Design of ultraprecision machine tools with application to manufacturing of miniature and micro components

Currently the underlying necessities for predictability, producibility and productivity remain big issues in ultraprecision machining of miniature/microproducts. The demand on rapid and economic fabrication of miniature/microproducts with complex shapes has also made new challenges for ultraprecision machine tool design. In this paper the design for an ultraprecision machine tool is introduced by describing its key machine elements and machine tool design procedures. The focus is on the review and assessment of the state-of-the-art ultraprecision machining tools. It also illustrates the application promise of miniature/microproducts. The trends on machine tool development, tooling, workpiece material and machining processes are pointed out

Design of a five-axis ultra-precision micro-milling machine—UltraMill. Part 1: Holistic design approach, design considerations and specifications

High-accuracy three-dimensional miniature components and microstructures are increasingly in demand in the sector of electro-optics, automotive, biotechnology, aerospace and information-technology industries. A rational approach to mechanical micro machining is to develop ultra-precision machines with small footprints. In part 1 of this two-part paper, the-state-of-the-art of ultra-precision machines with micro-machining capability is critically reviewed. The design considerations and specifications of a five-axis ultra-precision micro-milling machine—UltraMill—are discussed. Three prioritised design issues: motion accuracy, dynamic stiffness and thermal stability, formulate the holistic design approach for UltraMill. This approach has been applied to the development of key machine components and their integration so as to achieve high accuracy and nanometer surface finish

Ultra-precision Machining Process Dynamics and Surface Quality Monitoring

AbstractSurface finish deterioration in the ultra-precision machining (UPM) process is often attributed to dynamic instabilities. Models and approaches to predict UPM process instabilities are in their infancy. In the present work, UPM dynamics and its relationship to surface characteristics are studied using a combined analytical modeling and experimental effort. A one degree-of-freedom delay differential equation model that incorporates the joint effects of shear and ploughing taking place at sub-micrometer scale machining is investigated to capture the source of vibrations in UPM dynamics. A temporal finite element method (TFEM) was used to simplify the model to facilitate validation studies. The model was verified using an experimental UPM setup integrated with three accelerometers, a 3-axis dynamometer and an acoustic emission (AE) sensor. The setup was employed for face turning of 6061 aluminum workpiece using a single point polycrystalline diamond tool at different cutting conditions. The surface characteristics were measured offline using MicroXam®, a confocal optical microscope. Experimental investigations suggest that the model predictions of stability characteristics match 70% of the experimental observations. Additionally, even under stable UPM process conditions determined based on the analytical model, surface roughness of UPM machined workpieces varied significantly due to uncertainties associated with complex chip formation process, thermal effects and other uncontrollable factors. A sensor-based approach based on a nonparametric Gaussian process model was used to estimate surface roughness (Ra) using statistical and nonlinear features from force and vibration signals recorded at UPM process. Over 80% of the Ra estimations under test condition were consistent with the experiment measurements. Hence, by combining the physical and statistical models, we can choose suitable “stable” process conditions to yield surface finish Ra in 10-50nm range, and estimate the surface roughness changes in real-time

Sensor based real-time process monitoring for ultra-precision manufacturing processes with non-linearity and non-stationarity

This research investigates methodologies for real-time process monitoring in ultra-precision manufacturing processes, specifically, chemical mechanical planarization (CMP) and ultra-precision machining (UPM), are investigated in this dissertation.The three main components of this research are as follows: (1) developing a predictive modeling approaches for early detection of process anomalies/change points, (2) devising approaches that can capture the non-Gaussian and non-stationary characteristics of CMP and UPM processes, and (3) integrating multiple sensor data to make more reliable process related decisions in real-time.In the first part, we establish a quantitative relationship between CMP process performance, such as material removal rate (MRR) and data acquired from wireless vibration sensors. Subsequently, a non-linear sequential Bayesian analysis is integrated with decision theoretic concepts for detection of CMP process end-point for blanket copper wafers. Using this approach, CMP polishing end-point was detected within a 5% error rate.Next, a non-parametric Bayesian analytical approach is utilized to capture the inherently complex, non-Gaussian, and non-stationary sensor signal patterns observed in CMP process. An evolutionary clustering analysis, called Recurrent Nested Dirichlet Process (RNDP) approach is developed for monitoring CMP process changes using MEMS vibration signals. Using this novel signal analysis approach, process drifts are detected within 20 milliseconds and is assessed to be 3-7 times faster than traditional SPC charts. This is very beneficial to the industry from an application standpoint, because, wafer yield losses will be mitigated to a great extent, if the onset of CMP process drifts can be detected timely and accurately.Lastly, a non-parametric Bayesian modeling approach, termed Dirichlet Process (DP) is combined with a multi-level hierarchical information fusion technique for monitoring of surface finish in UPM process. Using this approach, signal patterns from six different sensors (three axis vibration and force) are integrated based on information fusion theory. It was observed that using experimental UPM sensor data that process decisions based on the multiple sensor information fusion approach were 15%-30% more accurate than the decisions from individual sensors. This will enable more accurate and reliable estimation of process conditions in ultra-precision manufacturing applications

