Material Removal Simulation Research Articles

Gentelligent processes in biologically inspired manufacturing

Production systems have to meet quality requirements despite increasing product individuality, varying batch sizes and a scarcity of resources. The transfer of experience-based knowledge in a flexible and self-optimizing production and process planning offers the potential to meet these challenges. Biological systems solve conceptually similar challenges pertaining to the transfer of knowledge, flexibility of individual reactions and adaptation over time. Thus, in the context of digital transformation, mechanisms derived from biology are interpreted and applied to the knowledge domain of production technology. To be able to exploit the potential of bio-inspired production systems, genetic and intelligent properties of technical components and machines were identified and brought together under the concept of “Gentelligence”. Expanding upon this concept with the new idea of process-DNA and biologically inspired optimization algorithms facilitates a more flexible, learning and self-optimizing production, which is shown in three different applications. By using the new concept of gentelligent process planning it is possible to determine machine-specific process parameters in turning processes in order to ensure appropriate roughness within the requirements. Furthermore, the combination of the concept with a material removal simulation allows the determination of the resulting process force in tool grinding for subsequent unknown workpiece geometries. As a result of using the process-DNA, a workpiece-independent knowledge transfer and thus process adaptation for shape error compensation becomes possible. Gentelligent production scheduling enables a process-parallel, holistically optimized machine allocation, and as a result, a significantly reduced lead time.

Process design for 5-axis ball end milling using a real-time capable dynamic material removal simulation

For repairing turbine blades or die and mold forms, additive manufacturing processes are commonly used to build-up new material to damaged sections. Afterwards, a subsequent re-contouring process such as 5-axis ball end milling is required to remove the excess material restoring the often complex original geometries. The process design of the re-contouring operation has to be done virtually, because the individuality of the repair cases prevents actual running-in processes. Hard-to-cut materials e.g. titanium or nickel alloys, parts prone to vibration and long tool holders complicate the repair even further. Thus, a fast and flexible material removal simulation is needed. The simulation has to predict suitable processes focusing shape deviations under consideration of process stability for arbitrary complex engagement conditions. In this paper, a dynamic multi-dexel based material removal simulation is presented, which is able to predict high-resolution surface topography and stable parameters for arbitrary processes such as 5-axis ball end milling. In contrast to other works, the simulation is able to simulate an unstable process using discrete cutting edges in real-time.

Generation of tailored subsurface zones in steels containing metastable austenite by adaptive machining and validation by eddy current testing

Abstract In order to withstand high mechanical and tribological loads, it is important that the components not only have a high core ductility but also a hard surface. Typically, a suitable microstructure is created by heat treatment processes before the workpiece is machined. However, these processes are time and energy consuming and can lead to component distortion. It would therefore be of great advantage if no additional heat treatment process would be required to produce a hardened subsurface zone. Since turning is often already integrated as a machining process in production lines, it would be advantageous to create a hardened subsurface within this process. As there is no possibility to measure the hardness directly during the turning process, a soft sensor was developed to determine the properties of the subsurface directly during the machining process. Steels with metastable austenite are of particular interest in this context, as metastable austenite can be converted into martensite by deformation. The amount of martensite produced in the subsurface can be adjusted provided that suitable turning parameters can be found. For this purpose, a process parallel material removal simulation was used to determine the actual conditions governing the process. It was found that there is a correlation between the martensite content and the amplitude of the 3rd harmonic of eddy current testing. Therefore, an eddy current sensor accompanying the process can be used as a basis for controlling the turning process for tailored martensite volume content adjustment.

Simulation-based feed rate adaptation considering tool wear condition

The process forces generated in machining are related to a deflection of the milling tool, which results in shape deviations. In addition to process parameters like feed rate, width and depth of cut or cutting speed, the wear condition of the tool has a significant influence on the shape deviation during flank milling. In process planning it is important to take the tool condition and the ideal time for tool change into account when selecting the process parameters. An assistance system is being researched at the Institute of Production Engineering and Machine Tools (IFW) in cooperation with Kennametal Shared Services GmbH to support this task. The assistance system adjusts automatically the feed rate considering a predefined maximum shape deviation. Additionally, it identifies an optimal moment for tool change. The advantages of the system are particularly evident in planning of individual milling processes. The assistance system is based on a combination of a material removal simulation and empirical models of the shape error. For this purpose, spindle currents as well as measured shape errors are stored in a database. These data are extended by the actual local cutting conditions calculated by a process-parallel material removal simulation. Afterwards, the data is transferred into process knowledge via a Support Vector Machine (SVM). Within a technological NC simulation before the start of manufacturing, the generated knowledge is applied to predict the shape error of the workpiece and to automatically adjust the feed rate. By adapting the feed rate, it is possible to control the tool life. The required tool change is defined by specifying a limit for the permitted width of flank wear land. The presented assistance system enables the prediction of the shape error parallel to the manufacturing process and the automatic determination of the feed rate as well as the ideal time for tool change.

