Material Removal Simulation Research Articles

Kinetic simulation and experimental study of steel material removal by the diamond bead

Kinetic simulation and experimental study of steel material removal by the diamond bead

Active learning for the prediction of shape errors in milling

In machining processes, various influences, such as workpiece and tool geometry, process parameters or tool wear, can lead to decreasing part quality. Thus, predicting these influences to enable better process planning is essential. However, no general, analytical model is available to facilitate this. Data-driven approaches, on the other hand, are costly due to the required data labeling efforts. To address this, the authors present a data-driven active machine learning approach to predict shape errors based on process data enhanced by a material removal simulation. Using two representative pocket milling datasets, it is shown that this approach can yield better (up to 5 % decrease of RMSE) and more consistent (up to 85 % decrease in standard deviation of RMSE) model accuracy for a given budget of tactile probing points compared to a passive learning strategy.

Simulative surface topography prediction of tribological surfaces on whirled thread flanks

This paper investigates the surface texture on trapezoidal thread flanks manufactured by whirling, which is used for machining long threaded spindles. An analytical and numerical approach for describing surface texture is compared, and texture parameters are developed based on existing methods. Although the analytical model provides a good description of texture height and length, it has limitations in representing the surface as a topography. In contrast, the numerical approach enables detailed modeling of the surface topography on the thread flank as a function of the process parameters. The effective number of flank cutters is found to significantly influence the resulting topography, leading to differences between the measured values and the analytical model within a specified investigation range. Using a material removal simulation, this effect could be explained by the flank cutter position error. In addition, material removal simulation offers great potential for optimizing the surface texture on thread flanks and for tailoring tribologically optimized surfaces in the whirling process.

A model based material removal simulation for vacuum suction blasting of composites

Automated scarfing of carbon reinforced plastic (CFRP) layers is on its way to support commercial aircraft repair. In the industry, still, the manual scarfing operation is the qualified method. However, automated techniques as milling, laser removal and water jet cutting are in development and showed already good results. Another promising method is vacuum suction blasting (VSB) that was until now in particular used for the roughening of surfaces before adhesive bonding. To find the right adjustment for the parameters many experiments would be necessary accordingly to different CFRP parts with changing layer thicknesses. Simulation is a way to avoid this and to predict the removal result and the settings for the machining parameters. The VSB model uses a pixel method dividing the simulated part surface into smaller volume elements. Experimental data of VSB static blasting spots are the basic for the source matrix. The simulation feeds the matrix with the blasted depth and removal volume for different blasting times. A shifting of columns of the source matrix in the blasting movement direction simulates the movement of the blasting nozzle on the work piece surface. With this, the model can also predict the nozzle feed to remove exactly one complete layer for each scarfing step. In addition, it visualizes the seamless overlapping distance between two blasted tracks. With further adjustments, the model will predict the dynamic removal for varying input parameters such as negative pressure and nozzle distance or blasting agent.

Simulation-based collision detection for CNC machining using sensor-based image recognition

In milling processes, collisions lead to cost-intensive machine damages and long-term maintenance downtimes. Collisions tests are essential in CAD/CAM planning when it comes to small batch sizes e.g. for aerospace parts. Currently, experimental tests are carried out based on the nominal clamping situation and CAD data. Clamping errors, probing errors and particularly incorrect tool lengths are thereby not considered. For this reason, a concept for a sensor-based collision detection system is presented. The integration of an automatic image recognition in combination with a material removal simulation enables an accuracy of 96.87 % in collision detection for three defined reference workpieces.

