Hierarchical optimal force-position control of complex manufacturing processes

A hierarchical optimal controller is developed in this paper to regulate the machining force and axis positions, simultaneously, in a micro end milling process. The process is divided into two levels of decision making. The bottom level includes the measurable states, which in this work comprises the axis positions. The top level includes the higher order objectives, which can be derived from the bottom level objectives by an aggregation relationship. In this work, the top level's objective is to regulate the machining force. A series of simulations were conducted in which the weighting between the top and the bottom level objectives is adjusted within the feasible range. The results demonstrated that excellent tracking of both axis positions and machining force are achieved during the steady state regardless of the weighting. However, the transient performance of the system could be systematically shaped to achieve better performance of either objective. For the purpose of comparison a decentralized optimal controller was constructed and simulated for the feasible range of controller weights. When the axis position errors were weighted heavily, both controllers were able to regulate the axis errors well, while the hierarchical controller had smaller machining force errors. When the machining force errors were weighted heavily, although the machining force error decreased for the decentralized controller the axis position errors increased significantly. However, with heavy machining force weighting, the hierarchical controller was able to manipulate the axial errors in a way that while the machining force error was reduced, the contour error (i.e., smallest deviation from the tool tip to the desired contour) remained small.     

Suppression of nonlinear regenerative chatter in milling process via robust optimal control

During the milling process, self-excited vibration or chatter adversely affects tool life, surface quality and productivity rate. In this paper, nonlinear cutting forces of milling process are considered as a function of chip thickness with a complete third order polynomial (instead of the common linear dependency). An optimal control strategy is developed for chatter suppression of the system described through nonlinear delay differential equations. Counterbalance forces exerted by actuators in x and y directions are the control inputs. For optimal control problem, an appropriate performance index is defined such that the regenerative chatter is suppressed while control efforts are minimized. Optimal control law is determined based on variation of extremals algorithm. Results show that under unstable machining conditions, regenerative chatter is suppressed effectively after applying the optimal control strategy. In addition, optimal controller guarantees robust performance of the process in the presence of model parametric uncertainties.     

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.     

Integrated approach to robotic machining with macro/micro-actuation

A novel integrated approach to high-accuracy machining with industrial robots is presented in this paper. By combining a conventional industrial robot with an external compensation mechanism, a significantly higher bandwidth of the control of the relative position between the tool and the workpiece can be achieved. A model-based feedback controller for the compensation mechanism, as well as a mid-ranging control architecture for the combined system with the robot and the compensation mechanism are developed. The system performance is evaluated in extensive machining experiments, and the workpiece accuracies achieved are quantified and compared to the corresponding results obtained with state-of-the-art approaches to robotic machining. It is shown that the proposed approach to machining offers significantly higher accuracy, up to eight times improvement for milling in steel, where the required process forces, and thus the exhibited position deviations of the robot, are significant.     

Algorithms for selecting cutters in multi-part milling problems

This paper describes geometric algorithms for automatically selecting an optimal sequence of cutters for machining a set of 2.5-D parts. In milling operations, cutter size affects the machining time significantly. Meanwhile, if the batch size is small, it is also important to shorten the time spent on loading tools into the tool magazine and establishing z-length compensation values. Therefore, in small-batch manufacturing, if we can select a set of milling tools that will produce good machining time on more than one type of parts, then several unnecessary machine-tool reconfiguration operations can be eliminated. In selecting milling cutters we consider both the tool loading time and the machining time and generate solutions that allow us to minimize the total machining time. In this paper we first present algorithms for finding the area that can be cut by a given cutter. Then we describe a graph search formulation for the tool selection problem. Finally, the optimal sequence of cutters is selected by using Dijkstra's shortest path planning algorithm.     

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.     

