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Publication# Part Programming to Realize Chatter Free and Efficient High Speed Milling

Résumé

High speed milling (HSM) is the most known machining process due to its application in various industries. In milling, a rotating cutting tool removes a large amount of material along a predefined toolpath to manufacture the final part with a desired shape. Milling of prismatic parts1 is very important in automotive, aerospace, mold and die industries. Even complicated parts are machined from a blank first by 2.5D roughing followed by 3D-5D finishing. Modern production floors have adopted high speed CNC2 machine tools to execute part programs, developed by CAD/CAM3 systems, to manufacture the final workpiece. The overall productivity of the milling process depends on the choice of cutting conditions and the toolpath. Current CAD/CAM systems do not provide any guidance to select cutting conditions due to the unavailability of models of the complex physical and dynamic interaction of machine tool and workpiece systems. Moreover, toolpath generation by CAD/CAM packages is purely geometric in nature and results in engagement angle variation along the toolpath. The selection of cutting conditions and toolpath rely solely on the part programmer’s experi- ence, CAD/CAM systems, handbook guidelines or specifications provided in the catalogues of cutting tools and machine tools. Their poor selection often causes chatter, high fluctuation of cutting forces, and/or violation of the available limits of power and torque of the machine tool. These phenomena result in poor surface finish, workpiece damage, high cutting tool wear, violation of tolerance limits, additional cost, unwanted waste and significant reduction in machine tool working life. In order to avoid these problems, part programs need to be verified iteratively using trial and error experiments and often conservative cutting conditions are selected. These practices lead to long preparation time of part programs and lower machining performance, which in a nutshell significantly lower overall productivity. Moreover, machine tool capabilities are not fully utilized due to the conservative selection of cutting conditions. In order to address these challenges, a genetic algorithm (GA) based optimal milling (OptMill) system is developed for optimal selection of cutting conditions and/or toolpath for a given set of inputs of machine tool/spindle/tool holder/cutting tool and workpiece system. Operational constraints of the machine tool, such as spindle speed and feed limits, available spindle power and torque, chatter vibration4 limits due to the dynamic interaction between cutting tool and workpiece, permissible limits of bending stress and deflection of the cutting tool and clamping load limits of the workpiece system are embedded. The developed system is applied to different industrial use cases: (i) Minimization of pocket milling time considering one-way toolpath (ii) minimization of machining time for multi-feature prismatic parts with the imple- mentation of pre-processing modules: extraction of toolpath and workpiece boundary from APT5 and STEP6 files respectively and calculation of engagement angle along the toolpath (iii) optimal selection of cutting conditions and corresponding smooth and constant engagement toolpath for pocket milling. The selected cutting conditions and/or toolpath are also validated using dedicated experiments conducted during the course of the research work. The present research work is inspired from an ongoing CTI project7. Following enhanced methodologies the identification of important inputs to mathematical models for prediction of cutting forces and chatter free limits have also been developed to expand the scope of the developed OptMill system. • Tangential force coefficients, an important input for prediction of cutting forces and chatter free limits, are identified experimentally with the use of a cutting force dy- namometer. This experimental setup is quite costly and not practical for industrial implementation. An enhanced methodology is presented for the indirect identifica- tion of tangential force coefficients from the spindle motor current. The methodology includes the development of an empirical model for cutting torque prediction from spindle motor current with the implementation of a spindle power model that accounts for all mechanical and electrical power losses. The cutting torque predicted by the developed model is then used for tangential force coefficient identification, and is also validated experimentally with direct measurement using a cutting torque dynamometer. • Dynamicresponseofeachvariantofmachinetool/spindle/toolholder/cuttingtool,in terms of FRF8, is required to predict chatter free limits accurately. FRF is often measured with hammer testing experiments. In order to avoid these tedious tests, an enhanced procedure using the receptance coupling technique is implemented to predict the FRF of a machine tool/spindle/tool holder/cutting tool system for different cutting tools. The predicted FRFs via numerical simulation are also validated with experimental measurement. Though the existing mathematical models predict accurately the chatter free limits, their use in small production floors has not yet been achieved due to the absence of technical expertise and experimental resources. Moreover, even modern machine tools do not provide any guidance to the machine operator regarding the occurrence of chatter during machining. To meet industrial requirements, a computationally fast, easy to