Development of a compact ultra-precision six-axis hybrid micro-machine

High precision miniature and micro products which possess 3D complex structures or free-form surfaces are now widely used in industries. These micro products are usually fabricated by several machining processes in order to apply for various materials such as hard-to-machine steel and ceramic etc. The integration of these machining processes onto one machine becomes necessary since this will help reduce realignment errors and also increase the machining efficiency. In this research, an ultra-precision hybrid micro-machine which is capable of micro milling, micro grinding, micro turning, laser machining and laser assisted micro-machining has been designed and commissioned. Control software for on-machine metrology system (contact probe and dispersed reference interferometry (DRI)) and several plug-in modules including camera and handle system are integrated through a customised human-machine interface (HMI)

Diamond turning of contact lens polymers

Contact lens production requires high accuracy and good surface integrity. Surface roughness is generally used to measure the index quality of a turning process. It has been an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. In this study, an ONSI-56 (Onsifocon A) contact lens buttons were used to investigate the triboelectric phenomena and the effects of turning parameters on surface finish of the lens materials. ONSI-56 specimens are machined by Precitech Nanoform Ultra-grind 250 precision machine and the roughness values of the diamond turned surfaces are measured by Taylor Hopson PGI Profilometer. Electrostatics values were measured using electrostatic voltmeter. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness and electrostatic discharge (ESD) on the turned ONSI-56. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. The required data for predictive models were obtained by conducting a series of turning test and measuring the surface roughness and ESD data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for ONSI-56 are compared with each other using mean absolute percentage error (MAPE) for accuracy and computational cost

An integrated system for ultra-precision machine tool design in conceptual and fundamental design stage

This paper presents an integrated system used for ultra-precision machine tool (UPMT) design in conceptual and fundamental design stage. This system is based on the dynamic, thermodynamic, and error budget theories. The candidate configurations of the machine tool are first selected from the configuration library or a novel configuration designed by the user, according to the functions of the machine tool expected to realize. Then, the appropriate configuration is given by comparing the stiffness chain, dynamic performance, thermal performance, and the error budget of each candidate configuration. Consequently, the integrated design system enables the conceptual and fundamental of the UPMT to be designed efficiently with a theoretical foundation. The proposed system was used for several UPMT designs, which demonstrate the effectiveness of the integrated design system

An experimental validation and optimisation tool path strategy for thin walled structure

This work was carried out with the aim to optimise the tool path by simulating the removal of material in a finite element environment which is controlled by a genetic algorithm (GA). To simulate the physical removal of material during machining, a finite element model was designed to represent a thin walled workpiece. The target was to develop models which mimic the actual cutting process using the finite element method (FEM), to validate the developed tool path strategy algorithm with the actual machining process and to programme the developed algorithm into the software. The workpiece was to be modelled using the CAD (ABAQUS CAE) software to create a basic geometry co-ordinate system which could then be used to create the finite element method and necessary requirement by ABAQUS, such as the boundary condition, the material type, and the element type

Discrete wavelet transforms analysis of vibration signals for correlating tool wear in diamond turning of additive manufactured Ti-6Al-4V alloy

Ultra-precision machining (UPM) of Ti-6Al-4V alloy is widely regarded as a challenging material processing due to excessive tool wear and chemical reactivity of the tool and workpiece. Tool wear has a significant influence on the surface quality and also causes damage to the substrate. Therefore, it is critical to consider the tool condition during diamond turning, especially as precision machining moves toward intelligent systems. Consequently, there is a need for effective ways for in-process tool wear monitoring in UPM. This study aims to monitor the diamond tool wear using time-frequency-based wavelet analysis on vibrational signals acquired during the machining of Additively Manufactured (AM) Ti6Al4V alloy. The analysis employed Daubechies wavelet (db4, level 8) to establish a correlation between the Standard Deviation (SD) of the magnitude in the decomposed vibrational signal obtained from both the fresh and used tools. The analysis revealed that at a feed rate of 1 mm/min, the change in SD is 32.3% whereas at a feed rate of 5 mm/min, the change in SD is 8.4%. Furthermore, the flank wear and microfractures are observed using a scanning electron microscope on the respective flank and rake face of the diamond tool.The author(s) received no financial support for the research, authorship, and/or publication of this article