Complex Parts Partitioning by Elementary Geometric Objects Clustering in CAD/CAM Process

Complex shaped parts are omnipresent in the industry: aeronautics, molds, etc. CAD/CAM software is used in their production process. To simulate this process, three geometric representation models can be considered: workpiece model, tool model and swept volume model which can be discrete or continuous. The use of discrete models is simple to implement compared to continuous models but requires a tight discretization to represent faithfully theoretical shapes. This is necessary for a high accuracy but increases processing time. To overcome this computing constrains, the main contribution of this work consists in partitioning the part space into regions by pooling the geometric elements (triangles, Dexels) into sets while introducing for the first time in this context two methods knows as Fast Global K-means (FGK) and Incremental K-means (IK), that arise from Basic K-means (BK) method presented in previous works. Another key point is developed; it consists on establishing a pretreatment on the Triple Dexels model in order to be able to use BK, IK and FGK methods in the material removal simulation process. A comparative study has been established to evaluate the performances of these methods. FGK demonstrated better performances in terms of computational time, cluster uniformity and reduction of regions overlapping areas, compared to BK and IK methods.

Prediction of Ground Surfaces by Using the Actual Tool Topography

This paper presents a prediction model for ground surfaces that uses the actual grinding wheel topography to perform a grinding simulation. Precise knowledge of expected machined surfaces plays an important role in process planning. Here, the main criterion is the achievement of the components’ function after manufacturing. Therefore, it is essential to consider the surface roughness to enable a function-orientated workpiece surface. The presented approach uses a real grinding tool topography, which is measured by a 3D laser triangulation sensor in the machine tool. After a data processing step, the measured topography is imported into a material removal simulation. A kinematic simulation of the realistic ground surface enables the data-based confirmation of the envelope profile theory for the first time.

Smoothed particle hydrodynamics (SPH) simulation of impinging jet flows containing abrasive rigid bodies

A fully Lagrangian model for simulating impinging jet flows containing abrasive particles is established based on smoothed particle hydrodynamics (SPH) method. In the model, both the fluid and the solid are described by SPH, where the jet flow is modeled as the viscous fluid and the metallic target is modeled as the elastic–plastic material. The main novelty of the model is that abrasive particles are explicitly included in the jet flow and modeled as arbitrarily shaped rigid bodies. The interactions among the fluid, solid and abrasives are modeled through suitable techniques that are commonly used in SPH. The simulation of material removal caused by the impact of continuous abrasive-jet flow is conducted as a challenging example to verify the applicability of the model. This new model is attractive for relevant applications, such as solid particle erosion and abrasive water-jet machining. The advantages of the model lie in its conceptual simplicity, straightforward implementation and the relative ease of incorporating new physics.

Material Removal Simulation in Sawing Processes of Photovoltaic Silicon

The wafering of thin silicon substrates is done by wire sawing technology. In this work a numerical model for the investigation of microstructural mechanisms like cracking and damage evolution during the sawing process is presented. A three-dimensional finite element model representing the phase transformation properties of silicon is validated by loading curves from nano-indentation experiments. By using cohesive zone finite elements, the crack lengths as well as crack initiation depths can be quantified and compared with the experimental results in terms of the maximum depth of subsurface damage.

Automatic re-contouring of repair-welded tool moulds

The process of repairing damaged tool moulds is conducted manually in the industry. This results in long process times as well as a high dependence of the repair result on the experience of the worker. After a visual inspection, the detected damages are removed by metal cutting and the missing material is filled by a build-up welding process. Afterwards, the target geometry is restored via machining re-contouring process. Because of the individual tool mould surface and welded seam, each repair case requires an individual machining strategy as well as toolpaths and process control parameters to ensure high surface quality and shape accuracy. This paper introduces an innovative design for re-contouring of repair-welded tool moulds, which takes into consideration the individual mould surface, repair welding and material properties. For that purpose, the actual geometry of the tool mould is measured directly in the CNC machine using an optical profile line sensor. Based on the measurement, the re-contouring process is planned automatically by means of a computer aided manufacturing (CAM) software. A material removal simulation with cutting force prognosis is carried out to adapt the process parameters individually with regard to repair time and surface quality. To set up the force and surface simulation model with high model quality, re-contouring experiments are carried out on welded seams made of 1.2343 (AISI H11) as well as on Toolox 44 and 1.2343 workpieces for comparison.