Process strategies for milling of dimples on tapered roller bearings

Frictional losses occur in tapered roller bearings, particularly at low rolling speeds, which pose a risk of wear. The increased friction losses are a result of insufficient lubricant film thicknesses in the rolling and rib contact. Micro-lubrication dimples can be used to induce additional lubricant into the contact zone and minimize friction. The aim of this paper is therefore to implement a suitable machining strategy for the production of defined microstructures for tribologically optimized applications and to identify relationships between geometry formation and process parameters. For this purpose, the microstructure milling process was first modelled with a material removal simulation, including tool displacement. Additionally the kinematic limits of the machine tool were determined. The tool displacement was determined experimentally for this purpose. Subsequently, the findings from the simulation were used to induce microstructures in a defined manner on tapered roller bearings made of hardened 100 Cr6 steel. The investigations showed that the defined generation of lubrication dimples is possible with the developed machining strategy. Due to the inclination of the inboard bearing, there is a deviating depth of engagement when the tool penetrates, which also increases the tool displacement. As a result of the microstructure milling process, burr formation occurs, which shows a dependence on cutting speed and structure alignment. Increased burr formation and tool wear at structure orientations of 45° and 70° were found.

Multiphysics Simulation of the Shape Prediction and Material Removal Rate in Electrochemical Machining Process

ABSTRACT The electrochemical machining process involves electrical and chemical processes while removing materials from the workpiece. Electrochemical machining (ECM) lacks accurate models for predicting the shape and material removal rate during the ECM process. This limits the ability to optimise the process and predict the final product geometry, which is crucial for industrial applications. This research mainly focused on the modelling and simulation of material removal from the workpiece using COMSOL Multiphysics software. This research involves developing a mathematical model that describes ECM’s electrical, chemical, and mechanical interactions. The model is based on electrochemistry, fluid dynamics, and solid mechanics principles and will be solved using numerical simulation techniques. Various workpiece materials considered in this investigation include Aluminium, Nickel, Stainless Steel, and Tungsten, whereas copper is electrode material. The effects of the various parameters, such as workpiece materials, the voltage applied during machining, and electrolyte conductivity on the material removal rate are being investigated. In addition, the shape of the machined workpiece is also predicted. The results of this research provide a deeper understanding of the underlying physics of the ECM process and lead to the development of more accurate models for predicting the shape and material removal rate during ECM.

Digital surface twin for ultra-precision high performance cutting

Increasing productivity of ultra-precision (UP) machining is vital to apply this technology in a broader range of applications. In order to achieve this goal, technologies such as diamond cutting with multiple cutting edges or the application of electromagnetic levitation guides are researched. Due to the additional, influencing factors from these technologies, a digital surface twin is beneficial for predicting surface features and characteristics. In this work, two approaches for generating a digital surface twin are investigated: a surface simulation based on numerical height maps and a dexel-based material removal simulation. Both incorporate multi degree of freedom position data provided by the levitation guide. Fly-cutting experiments are conducted to validate the digital twin approaches. Both approaches are able to predict surface profile, surface roughness and waviness with high accuracy. It is also shown, that the digital twin can support the development of compensation approaches for tool offsets. In conclusion digital surface twins for ultra-precision cutting offer a high potential to support productivity improvement especially in combination with axis position data. Further work will focus on real-time integration of the approaches.

Operational behaviour of graded diamond grinding wheels for end mill cutter machining

The varying related material removal rate during deep grinding of cemented carbide end mill cutters results in an unevenly wear of the grinding wheel. This study therefore presents a simulation-based model for the load-adjusted design of grinding wheels to achieve balanced radial wear, as well as an evaluation of this model. The related material removal rate along the width of the grinding wheel is determined by a Dexel based material removal simulation for different end mill geometries. Based on these results an equation is derived to adapt the abrasive layer properties to the local load differences. Three grinding wheels with different types of gradients are then manufactured by a grinding tool manufacturer based on this equation. These and two grinding wheels with constant abrasive layer properties are used for deep grinding of ten end mills each. Afterwards the radial wear of each grinding wheel is measured by a confocal microscope. An analysis of the cutting edge chipping is done to evaluate the influence on the graded grinding wheels on the cutting edge quality. It was found that a reduction of the wear difference over the grinding wheel width of 52% and an improved cutting edge quality can be achieved by using graded grinding tools. This allows the time intervals between dressing steps to be increased without compromising the accuracy of the grinding process, thus also increasing its productivity. Finally, this article shows that the presented model allows for a more balanced wear behaviour, but has to be extended by considering further factors influencing radial wear.