Digital copy milling—autonomous milling process control without an NC program

Conventional NC machine tools do not generally allow the change of cutting conditions such as depth of cut and stepover during machining operations, once they are given machining commands as NC programs. For that reason, the NC programs must be prepared adequately and verified in advance, which requires extensive time and effort. It is therefore necessary to develop functions to generate the cutter path autonomously and control the cutting conditions adaptively during machining to optimize the cutting process, maintain stable cutting, and avoid cutting trouble. This paper proposes a new architecture to realize autonomous control of the cutting process without using NC programs. A technique called digital copy milling is developed to control the NC machine tool in real time. The digital copy milling system can generate tool paths in real time, based on the principle of copy milling. In addition, a new control strategy is developed to control the cutting conditions adaptively. A prototype of an autonomous controller was implemented in a three-axis control machining center. Thereafter, experimental milling tests were carried out to verify the effectiveness of the proposed system. The cutter paths were generated autonomously by the digital copy milling system. Results show that the cutting depth and stepover can be changed during milling tests. Cutting conditions were controlled adaptively.     

Identification of milling inserts in situ based on a versatile machine vision system

This paper proposes a novel method for in situ localization of multiple inserts by means of machine vision techniques, a challenging issue in the field of tool wear monitoring. Most existing research works focus on evaluating the wear of isolated inserts after been manually extracted from the head tool. The method proposed solves this issue of paramount importance, as it frees the operator from continuously monitoring the machining process and allows the machine to continue operating without extracting the milling head for wear evaluation. We use trainable COSFIRE filters without requiring any manual intervention. This trainable approach is more versatile and generic than previous works on the topic, as it is not based on, and does not require, any domain knowledge. This allows an automatic application of the method to new machines without the need of specific knowledge on machine vision. We use an experimental dataset that we published to test the effectiveness of the method. We achieved very good performance with an F₁ score of 0.9674, in the identification of multiple milling head inserts. The proposed approach can be considered as a general framework for the localization and identification of machining pieces from images taken from mechanical monitoring systems.     

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.     

Optimal control for chatter mitigation in milling—Part 1: Modeling and control design

Active structural methods constitute a promising way to mitigate chatter vibrations in milling. This paper presents an active system integrated into a spindle unit. Two different optimal control strategies are investigated. The first one only considers the dynamics of the machine structure in the controller design and minimizes the influence of cutting forces on tool tip deviations. The second one takes explicitly the process interaction into account and attempts to guarantee the stability of the overall closed-loop system for specific machining conditions. The modeling and formulation used for both strategies are presented in this first part. A simulation allows the comparison of their respective working principle. The validation of the proposed concept in experimental conditions is described in the second part.     

Robotic path compensation training method for optimizing face milling operations based on non-contact CMM techniques

Currently, the use of industrial robots in the machining of large components in metallic materials of significant hardness is proliferating. The low rigidity of industrial robots is still the main conditioning for their use in machining applications, where the forces developed in the process cause significant deviations on the cutting tool path. Although there are already methodologies that facilitate the pose study of the robot mechanical behaviour, predicting deviation values of the cutting tool path and facilitating the selection of process variables, robotic cell users still request new methods able to allow them to optimize the use of these production systems. On the other hand, non-contact measurement technologies have burst into many fields of knowledge, their use is becoming consolidated, and they allow the digitization of complex surfaces. This research presents the development of a new method of robotic machining trajectory compensation that allows optimizing the manufacture of flat surfaces using an industrial anthropomorphic robot. The new training method determines the actual deviations of the cutting tool after the machining process, and checks if these are within the admissible range of flatness error. This method is a novel iterative technique that incorporates the algorithm that uses the measured deviations and a reduction factor fr to calculate the offset that modifies the coordinate value of the programmed path points outside the admissible range and generates a new machining path to be tested. The method has been tested on a pre-industrial scale for aluminium machining, and the algorithm has carried out two iterations to generate a compensated robotic milling path within a flatness tolerance range of 300 µm, improving the error deviation by 37% comparing to the initial path.     

Process planning for Floor machining of 2½D pockets based on a morphed spiral tool path pattern

