This report evaluates some of the challenges faced with 2D camera based on-machine metrology and potential options with using 3D sensors for such Direct Write applications. Specifically, in order to fully exploit 3D direct write technology to surfaces in excess of 45 degree to the print direction, non-planar motion employing 4th and 5th rotary axes are often necessary. This report will outline a procedure for doing high accuracy rotary axis calibration. Furthermore, the use of online metrology solution to enable tuning of the rotary axis as well as for online print characterization will be detailed. These efforts will provide a fresh impetus to the use of 3D sensors for on-machine monitoring applications in additive manufacturing.
The use of multiple axis machines for direct write additive manufacturing offers the potential for the custom manufacture of a wide range of useful structures ranging from sensor on curved parts to electronics on unique shapes. The motion axis for such systems do not need the force and robustness of metal cutting applications, but the geometric requirements for electronic patterns means the machines need to compensate for a wide range of errors at micron levels. The types of errors of concern may include motion axis alignments in 5-axis and more specific printing tool or fixture variations. In order to correct for any such errors, there is a need for a mapping method that can be applied across machine platforms without the need for substantial modifications to the machines. This paper will describe the selection and application of well defined, readily available basic geometric artifacts that permit the separate mapping of key error sources in such a system. The ability to provide the error measurement using simple sensors commonly available will be contrasted to more expensive, traditional volumetric mapping methods.
Gaps are important in a wide range of measurements in manufacturing, from the fitting of critical assemblies too cosmetic features on cars. There are a variety of potential sensors that can measure a gap opening, each with aspects of gap measurements that they do well and other aspects where the technology may lack capability. This paper provides a review of a wide range of optical gages from structured light to passive systems and from line to area measurement. Each technology is considered relative to the ability to accurately measure a gap, including issues of edge effects, edge shape, surface finish, and transparency. Finally, an approach will be presented for creating an optimize measurement off gap openings for critical assembly applications.
Many sheet products from plastic to structural composites are produce in tightly controlled thickness needed for functional applications. There are many methods that have been used to measure such sheeting from mechanical rollers to optical micrometers. However, many materials are produced with a thin protective film on either side that may not have critical dimensional controls. This paper addresses the challenge of measuring sheet products to critical thickness values in the presence of protective plastic films using high speed optical gaging methods. For this application, the protective films are assumed to be transparent though not necessarily scatter free, and have thickness variations that are comparable to the tolerances of the sheet product. We will examine the pros and cons of a number of different optical measurement methods in light of resolution, speed and robustness to the film thickness variation and present an approach able to address the desired sheet measurement tolerances.
Structured light methods are used by many commercial products on the market today. Many such systems using white light projectors while many line gages use standard red laser diodes. However, in recent years there has been much claimed about using blue light, polarized light and partially coherent systems to obtain better performance. Unlike interferometers, moving from red to blue light for a system using only geometric shape information does not gain an automatic advantage from the shorter wavelength. The sensitivity metric does not have a wavelength component to it. But there are other factors that can improve gage performance. The ability to measure some feature is also a function of other parameters such as signal to noise ratio, reflectivity variations, and depth-of-field over which a clear pattern can be seen. This paper will explore the theoretical and experimental data relating to what works and what can be expected from variations on the old methods.
Optical measurement techniques are often employed to digitally capture three dimensional shapes of components. The digital data density output from these probes range from a few discrete points to exceeding millions of points in the point cloud. The point cloud taken as a whole represents a discretized measurement of the actual 3D shape of the surface of the component inspected to the measurement resolution of the sensor. Embedded within the measurement are the various features of the part that make up its overall shape. Part designers are often interested in the feature information since those relate directly to part function and to the analytical models used to develop the part design. Furthermore, tolerances are added to these dimensional features, making their extraction a requirement for the manufacturing quality plan of the product. The task of “extracting” these design features from the point cloud is a post processing task. Due to measurement repeatability and cycle time requirements often automated feature extraction from measurement data is required. The presence of non-ideal features such as high frequency optical noise and surface roughness can significantly complicate this feature extraction process. This research describes a robust process for extracting linear and arc segments from general 2D point clouds, to a prescribed tolerance. The feature extraction process generates the topology, specifically the number of linear and arc segments, and the geometry equations of the linear and arc segments automatically from the input 2D point clouds. This general feature extraction methodology has been employed as an integral part of the automated post processing algorithms of 3D data of fine features.
It is very difficult to measure the inner profile geometry of small holes of less than a millimeter in size, yet that geometry may be important for some manufacturing operations. This paper will present a method to measure key dimensional parameters of small holes used in a variety of applications from cooling to lubrication. Precision shaped holes can consist of a hole at some angle to the surface of the part and an area around the entrance to the hole for the purpose of diffusing the air or lubricant across the surface of the part to achieve the most effective performance. The drive towards smaller and more complex hole geometries means that previous methods such as conventional touch probes do not provide a good mapping in a time that can be used as part of production. The advanced designs of the holes means simple pin gages do not provide enough information. This paper will discuss tests of various methods considered for mapping small hole inner diameters, and present some sample results of a possible solution.
We describe a new algorithm for 3D edge detection on composite part surfaces based upon phase shift analysis. Current phase shift based algorithms generate 3D surface profiles, they do not directly compute 3D edge information. The proposed algorithm has been developed in this context for 3D edge detection. One advantage of this method is its ability to measure smooth 3D edges that cannot be accurately measured using traditional contact techniques. A dense 3D point cloud representing part edges are computed, all such edges in view may be computed simultaneously. The inherent accuracy available with phase shift analysis is leveraged for detecting the smooth edges with minimal error. Experimental results with some test parts are presented.