Edge computing-based virtual measuring machine for process-parallel prediction of workpiece quality in metal cutting

Digital process twins enable in-line quality inspection and root cause analysis, thus sustainably optimizing the production ramp-up and quality control cycle. Current approaches reply on expensive computing units or platforms to perform material removal simulations followed by virtual metrology. This raises deployment costs and conflicts with industrial edge devices. In this paper, a novel analytical approach is proposed to build an edge computing-based digital twin of machining processes for the process-parallel prediction of workpiece tolerances by leveraging machine internal signals and contextualizing metadata sources along the digital process chain, i.e., the digital thread. This concept is validated with the results of a state-of-the-art digital process twin and conventional measurement data from a coordinate measuring machine.

MODELLING OF PROCESS FORCES FOR COMPLEX MULTIAXIAL TURNING PROCESSES

Increasing demands regarding productivity and component quality are a major challenge in turning. To meet these demands, complex multiaxial turning processes like enhanced variants of the trochoidal turn-ing processes are increasingly used. For these processes, the tool path is optimized to achieve advanta-geous cutting conditions and thus higher productivity. However, the process forces and their relations to the process parameters for these processes are currently unknown, which complicates the process de-sign and calculation of required clamping forces. This paper presents a simulation based approach to estimate the process forces of complex multiaxial turning processes. Therefore, a dexel based material removal simulation is used to calculate the chip parameters, e.g. undeformed chip thickness, and the chip cross-sectional area. On this basis, the process forces are modelled as a function of the undeformed chip thickness and undeformed chip width. By this, the force model is parameterized and the calculated process forces are validated by comparison with process force measurements.

Reprint of: 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.

Identification of rake and flank face engagement parameters using a dexel-based material removal simulation with an oriented sweep volume

This paper presents an approach for the simulation of cutting processes with a dexel based-simulation considering process damping. The approach allows an independent modeling of engagement parameters for both rake and flank faces of arbitrary tool and workpiece geometries including dynamic behavior for the first time. A method is introduced for calculating the indentation volume of the flank face independently of the rake face. Therefore, the oriented sweep volume is introduced. The indentation volume of the flank face is used to model process damping for improving stability and dimensional form error prediction. For verification, experimental milling tests are carried out to create stability charts and surfaces. Both, the stability and surface prediction of the approach are analyzed for a chamfered tool, where a flank face - workpiece contact occurs, and an unchamfered tool, where no flank face - workpiece contact occurs. Even though the prediction accuracy of the stability limit can be improved compared to different approaches, there are significant differences between simulation and experiment. Thus, there is still a high need for a more accurate modelling of the process damping effect.

GPU accelerated voxel-based machining simulation

The simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex machining processes and large workpieces is within reach. These simulations require fast material removal predictions and analysis with high spatial resolution for multi-axis operations. Within this contribution, we propose to leverage voxel-based concepts introduced in the computer graphics industry to accelerate material removal simulations. Corresponding schemes are well suited for massive parallelization. By leveraging the computational power offered by modern graphics hardware, the computational performance of high spatial accuracy volumetric voxel-based algorithms is further improved. They now allow for very fast and accurate volume removal simulation and analysis of machining processes. Within this paper, a detailed description of the data structures and algorithms is provided along a detailed benchmark for common machining operations.

Simulation-Based and Experimental Investigation of Micro End Mills with Wiper Geometry.