This work proposes a process planning for machining of a Floor which is the most prominent elemental machining feature in a 2½D pocket. Traditionally, the process planning of 2½D pocket machining is posed as stand-alone problem involving either tool selection, tool path generation or machining parameter selection, resulting in sub-optimal plans. For this reason, the tool path generation and feed selection is proposed to be integrated with an objective of minimizing machining time under realistic cutting force constraints for given pocket geometry and cutting tool. A morphed spiral tool path consisting of G¹ continuous biarc and arc spline is proposed as a possible tool path generation strategy with the capability of handling islands in pocket geometry. Proposed tool path enables a constant feed rate and consistent cutting force during machining in typical commercial CNC machine tool. The constant feed selection is based on the tool path and cutting tool geometries as well as dynamic characteristics of mechanical structure of the machine tool to ensure optimal machining performance. The proposed tool path strategy is compared with those generated by commercial CAM software. The calculated tool path length and measured dry machining time show considerable advantage of the proposed tool path. For optimal machining parameter selection, the feed per tooth is iteratively optimized with a pre-calibrated cutting force model, under a cutting force constraint to avoid tool rupture. The optimization result shows around 32% and 40% potential improvement in productivity with one and two feed rate strategies respectively.     

Fixed‐structure robust controller design for chatter mitigation in high‐speed milling

Chatter is an instability phenomenon in high‐speed milling that limits machining productivity by the induction of tool vibrations, inferior machining accuracy, noise, and wear of machine components. In this paper, a fixed‐structure active chatter control design methodology is proposed, which enables dedicated shaping of the chatter stability boundary such that working points of higher machining productivity become feasible while avoiding chatter. The control design problem is cast into a nonsmooth optimization problem, which is solved using bundle methods. Using this approach, fixed‐structure dynamic (delayed) output feedback controllers can be synthesized. Distinct benefits of this approach are the a priori fixing of the controller order, the limitation of the control action, and the fact that no finite‐dimensional model approximations and online chatter estimation techniques are required. All these benefits are important in milling practice. Representative examples illustrate the power of the proposed methodology in terms of increasing the chatter‐free depth of cut, thereby enabling significant increases in machining productivity. Copyright © 2014 John Wiley & Sons, Ltd.     

Process planning for Floor machining of 2½D pockets based on a morphed spiral tool path pattern

This work proposes a process planning for machining of a Floor which is the most prominent elemental machining feature in a 2½D pocket. Traditionally, the process planning of 2½D pocket machining is posed as stand-alone problem involving either tool selection, tool path generation or machining parameter selection, resulting in sub-optimal plans. For this reason, the tool path generation and feed selection is proposed to be integrated with an objective of minimizing machining time under realistic cutting force constraints for given pocket geometry and cutting tool. A morphed spiral tool path consisting of G¹ continuous biarc and arc spline is proposed as a possible tool path generation strategy with the capability of handling islands in pocket geometry. Proposed tool path enables a constant feed rate and consistent cutting force during machining in typical commercial CNC machine tool. The constant feed selection is based on the tool path and cutting tool geometries as well as dynamic characteristics of mechanical structure of the machine tool to ensure optimal machining performance. The proposed tool path strategy is compared with those generated by commercial CAM software. The calculated tool path length and measured dry machining time show considerable advantage of the proposed tool path. For optimal machining parameter selection, the feed per tooth is iteratively optimized with a pre-calibrated cutting force model, under a cutting force constraint to avoid tool rupture. The optimization result shows around 32% and 40% potential improvement in productivity with one and two feed rate strategies respectively.     

Modeling and adaptive force control of milling by using artificial techniques

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

Optimizing tool orientations for 5-axis machining by configuration-space search method

This paper presents a methodology and algorithms of optimizing and smoothing the tool orientation control for 5-axis sculptured surface machining. A searching method in the machining configuration space (C-space) is proposed to find the optimal tool orientation by considering the local gouging, rear gouging and global tool collision in machining. Based on the machined surface error analysis, a boundary search method is developed first to find a set of feasible tool orientations in the C-space to eliminate gouging and collision. By using the minimum cusp height as the objective function, we first determine the locally optimal tool orientation in the C-space to minimize the machined surface error. Considering the adjacent part geometry and the alternative feasible tool orientations in the C-space, tool orientations are then globally optimized and smoothed to minimize the dramatic change of tool orientation during machining. The developed method can be used to automate the planning and programming of tool path generation for high performance 5-axis sculptured surface machining. Computer implementation and examples are also provided in the paper.     

