A fiducial-aided data fusion method for the measurement of multiscale complex surfaces

Multiscale complex surfaces, possessing high form accuracy and geometric complexity, are widely used for various applications in fields such as telecommunications and biomedicines. Despite the development of multi-sensor technology, the stringent requirements of form accuracy and surface finish still present many challenges in their measurement and characterization. This paper presents a fiducial-aided data fusion method (FADFM), which attempts to address the challenge in modeling and fusion of the datasets from multiscale complex surfaces. The FADFM firstly makes use of fiducials, such as standard spheres, as reference data to form a fiducial-aided computer-aided design (FA-CAD) of the multiscale complex surface so that the established intrinsic surface feature can be used to carry out the surface registration. A scatter searching algorithm is employed to solve the nonlinear optimization problem, which attempts to find the global minimum of the transformation parameters in the transforming positions of the fiducials. Hence, a fused surface model is developed which takes into account both fitted surface residuals and fitted fiducial residuals based on Gaussian process modeling. The results of the simulation and measurement experiments show that the uncertainty of the proposed method was up to 3.97 × 10 −5 μm based on a surface with zero form error. In addition, there is a 72.5% decrease of the measurement uncertainty as compared with each individual sensor value and there is an improvement of more than 36.1% as compared with the Gaussian process-based data fusion technique in terms of root-mean-square (RMS) value. Moreover, the computation time of the fusion process is shortened by about 16.7%. The proposed method achieves final measuring results with better metrological quality than that obtained from each individual dataset, and it possesses the capability of reducing the measurement uncertainty and computational cost

Feature-based hybrid inspection planning for complex mechanical parts

Globalization and emerging new powers in the manufacturing world are among many challenges, major manufacturing enterprises are facing. This resulted in increased alternatives to satisfy customers\u27 growing needs regarding products\u27 aesthetic and functional requirements. Complexity of part design and engineering specifications to satisfy such needs often require a better use of advanced and more accurate tools to achieve good quality. Inspection is a crucial manufacturing function that should be further improved to cope with such challenges. Intelligent planning for inspection of parts with complex geometric shapes and free form surfaces using contact or non-contact devices is still a major challenge. Research in segmentation and localization techniques should also enable inspection systems to utilize modern measurement technologies capable of collecting huge number of measured points. Advanced digitization tools can be classified as contact or non-contact sensors. The purpose of this thesis is to develop a hybrid inspection planning system that benefits from the advantages of both techniques. Moreover, the minimization of deviation of measured part from the original CAD model is not the only characteristic that should be considered when implementing the localization process in order to accept or reject the part; geometric tolerances must also be considered. A segmentation technique that deals directly with the individual points is a necessary step in the developed inspection system, where the output is the actual measured points, not a tessellated model as commonly implemented by current segmentation tools. The contribution of this work is three folds. First, a knowledge-based system was developed for selecting the most suitable sensor using an inspection-specific features taxonomy in form of a 3D Matrix where each cell includes the corresponding knowledge rules and generate inspection tasks. A Travel Salesperson Problem (TSP) has been applied for sequencing these hybrid inspection tasks. A novel region-based segmentation algorithm was developed which deals directly with the measured point cloud and generates sub-point clouds, each of which represents a feature to be inspected and includes the original measured points. Finally, a new tolerance-based localization algorithm was developed to verify the functional requirements and was applied and tested using form tolerance specifications. This research enhances the existing inspection planning systems for complex mechanical parts with a hybrid inspection planning model. The main benefits of the developed segmentation and tolerance-based localization algorithms are the improvement of inspection decisions in order not to reject good parts that would have otherwise been rejected due to misleading results from currently available localization techniques. The better and more accurate inspection decisions achieved will lead to less scrap, which, in turn, will reduce the product cost and improve the company potential in the market

Improvement of Geometric Quality Inspection and Process Efficiency in Additive Manufacturing

Additive manufacturing (AM) has been known for its ability of producing complex geometries in flexible production environments. In recent decades, it has attracted increasing attention and interest of different industrial sectors. However, there are still some technical challenges hindering the wide application of AM. One major barrier is the limited dimensional accuracy of AM produced parts, especially for industrial sectors such as aerospace and biomedical engineering, where high geometric accuracy is required. Nevertheless, traditional quality inspection techniques might not perform well due to the complexity and flexibility of AM fabricated parts. Another issue, which is brought up from the growing demand for large-scale 3D printing in these industry sectors, is the limited fabrication speed of AM processes. However, how to improve the fabrication efficiency without sacrificing the geometric quality is still a challenging problem that has not been well addressed. In this work, new geometric inspection methods are proposed for both offline and online inspection paradigms, and a layer-by-layer toolpath optimization model is proposed to further improve the fabrication efficiency of AM processes without degrading the resolution. First, a novel Location-Orientation-Shape (LOS) distribution derived from 3D scanning output is proposed to improve the offline inspection in detecting and distinguishing positional and dimensional non-conformities of features. Second, the online geometric inspection is improved by a multi-resolution alignment and inspection framework based on wavelet decomposition and design of experiments (DOE). The new framework is able to improve the alignment accuracy and to distinguish different sources of error based on the shape deviation of each layer. In addition, a quickest change point detection method is used to identify the layer where the earliest change of systematic deviation distribution occurs during the printing process. Third, to further improve the printing efficiency without sacrificing the quality of each layer, a toolpath allocation and scheduling optimization model is proposed based on a concurrent AM process that allows multiple extruders to work collaboratively on the same layer. For each perspective of improvements, numerical studies are provided to emphasize the theoretical and practical meanings of proposed methodologies

