Tool Influence Function Research Articles

Mid-spatial frequency error restraint based on variable optimal angle-step trajectory strategy for the removal attenuation effect of magnetorheological finishing

In the field of ultra-precision manufacturing, such as lithography lenses, achieving nanometer-level errors across the entire frequency range is crucial. Magnetorheological finishing (MRF) technology, a high-precision processing method with high efficiency and low subsurface damage, often introduces mid-spatial frequency (MSF) error due to the removal attenuation effect and regular polishing trajectory in the long continuous polishing process. It causes various imaging and light transmission defects that limit the performance of precision optical instruments. The attenuation of material removal capacity of MRF is characterized by the attenuation of the tool influence function, which is obtained by an equal time interval point removal experiment. The variable optimal angle-step trajectory strategy is proposed to mitigate the removal attenuation effect of MRF and suppress MSF error. To validate the effectiveness and practicability of the proposed method, a uniform polishing experiment is performed on fused silica components. The experimental results show that the 90° grating trajectory introduces significant MSF error on surface shape with PV = 0.008 λ, and the variable optimal angle-step trajectory strategy does not introduce MSF error, which confirms the variable optimal angle-step trajectory strategy effectively eliminates the removal attenuation effect of MRF and suppresses MSF error. The study presents a general approach for ultra-precision optical processing and improves the manufacturing accuracy of optical components.

Mapping model of ribbon contour and tool influence function based on distributed parallel neural networks in magneto-rheological finishing

Magnetorheological finishing (MRF) stands out as a notable polishing technology, characterized by high precision and minimal damage. However, establishing an accurate and practical model for the tool influence function (TIF) of MRF poses a significant challenge. In this paper, a TIF modeling method of MRF based on distributed parallel neural networks is proposed for the first time. Assessment of the viability of this approach through multiple sets of robot-assisted MRF experiments is detailed. The experimental results conclusively demonstrate the successful intelligent prediction of TIF, with key indicators such as volume removal rate and peak removal rate achieving an average prediction accuracy exceeding 95%. This method can remarkably advance the intelligence of the TIF model in MRF and serve as a valuable reference for other optical fabrication methods.

Surrogate-model-based dwell time optimization for atmospheric pressure plasma jet finishing

With the rapidly increasing requirements for high-precision optical elements in modern optical systems, atmospheric pressure plasma jet (APPJ) finishing has attracted wide attention due to its high material removal efficiency and superior surface quality. However, the temperature fluctuation in the localized processing footprint, induced by the heated plasma and varying dwell time, emerges as a critical factor that oscillates the etching rate and thus degrades the deterministic removal control. Existing dwell time solution algorithms only apply under the assumption that the material removal rate exhibits temporal and spatial constancy, which is not the case in APPJ finishing. To address this challenge, a surrogate-model-based dwell time optimization approach for the temperature-dependent tool influence function (TIF) is proposed in this study. Firstly, a temperature-dependent TIF is proposed to quantitatively describe the thermal effect, and the corresponding calibration method is established. Subsequently, a thermodynamic simulation model is established to simulate the temperature of the localized processing footprint. By integrating the temperature-dependent TIF with the temperature prediction model, a novel dwell time optimization algorithm is proposed to alleviate the error caused by the temperature fluctuation. Given the prohibitive long computation time of the thermodynamic simulation model, a data-driven surrogate model based on Long Short Term Memory (LSTM), capable of rapid temperature prediction, is trained through the customized simulation dataset. The effectiveness of the proposed surrogate-model-based dwell time optimization algorithm is verified through simulations and experiments. Compared to traditional dwell time solution algorithms, the proposed approach yields a 47.12 % improvement in machining precision.