use and practical system is developed that detects chatter automatically during milling and thereafter proposes a control strategy to the machine operator. The developed online chatter detection and control system is also validated experimentally with an industrial end-user partner. Apart from the many challenges and the developments discussed above, milling of thin- walled workpieces is also a concern due to changing dynamics during machining. Thus, an enhanced numerical procedure is developed for the selection of chatter free cutting conditions while considering the change in workpiece dynamics along the toolpath using finite element analysis. In order to realize the developed system, MATLAB is used as a programming language. Ge- ometrical modeling and part programming of prismatic parts is done with CATIA. The data acquisition platform for the experimental validation is designed in LABVIEW. Finite element modeling and analysis is implemented with the ANSYS parametric design language (APDL). The developed system is very appealing for industrial application by direct integration with existing CAD/CAM systems and/or modern machine tools. Increase in overall productiv- ity is ensured by optimal selection of cutting conditions and/or toolpath and simultaneous avoidance of repercussions due to their wrong selection.

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The milling process has been continuously optimized from many aspects: forces, velocities, stability, quality. Numerous papers have been published that report results in the domain of toolpath optimization with criteria such as raising the quality of the machined part, obtaining a stable machining process and maximizing the material removal rate. However, only recently researchers have started investigating the ecological impact of machining processes. With the constantly increasing prices of electrical energy and all environmental problems caused by the production and waste of the energy, it has become indispensable to look for ways to optimize machining processes from the energy consumption aspect, too. It is in this domain, then, that the work described in this dissertation contributes – to add a new criterion for the sustainable operation of machine tools: reducing the energy consumption during the use phase of the machine tool. The total energy spent in a machining process is the sum of power spent by all machine tool subsystems multiplied by the time that they are working. Therefore, the operating strategies for increasing energy efficiency for the high-speed machining processes must be oriented towards the reduction of the total machining time (productive and unproductive) and reduction of instantaneous consumed power in the machining process or, preferably, both. There are many causes for milling process time and energy inefficiency. The major sources of potential time reductions include: (i) the issues related to the geometry and topology of the toolpath („air-time”, overlapping segments, gouge and uncut regions, orientation of the toolpath), (ii) the issues related to the kinematics of the feed drives (the feed velocity/acceleration profile). The possible domains for power consumption reduction are related to the forces that exist in the machining process (cutting, inertial, gravitational and friction forces). The power invested to overcome the cutting force is necessary for the machining process but its consumption should be optimized through constant cutting engagement and feedrate scheduling strategies. The other forces are responsible for the pure mechanical power loss and they should be minimized. The optimization variables for inertial and gravitational forces include the feed motion profile and moving mass configuration, while friction losses are load and/or velocity dependent. In addition, there are some power losses in the electric components of machine tool drives (electrical power losses), whose dependency on velocity and mechanical load can also be modeled. Despite many research efforts to analyze and model the phenomena causing inefficiency of the machining process, they are usually not taken into account during milling process planning. In fact, rare are the attempts to use this knowledge to optimize the milling process with more than one of the above mentioned criteria. The reason for that lies in the fact that increasing the instantaneous values of tool engagement and feedrate in order to minimize total machining time also increases the consumed power and, indirectly, deteriorates the tool and/or destabilizes the cutting process. The existence of conflicting criteria in the objective functions imposes the use of evolutionary algorithms for multiobjective optimization. The process of experimental validation of such an optimization model requires costly and complex external hardware setup and it is also time consuming. Hence, there is an emerging need for the development of an accurate model, which would be used for the simulation of the milling process and testing the competing optimization strategies. The ultimate goal of contemporary manufacturing science and engineering is the development of a holistic virtual machine tool system. The motivation of this PhD research is to contribute to this objective by developing the “virtual milling process for prismatic parts (produced by 2.5D milling)”. This category of objects is selected, because the majority of CNC milling tasks can be performed using 2.5D milling. A very large number of mechanical parts are prismatic (their geometry consists exclusively of features that represent 2D contours extruded