High Definition Metrology based Process Control: Measurement System Analysis and Process Monitoring.

Process control in high precision machining necessitates high-definition metrology (HDM) systems that provide fine resolution data needed to characterize surface shape. HDM data is critical for the evaluation of process surface variation, as it reveals local surface patterns that are undetectable using low definition metrology (LDM) systems. Monitoring of the part-to-part variation of these patterns identified by HDM enables the detection of abnormal surface variation and the degradation of process conditions. HDM systems present many opportunities for surface variation reduction. However, there are challenges to using HDM data for process control. Conventional HDM systems are inefficient and may take a long time to measure a part, such that sufficient samples cannot be obtained for process control purposes. In addition, conventional monitoring methods are difficult to implement due to the high density of data. A new study uncovered significant cross-correlations between part surface height and process variables in an automotive engine milling process. This dissertation aims to apply new insights gained from HDM to develop algorithms and methods for surface variation control, specifically: - Surface modeling through fusion of process variables and HDM data: An improved surface model is developed by incorporating process and multi-resolution data through spatial and cross-correlation to increase prediction accuracy and reduce the amount of HDM measurements necessary for process control. - Measurement system analysis for HDM using: A method to effectively estimate the gage capability for HDM systems is proposed. - Surface variation monitoring using HDM data: A sequential monitoring framework is developed to monitor surface variations as reflected by HDM data. Based on the surface data-process fusion model, a progressive monitoring algorithm under a Bayesian framework is developed to monitor surface variations when limited HDM measurements are available. - Multistage modeling and monitoring of HDM Data: A morphing-based approach is proposed to model process multistage interdependence. A new multistage monitoring procedure is developed based on the morphing model. The research presented in this dissertation will aid in transforming quality control practices from dimensional variation reduction to surface shape variation control. The proposed HDM data monitoring algorithms can be extended to other high precision manufacturing processes.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99874/1/ssuriano_1.pd

Functional Morphing for Manufacturing Process Design, Evaluation and Control.

Shape changes are commonly identified in product development and manufacturing. These changes include part shape changes in a product family from one generation to another, surface geometric changes due to manufacturing operations, etc. Morphing is one method to mathematically model these shape changes. However, conventional morphing focuses only on geometric change without consideration of process mechanics/physics. It thus has limitations in representing a complex physical process involved in product development and manufacturing. This dissertation proposes a functional morphing methodology which integrates physical properties and feasibilities into geometric morphing to describe complex manufacturing processes and applies it to manufacturing process design, evaluation, and control. Three research topics are conducted in this dissertation in areas of manufacturing process design, evaluation and control. These are: • Development of evolutionary stamping die face morphing: Similarities which are identified among parts of the same product family allow the possibilities for the knowledge learned from the die design of one generation of sheet metal product to be morphed onto that of a new but similar product. A new concept for evolutionary die design is proposed using a functional morphing algorithm. Case studies show that the proposed method is able to capture the added features in the new part design as well as the springback compensation inherited from the existing die face. • Formability assessment in die face morphing: A strain increment method is proposed for early formability assessment by predicting strain distribution directly from the part-to-part mapping process based on the functional morphing algorithm. Since this method does not require the knowledge on the new die surface, such formability assessment can serve as an early manufacturing feasibility analysis on the new part design. • Functional morphing in monitoring and control of multi-stage manufacturing processes: A functional free form deformation (FFD) approach is developed to extract mapping functions between manufacturing stages. The obtained mapping functions enable multi-scale variation propagation analysis and intermediate-stage process monitoring. It also allows for accurate inter-stage adjustment that introduces shape deformation upstream to compensate for the errors downstream.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75924/1/zhl_1.pd

Shape variation modelling, analysis and statistical control for assembly system with compliant parts

