaptive control algorithm is embedded into interpolation calculation module to synchronize the procedure of machining parameters optimization and interpolation calculation. Procedure of realtime machining process control based on STEPNC is shown in Figure 6. There are two host processes in the software CNC kernel of STEPNC controller. The first process is responsible for interpreting STEPNC file and generating tool paths. Tool paths are encapsulated as task segments, which contain information of realtime machining process control such as start point, end point, feed rate, spindle speed, and ideal cutting force. Task segments are pushed into a first in, first out (FIFO) task segment queue in shared memory. The second process is responsible for executing the task segment in realtime. When the task segment queue is not empty, which means the process of STEPNC interpreting has started, the realtime process will get the first task segment from the queue and execute it until the queue is empty. Realtime machining process control algorithm is called to analyze cutting force signal, which is acquired from dynamometer, and to adjust the feed rate. Interpolation algorithm is called to calculate the position of interpolation point according to the new feed rate in realtime. Servo system, which drives the feed shafts and spindle, is controlled via hardware interface to implement the adaptive control algorithm. The acquired cutting force data is saved in a data file and associated with corresponding working step by modifying the entities of input STEPNC file and saved as output STEPNC file.
4. Design and Validation of Fuzzy Control Algorithm
4.1. Fuzzy Control Algorithm with SelfAdjusting Factor for Constant Force Milling
The complexity of cutting process makes it hard to be modeled accurately with statespace equations. Artificial intelligence algorithms, which are able to handle unpredictable, nonlinear, multivariable, and incertitude controlled objects, can be a feasible solution. However, most artificial intelligence algorithms are used for offline machining process optimization due to the limitation of computational complexity. Fuzzy logical algorithm, which has high computational efficiency, is suitable for realtime machining process control. In this paper, a fuzzy control algorithm with selfadjusting factor is proposed to adjust the feed rate in realtime in order to get constant cutting force. The principle of proposed control algorithm, which is designed by modifying the conventional fuzzy control algorithm, is illustrated in Figure 7. The input of control system is ideal cutting force while the controlled object is actual cutting force. The input lingual variables of fuzzy controller are the error between ideal cutting force and actual cutting force and the variety rate of error. The output lingual variable of fuzzy controller is the variety rate of feed rate. CNC system adjusts the feed rate according to the output quantity of fuzzy controller.
The error and error variety rate of ideal cutting force and actual cutting force can be calculated aswhere is ideal cutting force (N), is actual cutting force (N), is error (N), and is error variety rate (N/s).
Then the error and error variety rate are quantified according to the domain of fuzzy rule input aswhere is fuzzy variable for error, is fuzzy variable for error variety rate, is quantitative factor of error (N^{−1}), is quantitative factor of error variety rate (s/N), is adjusting factor of , and is adjusting factor of (s^{−1}).
The quantitative factors and are associated with ideal cutting force in order to improve the adaptability of fuzzy controller. As shown in Figure 8, the domain of fuzzy rule input is and triangle function, which has been widely used and has low computational complexity, is adopted as membership function of fuzzy rule. The fuzzy language value and membership value of input are expressed as follows: and are fuzzy language values of error. and are fuzzy language values of error variety rate. and are membership value of error. and are membership value of error variety rate.
The output fuzzy language values and are calculated by using , , , and aswhere is selfadjusting factor, is upper limit of selfadjusting factor, and is lower limit of selfadjusting factor.
The selfadjusting factor is used to adjust the weight of error and error variety rate adaptively when calculating the output fuzzy language value. The upper and lower limits of selfadjusting factor are set to 0.85 and 0.45. The fuzzy control output and variety rate of feed rate are calculated aswhere is fuzzy control output, is variety rate of feed rate (mm/s^{2}), is scaling factor of fuzzy control output (mm/s^{2}), and is adjusting factor of (mm/s^{2}).
4.2. Test of Fuzzy Control Algorithm
Factors , , and can be used to adjust the rapidity, accuracy, and stability of fuzzy control system. Lower and can improve the rapidity but reduce the accuracy and stability. Lower can improve the stability but the rapidity is reduced. The proposed fuzzy control algorithm is validated and adjusted by actual cutting tests as shown in Figure 9. The test part is made of aluminum alloy and the cutting tool used here is a high speed steel end mill tool that is 10 mm in diameter and has 3 teeth. Considering the inertance of feed shaft, the feed rate is adjusted every 10 interpolation periods. The cutting force data is firstly processed by using mean filter algorithm aswhere is average cutting force at the th acquisition cycle (N), is actual cutting force at the th acquisition cycle (N), is spindle speed (r/min), and is sampling time of cutting force (s).
The test results are shown in Figure 10, in which the subtitle is in format of “.” The feed rate fluctuates obviously when = 8.0 mm/s^{2} and more steadily when = 4.0 mm/s^{2}. The change of cutting force signal is more smooth when = 7.2 s^{−1}. So in this paper, adjusting factors , , and are set to 0.9, 7.2 s^{−1}, and 4.0 mm/s^{2}. The results indicate the fuzzy control algorithms proposed in this paper can work properly without building the model of metal cutting process with statespace equations.