in a perpendicular direction). Parts that are even more complex are usually produced from a blank by a 2.5D roughing and 3D–5D finishing. Therefore, development of an accurate model of a 2.5D milling process represents a significant contribution to manufacturing engineering. Starting with above given problem description, we have defined the following research objectives: 1. Model power and energy consumption of a 2.5D milling machine as a function of the toolpath geometry (curvature and continuity), cutting engagement, real feedrate profiles and spindle rotational speed, taking into account moving mass configuration, all power losses (mechanical and electrical) in feed-axis and spindle drives and electromechanical constraints of servo drives; 2. Design an integrated software environment for simulation of the developed models; 3. Validate the model experimentally; 4. Demonstrate the potential of its use for multi-objective process optimization. Cutting engagement calculation is performed by using two competing methodologies, in order to make a comparison of their performances. The first one, the pixel based method, is based on discrete image processing, while the other, the exact method, is based on Boolean operations and vector geometry. The tests performed in more than 20 feature/toolpath combinations demonstrate that the exact method is more precise whereas the pixel-based method is computationally much faster. The machine tool operator commands a constant feedrate value. During the process, based on his/her subjective judgment, he/she can manually tune it to keep the process in the desired boundaries. One of the objectives of this research is to predict the real feedrate values so that they can be integrated in the part program before the process start. The feasible profiles of feed velocities for each toolpath segment are analyzed by answering the following questions: can the commanded feedrate be achieved and with what sequence of acceleration and deceleration phases, how much the tool has to slow down in corners and how does the feedrate profile degenerate in segments where the commanded feedrate cannot be reached? In order to determine all forces and torques, a machine tool model is developed. The following subsystems are modeled as rigid bodies (without vibrations): feed motor shaft, transmission, lead screw/nut pair, lead screw bearings, guides (sliding and rolling design), table with the workpiece and electrospindle (rotor, stator and bearings). All kinetic variables (linear and angular speeds; cutting, inertial and friction forces and torques) are then calculated in the joints of this multi-body system. The modeling of all speeds, forces and torques that occur in the machine tool system during the machining process, allows for calculation of various indicators of process time and energy efficiency: material removal rate, total machining time, useful power (invested in cutting), power loss due to the friction in the joints, power losses in electrical motors, electrical energy consumption of feed and spindle drives. In order to assess the accuracy and the reliability of the developed model two sets of experiments have been performed. The model validation is performed on several subsystem levels and the simulation results show a very good compliance with the experimental results. The first experimental setup consists of the force dynamometer, to measure cutting force components in linear axis directions, two laser optical position sensors, to measure the real feedrate and the power sensor, to measure the total power consumption of the machine tool electrical motors. The machining process was performed on the 6-axis machining center C.B. Ferrari A152 and the data acquisition platform was developed in LabVIEW 2010. The second experimental setup consists of the torque dynamometer, to measure the cutting torque directly on the spindle, the built-in current sensor that measures the motor current proportional to the electrical power consumed by the spindle and, again, the power sensor, to measure the total power consumption of the machine tool electrical motors. This time, MIKRON HPM 600U, a 5-axis milling machine equipped with the controller iTNC530, was used for the experimentation. The developed model is ready for practical implementation. Several examples have been prepared to demonstrate its capabilities in the domains of milling process simulation, sensitivity analysis and prospective optimization strategies. These examples include the prediction of various kinetostatic variables for given toolpaths, choice of the optimum machining strategy (minimization of machining time, moving mass and energy consumption) and process optimization by feedrate scheduling (reduction of cutting force fluctuation). As a conclusion, the major contribution of this PhD research is the development of a comprehensive rigid-body dynamics model of a machine tool system, aimed for the generation, simulation and optimization of 21⁄2D milling process plans. The model features several novelties compared to existing approaches: (i) it takes into account the machine tool rigid body dynamics and real tool kinematics in toolpath planning, (ii) it predicts all mechanical and electrical power losses in the machine tool system (iii) it allows the modeling of machine tool system electromechanical constraints. The model was experimentally validated on two real machine tools in different workshops and its capabilities for the simulation of different toolpath patterns and optimization of process parameters were successfully demonstrated using several examples.