Efficient full-aperture mirror polishing method with variable orbital radius in computer-controlled optical surfacing

Optical systems in astronomy have extremely high requirements on the full-aperture surface precision and fabrication efficiency of aspherical mirrors. However, the current full-aperture optics fabrication method suffers from both fabrication and computation inefficiency. The former is caused by the isolated polishing strategy for the inner and edge regions of the mirror with different tools, while the latter is caused by the global computation strategy for the two regions. In this paper, a full-aperture mirror polishing method with the reversed strategy is proposed to solve this problem. Firstly, the dwell time of inner/edge regions are respectively calculated by the deconvolution method and the linear equations method based on the space-variant tool influence function. Therefore, both the computation cost and the edge polishing error can be reduced. Then, a fused tool path is developed to achieve variable orbital radius in the inner/edge region so the full aperture can be polished in one round. Simulations and experiments using a SiC aspherical mirror and large segmented mirrors demonstrate that the surface error can converge quickly in the full aperture. As a consequence, the full-aperture fabrication accuracy and efficiency can be greatly improved through the proposed method.

Research on the tool influence function characteristics of magnetorheological precession finishing (MRPF).

Magnetorheological finishing (MRF) technology is characterized by its high convergence rate and minimal subsurface damage as advantages. However, the non-Gaussian type tool influence function (TIF) it generates may cause mid-frequency errors and oriented surface texture issues. Magnetorheological precession finishing (MRPF) technology capable of generating Gaussian-like removal functions, lacks a clearly defined removal function model. This study acquired polishing spots in tilted polishing, discrete precession, and continuous precession modes via fixed-point polishing experiments. Using Multiphysics simulation software, stress and velocity distribution in the contact area were simulated. A TIF model, incorporating the synergistic effects of pressure and shear force and multiple influence coefficients, was proposed based on velocity characteristics across the three modes. To accurately predict the TIF, surface topographies with varying coefficients were constructed using this model, analyzing the coefficients' impact on the TIF profile. Optimal coefficients were identified using a least fit error algorithm. Further analysis of the TIF's internal textures revealed that the precession mode of MRPF yields superior surface quality, thereby elucidating the material removal mechanism of MRPF and laying a theoretical groundwork for advancing processing technologies.

Tool mark prediction on the surface of large-aperture mirrors via magnetorheological finishing.

The magnetorheological finishing (MRF) of surfaces often results in tool mark errors. A prediction model can effectively guide subsequent processing, necessitating thorough research. To address this issue, this paper introduces an enhanced continuous tool influence function method. This method involves sub dwell time convolution with varying tool influence functions, enabling tool mark prediction. Numerical simulations demonstrate the proposed method's effectiveness, while the data size is estimated to confirm its economic properties. Subsequently, a MRF experiment was conducted, affirming the practicability through power spectral density evaluation. A fast algorithm is given to guide tool mark predictions on large-aperture mirrors fabrication engineering subjected to sub-aperture polishing.

Mechanism of a deterministic sponge figuring processing (SFP) in Ni–P surface formation considering the robustness of the tool influence function: modeling and experimental investigation

Mechanism of a deterministic sponge figuring processing (SFP) in Ni–P surface formation considering the robustness of the tool influence function: modeling and experimental investigation

Modeling of tool influence function for the abrasive cloth flap wheel and optimization of polishing path spacing and feed rate

Modeling of tool influence function for the abrasive cloth flap wheel and optimization of polishing path spacing and feed rate

Compensation of the tool influence function changes under neighborhood effect in atmospheric pressure plasma processing.

Nowadays, computer controlled optical surfacing (CCOS) is greatly developed in the fabrication of ultra-precision optical surfaces. As one of the deterministic CCOS techniques, atmospheric pressure plasma processing (APPP) can achieve the efficient removal based on the chemical reaction. In this process, the temperature rising issue caused by plasma heating is of great importance for accelerating the etching rate, but also increases the difficulty of deterministic removal control, because the tool influence function (TIF) in APPP is no longer time-invariant. In the existing research, the time-variant TIF model can describe the TIF changes to a certain degree, which is considered in the dwell time calculation. However, there inevitably exists the strong neighborhood effect, which indicates that the TIFs of different dwell points are thermally interacted. Thus, the existing time-variant TIF model is no longer accurate for predicting TIF changes, and not suitable for application to further compensation. To solve this, the compensation of TIF changes under neighborhood effect is proposed in this paper. First, the mechanism of TIF changes caused by neighborhood effect is explained in depth. Then, the theoretical analysis is implemented and the compensation method is systematically discussed, encompassing the tool path optimization, model of TIF changes and dwell time calculation. Further, the proposed method is demonstrated and verified by a series of simulations. Finally, the experimental validation is carried out, which proves the capability of achieving high-precision removal and high convergence rate.