(a) 0.910.88
(b) 0.97.28
(c) 0.910.84
(d) 0.97.24
5. Experimentation
Experiments are designed and carried out to verify the function of proposed STEPNC controller for realtime machining process control. The experiment platform consists of Qier XKV715 vertical milling machine, Kistler 9257B dynamometer, and industrial computer is shown in Figure 11. The proposed software CNC kernel of STEPNC controller along with realtime machining process control algorithm runs on an industrial computer, in which a Rexroth PCMS11.2 SERCOS master card is used for communicating with servo drivers and IO ports, and an Advantech PCI1741U data acquisition card is used for collecting cutting force data from dynamometer. Both expansion cards are plugged in the PCI slots of industrial computer.
Two test parts made of aluminum alloy are machined on the experiment platform as shown in Figure 12. Planer face is chosen as manufacturing feature and side milling is chosen as machining operation. High speed steel end mill tool that is 10 mm in diameter is used to machine the parts with side milling method. The planar face of test part 1 has six sections that are different in width, which will cause irregularly variation of axial cutting depth in machining process. The shape of planar face in part 2 is a rightangled trapezoid, which will cause linear variation of axial cutting depth in machining process. Taking test part 1 as an example, instances of key entities related to realtime machining process control are extracted from Part 21 STEPNC file, which is interpreted, modified, and outputted by STEPNC controller. As shown in Box 1, instance in line #23 connects with planar face in line #29 and side finish milling operation in line #45 is the machining working step to be executed in adaptive mode. Milling technology in line #50 represents the cutting parameters, and in its attribute list, a constant force milling adaptive control strategy is assigned. As represented in line #53, the ideal cutting force is 50 N in the minus direction of feature coordinate system. The STEPNC controller interprets the STEPNC file, generates tool path for each working step, and executes it in realtime. The acquired cutting force signal is saved in a data file that is stored at local hard disk after a working step is finished. Lines #54 to #56 are added in the input file by STEPNC controller to preserve the information of realtime machining process condition. Line #56 is the newly added instance of entity milling_force_save, and in its attribute list, there are the path of cutting force data file and corresponding working step represented in line #23, which can be linked with each other. Lines #54 and #55 are instances of cartesian_point, which represent the position of milling cutter in feature coordinate system when the data acquisition is at start and end.

(a)
(b)
(a) Conventional machining
(b) Constant force milling
(c) Conventional machining
(d) Constant force milling
6. Results and Discussion
Results of realtime machining process control are presented in Figure 13. The average cutting force per revolution and feed rate are represented as absolute value in the data graph, because all values of milling force and feed rate are negative according to the definition of feature coordination system. Approach feed rate is set to 40 mm/min for both conventional and constant force milling, and the adjusting range of feed rate in constant force milling algorithm is from 20 mm/min to 180 mm/min. For test part 1, cutting force signal is steplike while the feed rate is constant in conventional machining process as presented in Figure 13(a). In constant force milling process, cutting force signal changes suddenly at the start of every section while the proposed constant force milling algorithm decelerating or accelerating until the actual cutting force is equal to ideal cutting force or the feed rate reaches upper or lower limit as presented in Figure 13(b). For test part 2, cutting force signal changes gradually while the feed rate is constant in conventional machining process as presented in Figure 13(c). In constant force milling process, the feed rate is adjusted by the proposed fuzzy control algorithm and actual cutting force is kept close to ideal value as presented in Figure 13(d). Experiment results show that the STEPNC interpreter can identify the ideal cutting force from STEPNC file and keeps the actual cutting force around it by adjusting feed rate in realtime. The cutting force signals are recorded and linked with corresponding machining working step correctly. As a result, highlevel information of realtime machining process control, instead of lowlevel information in most other published methods, can be interpreted at shop floor directly by using the proposed STEPNC data model. Realtime machining process control algorithm is integrated with the software CNC kernel rather than external MPC in other research works.
7. Conclusion
This study has focused on the information exchanging mechanism, implementation method, and system scenario of realtime machining process control. An advanced solution method for optimization problems in manufacturing is proposed for implementing realtime machining process control at shop floor. Two key issues for implementing realtime machining process control are studied and solved. The issue of exchanging highlevel product manufacturing data between CAPP systems and CNC systems is solved by extending STEPNC standard and building an open STEPNC controller that interpret STEPNC data directly at shop floor. The issue of implementing realtime machining process control at shop floor is solved by integrating adaptive control algorithm with interpolation algorithm in software CNC kernel. Cutting force at specific direction is chosen to be the controlled object of realtime machining process control to demonstrate the newly proposed STEPNC data model and implementation methods. A fuzzy control algorithm with selfadjusting factor is proposed to keep the cutting force constant by adjusting feed rate in realtime. The verification of proposed STEPNC data model and implementation method is carried out on an open CNC platform. The experiment results indicate that the STEPNC controller is able to interpret STEPNC file with realtime machining process control functions correctly and keep the cutting force at specific direction close to ideal value. More research works on performance of realtime machining process control algorithms will be carried out based on the proposed implementation method in the future.
Competing Interests
The authors declare that they have no competing interests.
Acknowledgments
This study is financially supported by National Science and Technology Major Projects of China (Grant no. 2013ZX04013011).
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Copyright © 2016 Po Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.