Modern competitive market demands frequent change in product variety, increased production volume and shorten product/process change over time. These market requirements point towards development of key enabling technologies (KETs) to shorten product and process development cycle, improved production quality and reduced time-to-launch. One of the critical prerequisite to develop the aforementioned KETs is efficient and accurate modelling of product and process dimensional errors. It is especially critical for assembly processes with compliant parts as used in automotive body, appliance or wing and fuselage assemblies. Currently, the assembly process is designed under the assumption of ideal (nominal) products and then check by using variation simulation analysis (VSA). However, the VSA simulations are oversimplified as they are unable to accurately model or predict the effects of geometric and dimensional variations of compliant parts, as well as variations of key characteristics related to fixturing and joining process. This results in product failures and/or reduced quality due to un-modelled interactions in assembly process. Therefore, modelling and prediction of the geometric shape errors of complex sheet metal parts are of tremendous importance for many industrial applications. Further, as production yield and product quality are determined for production volume of real parts, thus not only shape errors but also shape variation model is required for robust assembly system development. Currently, parts shape variation can be measured during production by using recently introduced non-contact gauges which are fast, in-line and can capture entire part surface information. However, current applications of non-contact scanners are limited to single part inspection or reverse engineering applications and cannot be used for monitoring and statistical process control of shape variation. Further, the product shape variation can be reduced through appropriate assembly fixture design. Current approaches for assembly fixture design seldom consider shape variation of production parts during assembly process which result in poor quality and yield. To address the aforementioned challenges, this thesis proposes the following two enablers focused on modelling of shape errors and shape variation of compliant parts applicable during assembly process design phase as well as production phase: (i) modelling and characterisation of shape errors of individual compliant part with capabilities to quantify fabrication errors at part level; and (ii) modelling and characterisation of shape variation of a batch of compliant parts with capabilities to quantify the shape variation at production level. The first enabler focuses on shape errors modelling and characterisation which includes developing a functional data analysis model for identification and characterisation of real part shape errors that can link design (CAD model) with manufacturing (shape errors). A new functional data analysis model, named Geometric Modal Analysis (GMA), is proposed to extract dominant shape error xixmodes from the fabricated part measurement data. This model is used to decompose shape errors of 3D sheet metal part into orthogonal shape error modes which can be used for product and process interactions. Further, the enabler can be used for statistical process control to monitor shape quality; fabrication process mapping and diagnosis; geometric dimensioning and tolerancing simulation with free form shape errors; or compact storage of shape information. The second enabler aims to model and characterise shape variation of a batch of compliant parts by extending the GMA approach. The developed functional model called Statistical Geometric Modal Analysis (SGMA) represents the statistical shape variation through modal characteristics and quantifies shape variation of a batch of sheet metal parts a single or a few composite parts. The composite part(s) represent major error modes induced by the production process. The SGMA model, further, can be utilised for assembly fixture optimisation, tolerance analysis and synthesis. Further, these two enablers can be applied for monitoring and reduction of shape variation from assembly process by developing: (a) efficient statistical process control technique (based on enabler ‘i’) to monitor part shape variation utilising the surface information captured using non-contact scanners; and (b) efficient assembly fixture layout optimisation technique (based on enabler ‘ii’) to obtain improved quality products considering shape variation of production parts. Therefore, this thesis proposes the following two applications: The first application focuses on statistical process control of part shape variation using surface data captured by in-process or off-line scanners as Cloud-of-Points (CoPs). The methodology involves obtaining reduced set of statistically uncorrelated and independent variables from CoPs (utilising GMA method) which are then used to develop integrated single bivariate T2-Q monitoring chart. The joint probability density estimation using non-parametric Kernel Density Estimator (KDE) has enhanced sensitivity to detect part shape variation. The control chart helps speedy detection of part shape errors including global or local shape defects. The second application determines optimal fixture layout considering production batch of compliant sheet metal parts. Fixtures control the position and orientation of parts in an assembly process and thus significantly contribute to process capability that determines production yield and product quality. A new approach is proposed to improve the probability of joining feasibility index by determining an N-2-1 fixture layout optimised for a production batch. The SGMA method has been utilised for fixture layout optimisation considering a batch of compliant sheet metal parts. All the above developed methodologies have been validated and verified with industrial case studies of automotive sheet metal door assembly process. Further, they are compared with state-of-the-art methodologies to highlight the boarder impact of the research work to meet the increasing market requirements such as improved in-line quality and increased productivity

Uncertainty quantification Of performance and stability of high-speed axial compressors