Advancements of the KUCIM automated flexible manufacturing workstation

Incremental automation, i.e. adding capabilities over time, is an alternative for small mannfacturers to large scale automation projects as a method of achieving automated manufacturing. As an example of incremental automation, the University of Kansas Computer Integrated Manufacturing (KUCIM) workstation has developed over time into an unmanned flexible machining workstation. Presently, the workstation contains a Hurco three-axis milling machine, an American Robot six-axis robot, a Sun 4/260, a versatile fixturing system, an IBM PC-AT compatible computer, and an interface between the milling machine and PC. The control architecture is based on the National Institute for Standards and Technology (NIST) Automated Manufacturing Research Facility (AMRF) manufacturing control hierarchy. The workstation controller employs three software modules: the Supervisor, the Robot Foreman, and the Fixture/Machine Tool Foreman. At the equipment level, two modules are used: the Robot Command Interpreter and the Fixture/Machine Tool Command Interpreter. The three functions that the KUCIM workstation performs to produce parts are: processing an input file, performing tool zeroing and performing machining cycles.     

ISO 14649-based nonlinear process planning implementation for complex machining

Increasing attention is being paid to complete machining, i.e., machining of the whole part in a single machine tool, in the metal working industry. For this purpose, complex machine tools equipped with machining components, such as multiple spindles and turrets have been developed by leading machine tool builders. The efficiency of complex machine tools is largely dependent on how the machining components are utilized. The main thrust of this paper is twofold: (1) Proposition of a nonlinear process planning based on the STEP-NC (STEP-compliant data interface for numerical controls) paradigm whose data model is formalized as ISO 14649, and (2) Development of an optimal solution algorithm for process planning for complex machining. The developed algorithm is based on the branch-and-bound approach and heuristics derived from engineering insights. The developed process planning method and optimization algorithm were implemented and tested via the TurnSTEP system developed by our research team. Through the experiments, we are convinced that the new process planning and algorithm can be used as a fundamental means for implementing the third type of STEP-NC [Suh S. TurnSTEP: Tools to create CNC turning programs. In: White paper presented on STEP Implementers’ Forum ISO TC184/SC4 Meeting. 2004], i.e., an Intelligent and Autonomous STEP-NC system for the CAD-CAM-CNC chain supporting e-Manufacturing.     

Design of hybrid differential evolution and group method of data handling networks for modeling and prediction

This paper proposes a hybrid modeling approach based on two familiar non-linear methods of mathematical modeling; the group method of data handling (GMDH) and differential evolution (DE) population-based algorithm. The proposed method constructs a GMDH self-organizing network model of a population of promising DE solutions. The new hybrid implementation is then applied to modeling tool wear in milling operations and also applied to two representative time series prediction problems of exchange rates of three international currencies and the well-studied Box-Jenkins gas furnace process data. The results of the proposed DE–GMDH approach are compared with the results obtained by the standard GMDH algorithm and its variants. Results presented show that the proposed DE–GMDH algorithm appears to perform better than the standard GMDH algorithm and the polynomial neural network (PNN) model for the tool wear problem. For the exchange rate problem, the results of the proposed DE–GMDH algorithm are competitive with all other approaches except in one case. For the Box-Jenkins gas furnace data, the experimental results clearly demonstrates that the proposed DE–GMDH-type network outperforms the existing models both in terms of better approximation capabilities as well as generalization abilities. Consequently, this self-organizing modeling approach may be useful in modeling advanced manufacturing systems where it is necessary to model tool wear during machining operations, and in time series applications such as in prediction of time series exchange rate and industrial gas furnace problems.     

A novel open CNC architecture based on STEP-NC data model and IEC 61499 function blocks

Modern manufacturing industries demand computer numeric controllers, having higher level input languages than outdated G-code, and less proprietary vendor dependencies. IEC 61499 is a new standard for distributed measurement and control systems, that enables portability and interoperability of embedded controllers, along with the ease of their mapping to arbitrary distributed networking hardware configurations. This paper demonstrates that the IEC 61499 reference architecture can be successfully used to create a computer numeric controller, offering interoperability, portability, configurability, and distribution characteristics. The layered CNC-FB architecture is proposed, which simplifies the design of a CNC machine controller with the architecture layers responsible for data processing, data storage and execution. In combination with the object-oriented Model-View-Control design pattern, the CNC-FB architecture supports the design framework, in which simulation of the machining becomes natural and inherent part of the design process, with seamless transition from simulation to actual machining. The implemented controller was tested in both the model and on an actual milling machine.