Research on the modification of the tool influence function for robotic bonnet polishing with stiffness modeling

Robotic bonnet polishing (RBP) technology is widely used in the polishing process of optical components, but industrial robots have the characteristics of a wide processing range and low body stiffness. Therefore, in the actual polishing process, there are different polishing forces due to different stiffnesses, which leads to different tool influence functions (TIF) affecting the surface quality of optical components. To achieve a uniform surface quality of the components polished by the RBP, we modeled the stiffness of the robot and modified the TIF in conjunction with the Preston equation, finally verifying the accuracy of the modified model through experiments. The results of the fixed-point polishing experiments show that the maximum relative error before the TIF correction is 12.5% and the maximum relative error after the correction is 2.8% within the robot's 600mm*600mm machining range. The results of the plane polishing experiments show that the maximum relative error before the TIF correction is 13% and the maximum relative error after the correction is 1.7%. Therefore, a method for TIF corrections from RBP based on stiffness modeling has important engineering implications.

Accurately predicting the tool influence function to achieve high-precision magnetorheological finishing using robots.

Industrial robots with six degrees-of-freedom have significant potential for use in optical manufacturing owing to their flexibility, low cost, and high space utilisation. However, the low trajectory accuracy of robots affects the manufacturing accuracy of optical components when combined with magnetorheological finishing (MRF). Moreover, general robot trajectory-error compensation methods cannot compensate for the running errors of large robots with high precision. To address this problem, a three-dimensional (3D) tool influence function (TIF) model based on inverse distance interpolation is developed in this study to accurately predict the TIF of different polishing gaps. A high-precision robot-MRF polishing strategy based on variable TIFs and surface shape accuracy of polished optics is proposed to achieve high-precision manufacturing without compensating for trajectory errors. Subsequently, the accuracy of a ϕ420 mm fused silica mirror is experimentally verified to be from 0.11 λ RMS to 0.013 λ RMS. This validates that the robot-MRF can achieve high-precision polishing without compensating for trajectory errors. Furthermore, the proposed model will promote the applications of industrial robots in optical manufacturing and will serve as a reference in the field of intelligent optical manufacturing.

Densi-melting effect for ultra-precision laser beam figuring with clustered overlapping technology at full-spatial-frequency.

Laser beam figuring (LBF), as a processing technology for ultra-precision figuring, is expected to be a key technology for further improving optics performance. To the best of our knowledge, we firstly demonstrated CO2 LBF for full-spatial-frequency error convergence at negligible stress. We found that controlling the subsidence and surface smoothing caused by material densification and melt under specific parameters range is an effective way to ensure both form error and roughness. Besides, an innovative "densi-melting" effect is further proposed to reveal the physical mechanism and guide the nano-precision figuring control, and the simulated results at different pulse durations fit well with the experiment results. Plus, to suppress the laser scanning ripples (mid-spatial-frequency (MSF) error) and reduce the control data volume, a clustered overlapping processing technology is proposed, where the laser processing in each sub-region is regarded as tool influence function (TIF). Through the overlapping control of TIF figuring depth, we achieved LBF experiments for the form error root mean square (RMS) reduced from 0.009λ to 0.003λ (λ=632.8 nm) without destroying microscale roughness (0.447 nm to 0.453 nm) and nanoscale roughness (0.290 nm to 0.269 nm). The establishment of the densi-melting effect and the clustered overlapping processing technology prove that LBF provides a new high-precision, low-cost manufacturing method for optics.

Mid-high-frequency error suppression of small optical aspheric molds.