A Three-dimensional Deviation Analysis by the Coordinate Registration of Randomly Positioned Objects

Department of Mechanical EngineeringIt is very important to accurately inspect machining errors, assembly tolerances of product in manufacturing industry. Recently, a three-dimensional measurement system is widely used for industrial inspection. Typical three-dimensional measurement methods include a coordinate measuring machine (CMM), a line laser scanning method, and a structured light system comprising a camera and light source for generating a pattern. In general, the inspection system applying the three-dimensional measurement method require the physical calibration processing using special device to place object at home position with desired pose. However, such a process requires a considerable time for measurement, and it inhibits the flexibility of measurement spatially. Therefore, to solve this problem, this thesis proposed a methodology to measurement of randomly positioned objects by coordinate recognition. It is assumed that the position and pose of object is varied at every measurement. Coordinate of CAD model must be brought to the coordinate of measured data to calculate deviation of object. Transformation parameters of two coordinates are derived by following procedure. reference plane selection is preceded before measurement as preprocessing. The first step is rough registration based on principal component analysis and iterative closest point algorithm. The second step is main methodology of this thesis, which is coordinate adjustment to calibrate transformation parameters. Coordinate adjustment is composed of two stages, which are reference plane matching for calibrating rotation parameters and edge matching for translation parameters. Then, deviation is calculated by comparison to the CAD model.ope

Posture adjustment of workpiece based on stepwise matching by self-adaptive differential evolution algorithm

The workpiece contour errors from previous process affect current and subsequent process. In order to improve the uniformity of workpiece allowance distribution, a stepwise workpiece matching adjustment method with contact inspection is developed. This method includes two stepwise registration processes. At first, some pairs of measured points on the theoretical and actual surfaces are selected to build the corresponding local coordinate systems, and then the rough matching matrixes are obtained by the coordinate systems alignment. During the fine matching process, the objective function based on the least square method is established by the measured point sets adjusted through the rough matching. The fine matching matrixes can be obtained by self-adaptive differential evolution algorithm. The posture adjustment can be realized by transforming coordinate systems of 5 axis machine tool, of which adjusted values can be calculated by the matching matrixes and the machine tool topology. At last, some experiments were presented to demonstrate the performance of the method.</p

High-Throughput Tensile Testing Reveals Stochastic Properties in Additively Manufactured Steel

An adage within the Additive Manufacturing (AM) community is that “complexity is free”. Complicated geometric features that normally drive manufacturing cost and limit design options are not typically problematic in AM. While geometric complexity is usually viewed from the perspective of part design, this advantage of AM also opens up new options in rapid, efficient material property evaluation and qualification. This Thesis demonstrates how 100’s of miniature tensile bars can be produced and tested for comparable cost and in comparable time to a few conventional tensile bars. With this technique, it is possible to evaluate the stochastic nature of mechanical behavior and capture the statistical nature of mechanical properties. As a proof of concept, the technique is demonstrated on a precipitation hardened stainless steel alloy, commonly known as 17-4PH, produced by two commercial AM vendors using a laser powder bed fusion process, also commonly known as selective laser melting. Using two different commercial powder bed platforms, the vendors produced material that exhibited slightly lower strength and markedly lower ductility compared to wrought sheet. After demonstrating vendor to vendor variability, one vendor was chosen to produce 1000’s of tensile samples to explore within-build and between-build variability. Such a large dataset is seldom available in conventional materials evaluation and revealed rare defects that were only present in ~2% of the population. Worst-case failures were associated with residual porosity. To address the deleterious effect of porosity, the study examined a hot isostatic pressing process that collapsed a vast majority of the internal voids. Lastly, hardness testing which is an alternative high-throughput material evaluation technique was used to make a comparison between strength values obtained by tensile tests to those approximated by hardness testing. It is shown that hardness testing can be an appropriate technique for estimating the strength of wrought 17-4PH, but has a non-conservative error in strength estimations for AM 17-4PH

Augmented manual fabrication methods for 2D tool positioning and 3D sculpting

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 67-75).Augmented manual fabrication involves using digital technology to assist a user engaged in a manual fabrication task. Methods in this space aim to combine the abilities of a human operator, such as motion planning and large-range mechanical manipulation, with technological capabilities that compensate for the operator's areas of weakness, such as precise 3D sensing, manipulation of complex shape data, and millimeter-scale actuation. This thesis presents two new augmented manual fabrication methods. The first is a method for helping a sculptor create an object that precisely matches the shape of a digital 3D model. In this approach, a projector-camera pair is used to scan a sculpture in progress, and the resulting scan data is compared to the target 3D model. The system then computes the changes necessary to bring the physical sculpture closer to the target 3D shape, and projects guidance directly onto the sculpture that indicates where and how the sculpture should be changed, such as by adding or removing material. We describe multiple types of guidance that can be used to direct the sculptor, as well as several related applications of this technique. The second method described in this thesis is a means of precisely positioning a handheld tool on a sheet of material using a hybrid digital-manual approach. An operator is responsible for manually moving a frame containing the tool to the approximate neighborhood of the desired position. The device then detects the frame's position and uses digitally-controlled actuators to move the tool within the frame to the exact target position. By doing this in a real time feedback loop, a tool can be smoothly moved along a digitally-specified 2D path, allowing many types of digital fabrication over an unlimited range using an inexpensive handheld tool.by Alec Rivers.Ph.D