One of the major advantages of micromachining is the high achievable surface quality at highly flexible capabilities in terms of the machining of workpieces with complex geometric properties. Unfortunately, finishing operations often result in extensive process times due to the dependency of the resulting surface topography on the cutting parameter, e.g., the feed per tooth, fz. To overcome this dependency, special tool shapes, called wipers, have proven themselves in the field of turning. This paper presents the transfer of such tool shapes to solid carbide milling tools for micromachining. In this context, a material removal simulation (MRS) was used to investigate promising wiper geometries for micro end mills (d = 1 mm). Through experimental validation of the results, the surface topography, the resulting process forces, and tendencies in the residual stress state were investigated, machining the hot work tool steel (AISI H11). The surface-related results show a high agreement and thus the potential of MRS for tool development. Deviations from the experimental data for large wipers could be attributed to the non-modeled tool deflections, friction, and plastic deformations. Furthermore, a slight geometry-dependent increase in cutting forces and compressive stresses were observed, while a significant reduction in roughness up to 84% and favorable topography conditions were achieved by adjusting wipers and cutting parameters.

Parametric grinding wheel model for material removal simulation of tool grinding processes

Tool grinding is an essential process for the production of cemented carbide tools. In that context, the investigation of specific effects like the resulting surface profile and the fluid dynamic processes is of great interest, but requires microscopic modeling of the grinding wheel including its individual grains and bonding material. This paper introduces an approach for a parametric grinding wheel model, which provides a topography on microscopic scale depending on the grinding wheel specification and dressing conditions for subsequent use in material removal simulations. Scalable abrasive grains and variable distributions embedded in a shiftable bond layer are applied. Optical laser scans are used to derive surface parameters for an adaption and evaluation of the model. The prediction quality in terms of surface roughness is evaluated in surface grinding reference experiments.

Method for Data-Driven NC-Code Optimization based on Dexel Material Removal Simulation and Tool Holder Vibration Measurements

This paper presents a novel approach for data-driven NC-Code optimization, based on the integration of dexel-based material removal simulation and an instrumented tool holder, capable to measure vibrations during milling close to the cutting zone. Considering measured cutting vibrations, machine tool axis and NC-line data, a model has been developed optimizing cutting parameters to generate a NC-Code right after a first machining trial with mitigated vibration effects. Different modules for human-assisting vibration visualization and automated optimization of cutting parameters are presented, using milling use-cases implemented on a CNC machining center.

Prediction of plastic surface defects for 5-axis ball end milling of Ti-6Al-4 V with rounded cutting edges using a material removal simulation

The resulting surface quality after 5-axis ball end milling is of superior importance because finish milling is often the last process step determining the functional performance of a component. However, the prediction of surface topography is still a challenging task. Especially in ball end milling with the characteristic sickle shaped chip cross section, ploughing effects in the area of low chip thickness result in plastic deformation and surface defects (also known as burr). This paper provides a new approach to predict those surface defects by considering the minimum chip thickness for complex milling engagement conditions within a virtual process design. This allows the choice of suitable process parameters without extensive experimental efforts.

Advances in Modeling of the Kerf Formation considering the Primary and Deflection Jets for the Abrasive Water Jet Technology

Processing of difficult to machine materials is a promising application for abrasive water jet kerf cutting and milling. However, due to the large number of interactions of an energy-bound cutting process, a precise prediction of the material removal is crucial for its application. Based on an analytical approach, a material removal simulation model is introduced considering the primary and deflecting jet impacts to rapidly predict various cutting situations. The model describes fundamental cutting mechanisms considering the water jet’s material removal rate and is calibrated for titanium aluminides. The model allows for a comprehensive kerf prediction and thus potentially accelerates the process design improving the productivity and quality for abrasive water jet kerf cutting and milling.

Optimised process planning for re-contouring of repair-welded tool moulds by using a specific force model

In the repair process of tool moulds, detected damages are removed and the resulting cavities are filled up by a welding process. Due to different types of damage and unknown weld seam properties, each repair case requires an individual machining strategy to ensure high quality. The re-machining step, so-called re-contouring, is currently very time-consuming and leads to experience-dependent workpiece quality. Therefore, this paper presents a specific Kienzle force model combined with optimisation strategies to adapt process parameters and toolpaths as a part of an automated re-contouring process flow. Based on experimental investigations, the force model is generated and used in a material removal simulation to predict the cutting forces according to the local cutting conditions. The method will be validated by re-contouring experiments with welded workpieces.