To suppress the mid-high-frequency error of small optical tungsten carbide aspheric molds, it is proposed to quickly select the critical process parameters by simulating the residual error after convolution of the tool influence function (TIF). After polishing for 10.47min by the TIF, two simulation optimizations, RMS and Ra, converge to 9.3 and 5.347nm, respectively. Their convergence rates are improved by 40% and 7.9%, respectively, compared to ordinary TIF. Then, a faster and more high-quality multi-tool combination smoothing suppression method is proposed, and the corresponding polishing tools are designed. Finally, the global Ra of the aspheric surface converges from 5.9 to 4.5nm after smoothing for 5.5min with a disc-shaped polishing tool with a fine microstructure and maintains an excellent low-frequency error (PV 0.0781µm).

Theoretical and experimental investigation of material removal in semi-rigid bonnet polishing of binderless tungsten carbide

Binderless tungsten carbide (B-WC) has become a typical material for mold inserts due to its super mechanical and thermal properties. However, it is a typical hard-brittle material that is difficult to implement precision and high-efficiency polishing. This paper answers this challenge using a semi-rigid bonnet (SRB) tool, together with systematic theoretical and experimental investigation on its material removal. First, both polishing spots generation and uniform polishing experiments were conducted to demonstrate the polishing performance and material removal characteristic of SRB on B-WC substrates. Afterwards, the tool influence function model of SRB was established and verified experimentally, which enables the accurate prediction of surface generation during polishing. Finally, the surface and subsurface integrity of a B-WC substrate before and after SRB polishing was demonstrated. The results indicate that SRB is effective for fast polishing of B-WC with superior surface and sub-surface qualities. This discovery also paves the way for the application of SRB for the polishing of other hard materials. The developed material removal model can be a theoretical basis for the prediction of surface generation during the polishing process.

Numerical and experimental investigation on the effect of surface curvature and slope angle on the material removal characteristics in fluid jet polishing

Fluid jet polishing (FJP) has been extensively utilized in the ultra-precision manufacturing of freeform optical components and molds. The form accuracy is mainly improved by controlling the polishing dwell time at different positions, which is determined by the deconvolution of the initial form error and tool influence function (TIF). However, in previous studies, the erosion-induced TIF is considered constant without accounting for the freeform surfaces' curvature and slope angle variations. Due to the deviation of TIF, it is still a challenge to achieve a high form accuracy for freeform surface polishing. In this paper, the TIFs in freeform surface polishing have been modeled by computational fluid dynamics (CFD) analysis. A series of spot polishing experiments subsequently were conducted on the surfaces with different curvature radii and slope angles. The experimentally measured data including cross-section profiles of polishing spots, Peak-to-valley (PV) value, and material removal rate are found to agree well with the model simulation results, which verified the effectiveness of the CFD model. The effect of surface curvature and slope angle on the material erosion distribution was investigated and revealed by the fluid flow field distribution, abrasive impact, and abrasive trajectories. This paper not only provides a deep scientific understanding of the material erosion characteristics in FJP of freeform surfaces, but also offers a simple and cost-effective solution to build the database of TIFs on freeform surfaces under various conditions, which is beneficial for the ultra-precision form control during FJP of freeform surfaces.

Feed scheduling for time-dependent machining processes by optimization of bulk removal and NC blocks

Time-dependent processes such as CNC fluid jet polishing require feed-rate scheduling through deconvolution of a target removal profile and tool influence function. The dynamic constraints associated with the machine tool driving the process should be considered when solving this numerical problem. This research investigates how material bulk removal affects the scheduled feed-rate and acceleration both in the spatial and time domain, and proposes a method for matching exactly the maximum acceleration with the machine tool limit. Furthermore, the influence of CNC interpolator dynamics is investigated by means of the controller pulse response method. An optimization method for NC blocks is then proposed in order to achieve feed-rates that best match the intended profile. The benefit of bulk removal and NC blocks optimization is validated by fabrication, with and without optimization, of cylindrical Fresnel lenses. Both the shape accuracy and optical performance of the optimally fabricated lens are shown to substantially improve, suggesting that the proposed optimization method has strong potential for industrial application.