Advanced process design for re-contouring using a time-domain dynamic material removal simulation

The repair of components often requires the removal of excess weld material. This removal is considered as re-contouring. Re-contouring processes have to be designed individually for each case of damage to fulfil the high quality requirements. Therefore, a prognosis of the machined surface topography is crucial. The material removal simulation introduced in this paper allows the prediction of process stability and surface topography for 5-axis ball end milling including dynamic effects. Different process strategies for re-contouring of Ti-6Al-4V welds are examined. It is shown, that selecting suitable process parameters can lead to high surface quality while maintaining productivity.

Optimization of complex cutting tools using a multi-dexel based material removal simulation

Multi-dexel based material removal simulations provide a fast and flexible way to compute process forces and tool deflections for milling and turning operations. This allows an advanced process planning including detection of collisions for complex toolpaths. However, using dexel simulations for designing cutting tools has rarely been investigated. Especially the position of individual cutting edges is not considered, because current approaches only subtract the sweep volume of the tool envelop instead of the rake face. This paper presents a new method to design cutting tools using material removal simulations and a detailed tool geometry representation. The discretization of the tool allows an efficient calculation of the engagement conditions of individual cutting edges. The method is used to optimize novel porcupine milling cutters with round indexeble inserts, which produces a geometry analogous to serrated end mills. Based on the calculated forces, the positions of individual indexable inserts are adjusted to minimize the maximum radial force. An optimum has been found that reduces radial force by 12% compared to conventional porcupine milling cutters with squared inserts.

Estimation of the virtual workpiece quality by the use of a spindle-integrated process force measurement

Existing workpiece quality estimations utilize material removal simulations in combination with dynamic force measurements. The common force measurement method, using plate dynamometer, is not practicable due to space-constrains, high costs and inertial forces in case of 5-axis machining that falsify the measurement considerably. This paper examines the potential of workpiece quality monitoring by the use of a spindle-integrated high-resolution force measurement that uses displacement sensors. Considering the experimentally determined transfer-function between the force at the TCP and the displacement sensor, it is possible to calculate cutting forces during machining. The virtual quality is compared with CMM measurements.

Increasing productivity in heavy machining using a simulation based optimization method for porcupine milling cutters with a modified geometry

Porcupine milling cutters offer a high potential for increasing the metal removal rate in heavy machining of steel and titanium. Here, the available machine power and the maximum radial force represent important process limits. According to the current state of the art, mainly rectangular indexable inserts are used. Investigations show that the use of round inserts can significantly reduce the resulting radial force and cutting torque similar to serrated endmills. However, the design of such tools is a major challenge due to the complicated shape of cross-section of the undeformed chip. Therefore, this paper presents a new method for optimizing the position of individual indexable inserts by means of geometric material removal simulations. With the new method, the radial force can be reduced by 14%.

Self-optimizing tool path generation for 5-axis machining processes

This paper presents a self-optimizing process planning approach for 5-axis milling that allows an automatic compensation for tool deflection. For this purpose, process conditions are obtained from a process-parallel material removal simulation and merged with shape error measurements. Using machine learning methods, the resulting shape error is predicted and the tool path adapted automatically. The system has been implemented on a 5-axis CNC machine centre. It is shown that the resulting shape error can be reduced by 50%. Moreover, the article highlights the behaviour of the learning process and the transferability to other workpiece geometries.

Kinematic and stochastic surface topography of workpiece made of Al7075 in flank milling

In this paper, a method is given to predict the surface topography in flank milling based on the experiment data of Al7075. First, the kinematic topography of the machined surface is analyzed with dexel model by CutS. Then, the measured stochastic topography is described by four-distribution-moment Gaussian distribution and quadratic regression is used to fit the values. Abbott curve is applied in pseudo random number generation to create the simulated stochastic topography. Finally, the combination of the kinematic and stochastic topography is calculated by material removal simulations (MRS) and is used to predict the real measured topography. The results show that the roughness of the stochastic topography increases with an increased feed per tooth and depth of cut, and the combined topography mostly matches to the measured one within the proposed method.

