Jayaraman Raja

Multi-scale analysis of engineering surfaces

Abstract Conventional surface characterization techniques involving random process analysis are limited in characterizing multi-scale surface features relevant to manufacturing processes and functions. This paper introduces a novel technique for multi-scale characterization of engineering surfaces by applying wavelet transform. The main advantages of wavelet transform over other existing signal processing techniques are its space-frequency localization and multi-scale view of the components of a signal. Utilizing these properties of wavelet transform, we can effectively apply multi-channel filter banks to the surface data and link the manufacturing and functional aspects of a surface with its multi-scale features. Surfaces produced by typical manufacturing processes are analyzed using wavelet transform, and the usefulness of wavelet transform in the multi-scale analysis of engineering surfaces is demonstrated.

Line edge roughness metrology using atomic force microscopes

Line edge roughness (LER) measurements using two types of atomic force microscopes and three types of tips are compared. Measurements were made on specially prepared samples with inscribed edge roughness of different amplitudes and wavelengths. The spatial wavelengths and amplitudes each instrument was able to measure are compared. Techniques on checking the noise level of LER measuring instruments are highlighted.

Analysis of lay characteristics of three-dimensional surface maps

Abstract This paper describes a technique which can characterize the surface lay structure contained within a three-dimensional surface map. The technique is based on a generally known fact, that two, closely spaced, parallel profiles will appear shifted whenever the dominant lay direction is not perpendicular to the profile direction. The cross-correlation function is utilized to evaluate these apparent shifts. It will be shown that the functional relationship of these shifts is a characteristic of the lay pattern. Simple linear models are then applied to the shifts to quantify the lay characteristics. The resulting models can be used to extract lay angles and curvatures which correlate directly to the manufacturing process used to create the surface.

Thermal imaging with near-field microscopy

Optical microscopy is an important measurement tool in many industries. This importance is primarily due to the ease-of-use and nondestructive characteristics of optical microscopes. Unfortunately, the far-field optics of conventional microscopes limit their resolution to approximately 200 nm. An imaging technique called near-field microscopy uses a subwavelength aperture to circumvent this limit to obtain images with enhanced resolution without many of the destructive consequences of other techniques. Visible microscopes based on this technique have produced images that demonstrate 10–15 nm resolution. This article describes the extension of these techniques to the infrared regime. A description of an infrared microscope capable of imaging the thermal emissions from micron scale conductors using optical techniques has been given. The microscope has been designed to operate in both the collection mode using an external infrared radiation source and in the self-illumination mode using thermally activated objects. Several infrared images of 2-μm-wide conductors have been provided to demonstrate the resolution capabilities of the microscope. These images clearly show the presence of the conductors and represent a significant increase in resolution over conventional infrared imaging devices.

An internet based surface texture information system

This paper presents an internet based surface texture analysis and information system, which applies Java and internet technology to engineering metrology. The system includes most of the standardized and advanced analysis tools and a database for surface texture. The most distinct feature of this system is that it can run on the internet, which enables platform independent and remote data and analysis of surface texture.

Data Fusion Strategy for Multiscale Surface Measurements

Interdisciplinary research efforts have started focusing on the development of multiscale models and development of designer multiscale surfaces exhibiting specific properties at different scales for a specific purpose. With the rapid evolution of these new engineered surfaces for microelectromechanical systems (MEMS), microfluidics, etc., there is a strong need for developing tools to measure and characterize these surfaces at different scales. In order to obtain all meaningful details of the surface at various required scales, one is left with the only option of measuring the surface using multiple technologies using a combination of instruments. The majority of hardware-based approaches focus on the development of systems housing multiple technologies/capabilities into a single frame. These systems enable the user to obtain different surface maps using various technologies, but the user does not readily have the ability to combine all the obtained data into one single dataset. The effective approach toward multiscale measurement and characterization would be to use the individual measurement tools and finding a method to relate the individual coordinate systems and use an offline virtual tool to unify, manipulate, segment, merge, and retrieve data. Shape primitives and focus-based fusion strategies cannot be used as every data point in the data sets under consideration has to be treated as essentially at optimal focus. A multiscale data fusion strategy results in edge effects on nonplanar and high aspect ratio surfaces. An optimized fusion strategy, the “FWR method,” for the surface metrology domain is proposed where the subimages obtained from discrete wavelet frame (DWF) were separated into three regimes—form, waviness, and roughness—and fusion was not performed on subimages in the form regime. This approach effectively eliminates the edge effects. Individual data-point-level fusion was successfully demonstrated on Fresnel microlens array surface data as a case study of a nondirectional engineered surface with high aspect ratio.

Surface wavelength content based clustering using neural networks for manufacturing process mapping

Abstract Manufacturing processes produce a unique texture on the surface that serves as a fingerprint of the process. It is possible to provide feedback to the process by studying the surfaces carefully. Analytical techniques such as Fourier analysis and digital filters are commonly used to characterize surface profiles. Parameters extracted from filtered profiles are monitored to detect variations in the process. This requires the development of an inference engine to map metrology parameters to manufacturing process parameters. This paper presents an artificial neural network (ANN) based inference engine for providing process feedback with surface finish input. Parameters such as Ra and Wa as well as advanced wavelet based features are extracted from surface finish data collected from a crankshaft manufacturing line and fed as input to the neural network. This input is then clustered using a competitive neural network trained in unsupervised mode. The resulting clusters are analyzed and discussed. The network is then tested with new data to evaluate the quality of the clusters previously generated and to demonstrate the applicability of this technique for detecting process variations.

Local slope analysis in the stylus-based assessment of surface integrity

Abstract The need for a robust indication of surface integrity is addressed thrcugh the utilization of a traditional stylus instrument. Conventional parameters do not characterize adequately the functionally important, qualitative aspect of the surface. However, with the knowledge of the stylus flank angle and through the analysis of local slopes, a determination of the presence of reentrant features (folds, micro-burrs, etc.) can be made. The development of the methodology is presented along with an example of its application to machined cast iron surfaces. Further considerations are presented in light of the application of this methodology to the general assessment of surface integrity.

Internet based software system for surface texture and form analysis

With the emergence of a global market and the recent trend of outsourcing, it has become increasingly necessary for companies to have remote access to data for process diagnostics and quality control. This paper presents an Internet based surface texture and form analysis system that will allow a user at one geographic location to upload surface profiles into a database that another user can later analyze. This system has all the features found on commercial instruments and also some special analysis tools such as wavelet analysis, valley removal algorithms etc. The capabilities include surface texture analysis with database support, straightness, flatness, roundness and cylindricity analysis.

The robust detection and removal of unwanted asperities in applied surface metrology

The presence of unwanted asperities or debris in surface metrology data is a problem faced by many manufacturers of high volume precision components. The process of re-cleaning and re-measurement upon detection of an asperity can be extremely time consuming and costly. Current asperity removal methodologies are significantly limited due their fixed formats and to the user selection of thresholds. This paper presents an adaptive method of detection and an accompanying procedure for padding across the removal area. Results are presented for asperity removal in the measurement of roundness on fuel injector components.