Surface generation modelling and form maintenance strategy in maskless fluid jet polishing of structured array surface

Structured array surfaces are extensively utilized in the fields of functional optical components and molds. However, high surface form accuracy of these surfaces is usually needed to implement superior performance, leading to great challenges in manufacturing. In this study, fluid jet polishing (FJP) was utilized to polish the cylindrical array surface. The effect of abrasive size and jet pressure on the form maintainability was elucidated by combining computational fluid dynamics (CFD) simulation. The abrasive trajectory and abrasive impact information were analyzed to reveal the material removal process. Tool influence functions (TIF) at different positions were simulated to model the surface generation process and provide a theoretical basis for the optimization of polishing parameters. A surface form maintenance strategy was proposed to achieve high form accuracy together with excellent surface quality by controlling the feed rate of the nozzle. A series of experiments were conducted to validate the effectiveness of the form maintenance strategy. The results revealed that the form error agreed well with the requirement after polishing with the designed feed rate. This study paves a new way for the polishing of structured array surfaces with a good surface form control.

Statistical perception of the chaotic fabrication error and the self-adaptive processing decision in ultra-precision optical polishing.

Subaperture polishing is a key technique for fabricating ultra-precision optics. However, the error source complexity in the polishing process creates large fabrication errors with chaotic characteristics that are difficult to predict using physical modelling. In this study, we first proved that the chaotic error is statistically predictable and developed a statistical chaotic-error perception (SCP) model. We confirmed that the coupling between the randomness characteristics of chaotic error (expectation and variance) and the polishing results follows an approximately linear relationship. Accordingly, the convolution fabrication formula based on the Preston equation was improved, and the form error evolution in each polishing cycle for various tools was quantitatively predicted. On this basis, a self-adaptive decision model that considers the chaotic-error influence was developed using the proposed mid- and low-spatial-frequency error criteria, which realises the automatic decision of the tool and processing parameters. An ultra-precision surface with equivalent accuracy can be stably realised via proper tool influence function (TIF) selection and modification, even for low-deterministic level tools. Experimental results indicated that the average prediction error in each convergence cycle was reduced to 6.14%. Without manual participation, the root mean square(RMS) of the surface figure of a ϕ100-mm flat mirror was converged to 1.788 nm with only robotic small-tool polishing, and that of a ϕ300-mm high-gradient ellipsoid mirror was converged to 0.008 λ. Additionally, the polishing efficiency was increased by 30% compared with that of manual polishing. The proposed SCP model offers insights that will help achieve advancement in the subaperture polishing process.

Fluid lubricated polishing based on shear thickening.

With the development of short wavelength optics, high requirements are put forward for the full frequency errors of optical elements, while the processing efficiency and surface quality of traditional polishing methods are difficult to meet their requirements. In this paper, a fluid lubricated polishing method is proposed by combining non-Newtonian fluid with traditional polishing methods. According to Preston equation and shear thickening principle, the tool influence function of fluid lubricated polishing is established and verified by experiments. The results show that the fluid lubricated polishing has a very good convergence ability to the full frequency error of the workpiece. In addition, the convergence rate of fluid lubricated polishing on roughness is about twice that of chemical mechanical polishing. Finally, fluid lubricated polishing extends Preston from Newtonian fluid polishing to non-Newtonian fluid polishing.

Understanding the tool influence function during sub-aperture belt-on-wheel glass polishing.

The tool influence function (TIF) during sub-aperture belt-on-wheel polishing has been evaluated as a function of various process conditions (belt use/wear, dwell time, displacement, belt velocity, and wheel modulus and diameter) on fused silica glass workpieces using C e O 2 polishing media. TIF spots are circular or elliptical in shape with a largely flat bottom character. The volumetric removal rate varies significantly with belt use (or wear), stabilizing after ∼15m i n of use. A modified Preston model, where the pressure dependence is adjusted using a different scaling of the wheel modulus (E w0.5), largely predicts the volumetric removal rate over the range of process conditions evaluated. The relatively high volumetric removal rate of 30-60m m 3/h using a fixed C e O 2-in-resin-host belt offers a rapid, and hence, more economical, initial polish of aspheric and freeform optics.