Saurabh Aggarwal, Paul Xirouchakis

Pocket milling is the most known machining operation in the domains of aerospace, die and mold manufacturing. In the present work, GA-OptMill, a Genetic Algorithm (GA) based optimization system for minimization of pocket milling time, is developed. A wide range of cutting conditions: spindle speed, feed rate, axial and radial depth of cut, are processed and optimized while respecting the important constraints during high speed milling. Operational constraints of the machine tool system, such as spindle speed and feed limits, available spindle power and torque, acceptable limits of bending stress and deflection of the cutting tool and clamping load limits of the workpiece system are respected. Chatter vibration limits due to the dynamic interaction between cutting tool and workpiece are also embedded in the developed GA-OptMill system. Enhanced capabilities of the system in terms of encoded GA design variables and operators, targeted cutting conditions and constraints are demonstrated for different pocket sizes. The automatically identified optimal cutting conditions are also verified experimentally. The developed optimization system is very appealing for industrial implementation to automate the selection of optimal cutting conditions to achieve high productivity.

The rigid body motion of thin walled parts and their elastic-plastic deformations induced during high speed milling are the main root causes of part geometrical and dimensional variabilities; these are governed mainly from the choice of process plan parameters such as; fixture layout design, operation sequence, tool path strategies and the values of cutting variables. Therefore, to avoid failures and achieve better machining results it becomes necessary to judge the validity of a given process plan before going into actual machining. After carefully reviewing previous research works for machined part quality analysis of milled parts, it is apparent that the task of milling process verification and optimization considering the effects of overall machining process parameters viz. fixture layout, operation sequence, tool path and cutting parameters on part quality, is complex, needing the development of a generalized methodology based on scientific principles. It is the purpose of the proposed work to address the problem and develop a sound methodology for the prediction and reduction of errors(resulting from rigid-elastic displacements) induced during machining of thin wall prismatic parts(those needing 2.5 axis machining). In this dissertation, a state of the art milling process plan verification and optimization system called FEM-Mill has been developed. The main novelties of the developed system lies in its two important computational modules namely; (i) the FEM based 3D transient milling simulation environment to predict part errors, and (ii)The rule based expert system for diagnosis and rectification of part error causes(if any). The system also incorporates a newly developed GA (genetic algorithm) model to maximize milling productivity through optimal selection of cutting parameters respecting the machine-tool technological constrains such as, spindle power, cutter safety, cutting parameter limits etc. The aforesaid computation modules along with a feature based process planner and a generalized machining load model(ANN(Artificial Neural Network) based) are successfully developed and implemented using APDL (ANSYS parametric design language) and C++ programming language. The proposed system was extensively tested for a variety of prismatic parts with different degrees of geometrical complexities and various combinations of cutting parameters and fixture assemblies taken from the industrial partners viz., (i) MCM s.p.a, Italy, a medium size machining center manufacturing enterprise and (ii) Quinson s.a., France, aircraft part manufacturer. The obtained transient numerical results namely, the cutting force, workpiece temperature distribution, part deflection and stresses for different part geometries were validated with real field data experimentally obtained during the course of this research work. A good agreement between the numerical and experimental data show the validity of the presented FEM based milling simulation model in handling real field problems. The developed milling simulation model would be an efficient means for analyzing the thermal-structural aspects of machining and their influence on the resulting machined part quality. Thus it allows manufacturing engineers in setting appropriate machining process parameters and obtaining better machining results without needing preliminary cutting trials which generally demand huge investment both in terms of Time and Money. This dissertation deals with a review of relevant literature, design of the modular architecture of FEM-Mill and detailed description of the developed computational methodologies along with demonstrations of the functional capabilities of the system with the help of some industrial test cases.