Material Removal Simulation for Abrasive Water Jet Milling

Abrasive water jet (AWJ) milling, often referred to as abrasive water jet milling, has proven to be one of the most flexible non-conventional production techniques for difficult-to-machine materials. Currently no suitable material removal simulation for the water jet milling process is available. A modularized material removal simulation for the AWJ controlled depth machining process has been developed. Because the volume of material removal strongly depends on energy input per unit length of programmed toolpath, undesired deviations of the programmed feed rate like feed rate drops at sharp corners introduced by the water jet machine tool must be considered.

Process-parallel virtual quality evaluation for metal cutting in series production

Within the metal cutting industry, methods of statistical process control are used to monitor the product quality of series processes. Following the specifications of an initial test planning, produced components are inspected in a sample-like manner. Due to the large number of machining centers, the resulting high output of manufactured parts and centrally installed quality assurance departments, there is still a considerable time delay when feeding back negative trends or tolerance violations. Within this period, scrap parts may be produced. The approach presented in this paper addresses this problem. Based on a combination of process-parallel recorded sensor signals and available manufacturing data along the CAD-CAM-NC process chain, an online material removal simulation is performed next to a real milling process. Within the simulation, the properties of cutting tools, machine tool and manufacturing process directly impacting workpiece quality are compiled in efficient models that run simultaneously and further approximate the machined workpiece shape. The resulting, digital workpiece is measured virtually based on given quality requirements defined in ISO 1101 to allow an early identification of negative trends and quality issues while reducing long quality feedback loops.

A Generic Multi-Axis Post-Processor Engine for Optimal CNC Data Creation and Intelligent Surface Machining

This paper focuses on the development of a multi-axis post-processor engine with a curvature-based feed adaptation module, capable of extracting generic CNC data for high precision machining. The motivation of this work stems from the drawback of standard and commercial post-processors to modify their internal source codes so as to be implemented to newly-developed functions which integrate modern CNC units. The multi-axis post-processor proposed in this work operates as a stand-alone function of an artificial intelligent module that optimizes machining parameters for standard swept cut multi-axis surface tool-paths. The post-processor developed receives APT source files previously been optimized by means of a genetic algorithm that handles cutting tool selection; radial cut engagement; maximum discretization step; lead and tilt angles. The algorithm optimizes the aforementioned machining parameters towards the minimization of the number of cutter locations found in a specific APT source file as well as the surface machining error as a combined effect of chordal deviation and scallop height. The final APT output is then properly handled by the post-processor engine so as to extract the final ISO code for a double-pivoted head 5-axis CNC machine and compute optimal values for feed rate in each NC block considering the interpolation error and curvature analysis given the surface properties. To simulate and verify our proposals, the MAZAK Vortex 1000 gantry-type 5-axis CNC machine tool equipped with a Fanuc 15i CNC unit has been selected as the manufacturing resource corresponding to the final CNC output that the proposed post-processor computes. A benchmark sculptured part is created and used for the virtual material removal simulation in CATIA® V5 R18. For that part, both the proposed post-processor engine and a commercially available post-processor were employed to extract G-code data whilst it was shown that identical outputs were obtained.

Design of individual re-contouring processes

The re-contouring process of turbine blades is performed subsequently to the material deposit and restores the shape of the blade. Each process within the process chain has to be adapted to an individual repair case depending on size, location and specification of the damage. Each damaged blade represents a batch size of one. Hence, there is no run-in on the machine tool. This leads to the requirement of an error-free process from the very beginning. Therefore, a reliable process design is needed, including an evaluation of technological blade properties. This paper introduces an integrated method for the design of re-contouring processes. The presented conceptual design considers special requirements regarding an individual repair case, e.g. an individual blade shape, blade properties and material deposit. The method encompasses the definition of an individual repair case and the tool path planning using an interface of a commercial software for computer aided manufacturing (CAM). For evaluation of the tool path, the method contains a high-resolution material removal simulation and a downstream analysis of engagement conditions.

Material removal simulation for steel mould polishing

The surface finish of an injection mould influences the quality of the moulded polymer optic parts. In order to improve and control the surface finish of the mould it is important to be able to predict the material removal during the polishing process of this mould. The aim of this work is to predict the material removal during the polishing process, comparing the results obtained from polishing attempts on steel samples and the results obtained from a simulation model. A simulation model is developed with the abrasive wear Holm-Archard equation in ANSYS. This simulation model will help to eliminate the iterative trial and error polishing, therefore facilitating the steel mould production.