Geometrical uncertainties in a compressor (due to manufacturing tolerance and/or in-service degradation) often result in flow asymmetry around the annulus of a compressor that jeopardises compressor stability and performance. Usually, sensitivity of compressor stability and performance for any parametric variation is arrived at by considering all blades to have same dimension. In reality, an inherent blade-to-blade variation causes the blades to have a probability distribution. These blades can be redistributed circumferentially resulting in adjacent passage areas between different blades to be completely random and hence the performance variation. Surrogate model is preferred for quantifying the effects of parametric variation on compressor stability and performance given its quick turnaround time vis-a-vis CFD and experiments. In this thesis, uncertainties for three test cases were considered: each representative of fans on military aircraft engines, fans on civil aircraft engines and a 1-stage transonic compressor used in industrial gas turbine. This research establishes a rule of thumb to arrange blades of differing dimensions around the compressor to eke out maximum performance and stability margin. The parameters tip gap and stagger angle represent manufacturing tolerance while in-service degradation was represented by leading edge damage. For both random tip gap variation (0.15% to 0.94% span) and random leading edge damage (4% to 18% chord), the compressor performance and stability boundaries were found to be best with a zigzag pattern of blade arrangement and worst with a sinusoidal pattern of arrangement. The converse was found to be true for blades having random stagger angle variation (± 2.25% change in nominal stagger angle). The best/worst arrangement of blades with differing dimensions was ascertained using a mix of CFD and travelling salesman (TSP) analogy. The TSP analogy is handy for determining the best arrangement when two or more parameters vary simultaneously. Generalised surrogate model was developed to accurately predict the performance of compressors undergoing random tip gap and stagger angle variation. Due to its robustness, the surrogate model was combined with Monte Carlo technique to gauge the impact of parametric variation on quantities of interest (QoI). The mean absolute percentage error between CFD and surrogate models of stagger angle and tip gap (for different QoI) were found to be less than 0.14% and 1.5% respectively. This de novo analysis considers only the aerodynamic effect from geometric variations while neglecting the associated aeroelastic effects. Detailed analyses based on past experience and physical reasoning were used to validate the numerical simulations.Open Acces

A Simplified Phase Display System for 3D Surface Measurement and Abnormal Surface Pattern Detection

Today’s engineering products demand increasingly strict tolerances. The shape of a machined surface plays a critical role to the desired functionality of a product. Even a small error can be the difference between a successful product launch and a major delay. It is important to develop tools that confirm the quality and accuracy of manufactured products. The key to assessing the quality is robust measurement and inspection tools combined with advanced analysis. This research is motivated by the goals of 1) developing an advanced optical metrology system that provides accurate 3D profiles of target objects with curvature and irregular texture and 2) developing algorithms that can recognize and extract meaningful surface features with the consideration of machining process information. A new low cost measurement system with a simple coherent interferometric fringe projection system is developed. Comparing with existing optical measurement systems, the developed system generates fringe patterns on object surface through a pair of fiber optics that have a relatively simple and flexible configuration. Three-dimensional measurements of a variety of surfaces with curvatures demonstrate the applicability and flexibility of the developed system. An improved phase unwrapping algorithm based on a flood fill method is developed to enhance the performance of image processing. The developed algorithm performs phase unwrapping under the guidance of a hybrid quality map that is generated by considering the quality of both acquired original intensity images and the calculated wrapped phase map. Advances in metrology systems enable engineers to obtain a large amount of surface information. A systematic framework for surface shape characterization and abnormal pattern detection is proposed to take the advantage of the availability of high definition surface measurements through advanced metrology systems. The proposed framework evaluates a measured surface in two stages. The first step focuses on the extraction of general shape (e.g., surface form) from measurement for surface functionality evaluation and process monitoring. The second step focuses on the extraction of application specific surface details with the consideration of process information (e.g., surface waviness). Applications of automatic abnormal surface pattern detection have been demonstrated. In summary, this research focuses on two core areas: 1) developing metrology system that is capable of measuring engineered surfaces accurately; 2) proposing a methodology that can extract meaningful information from high definition measurements with consideration of process information and product functionality.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136999/1/xinweng_1.pd

非線形弾性要素による内部力補償に基づく無段階変位–力変換機構の創生 ― 微小操作力で強大な磁気力・把持力・制動力・張力を制御可能とするロボット要素 ―

Tohoku University博士（情報科学）thesi

NASA Tech Briefs, August 2002

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