Data-Based Spatial and Temporal Modeling for Surface Variation Monitoring in Manufacturing.

Spatiotemporal processes exist widely in manufacturing, such as tool surface degradation in ultrasonic metal welding and surface shape progression in high-precision machining. High-resolution characterization and monitoring of spatiotemporal processes are crucial for manufacturing process control. The rapid development of 3D sensing technologies has made it possible to generate large volumes of spatiotemporal data for process characterization and monitoring. However, critical challenges exist in effectively acquiring and utilizing such spatiotemporal data in manufacturing, e.g., a high cost in the acquisition of high-resolution spatiotemporal data and a lack of systematic approaches for modeling multi-source data and monitoring spatiotemporal processes. To address these challenges, this dissertation carries out three research tasks for the development of collecting, modeling and monitoring spatiotemporal data. Specifically, (1) A novel dynamic sampling design algorithm is developed to efficiently characterize spatiotemporal processes in manufacturing. A state-space model and Kalman filter are used to predictively determine the measurement locations using a criterion considering both the prediction variance and the measurement costs. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the temporal transition parameters in the spatiotemporal model. (2) A new surface modeling approach is devised to cost-effectively assess spatial surface variations by integrating an engineering model with multi-task Gaussian process (GP) learning. Surface variation is decomposed into a global trend which is induced by process variables and a zero-mean GP which shares commonality among multiple similar-but-not-identical processes. An iterative algorithm is developed to simultaneously estimate the process-specific parameters and the GP parameters. (3) A tool condition characterization and monitoring framework is developed for ultrasonic metal welding in lithium-ion battery manufacturing. The geometric progression of the tool surfaces is characterized using high-resolution spatiotemporal data. Classification algorithms are developed with monitoring features extracted from both the space and frequency domains. A novel impression measurement method is designed to effectively measure tool surfaces without the need of disassembling tools for off-line measurement.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120743/1/chshao_1.pd

Modeling the Impact of Process Variation on Resistive Bridge Defects

Recent research has shown that tests generated without taking process variation into account may lead to loss of test quality. At present there is no efficient device-level modeling technique that models the effect of process variation on resistive bridges. This paper presents a fast and accurate technique to model the effect of process variation on resistive bridge defects. The proposed model is implemented in two stages: firstly, it employs an accurate transistor model (BSIM4) to calculate the critical resistance of a bridge; secondly, the effect of process variation is incorporated in this model by using three transistor parameters: gate length (L), threshold voltage (V) and effective mobility (ueff) where each follow Gaussian distribution. Experiments are conducted on a 65-nm gate library (for illustration purposes), and results show that on average the proposed modeling technique is more than 7 times faster and in the worst case, error in bridge critical resistance is 0.8% when compared with HSPICE

Simulation of multi-axis machining processes using z-mapping technique

Parameter selection in machining operations is curial for product quality and high productivity. Process parameters such as feed, spindle speed and depth of cuts are often chosen by trial-error methods. Mathematical models can be employed to predict the mechanics and the dynamics of the process. In this study, Z-mapping technique is utilized to simulate the process step by step by updating the workpiece according the given tool path where the cutter engagement areas are also determined. Using the numerical generalized process model, whole process is simulated for any milling tool geometry including intricate profiling tools, serrated cutters and tools with variable edge geometries

Analytical and experimental study of feed rate in high-speed milling

In the context of high-speed milling (HSM), during the machining process dynamic machine response has to be identified. To achieve this, we have to calculate the feed rate evolution in linear and circular interpolation according to dynamic performance of machine. In addition to that, actual trajectory for transition passages between two interpolations must be estimated with take into account of specific machining tolerances. This article proposes a model of machine tool behavior for a tool path with linear and circular interpolations and machining cycle time prediction. The method involves subdividing the trajectories into elementary geometries according to the type of interpolation (circular or linear). At points where different trajectories meet, there is often a discontinuity in curvature or in tangency, which decreases the feed rate. At the points of discontinuity in tangency, a fillet radius is inserted. In this article, the influence of the geometry for elementary trajectories was determined. Then, the value of the fillet radius between linear and circular contours in different combinations was modeled. An industrial application was carried out in order to validate models and to determine the influence of feed rate evolution on the machining cycle time

Numerical and experimental analyses of resin infusion manufacturing processes of composite materials

Liquid resin infusion (LRI) processes are promising manufacturing routes to produce large, thick, or complex structural parts. They are based on the resin flow induced, across its thickness, by a pressure applied onto a preform/resin stacking. However, both thickness and fiber volume fraction of the final piece are not well controlled since they result from complex mechanisms which drive the transient mechanical equilibrium leading to the final geometrical configuration. In order to optimize both design and manufacturing parameters, but also to monitor the LRI process, an isothermal numerical model has been developed which describes the mechanical interaction between the deformations of the porous medium and the resin flow during infusion.1, 2 With this numerical model, it is possible to investigate the LRI process of classical industrial part shapes. To validate the numerical model, first in 2D, and to improve the knowledge of the LRI process, this study details a comparison between numerical simulations and an experimental study of a plate infusion test carried out by LRI process under industrial conditions. From the numerical prediction, the filling time, the resin mass and the thickness of the preform can be determined. On another hand, the resin flow and the preform response can be monitored by experimental methods during the filling stage. One key issue of this research study is to highlight the changes in major process parameters during the resin infusion stage, such as the temperature of the preform and resin, and the variations of both thickness and fiber volume fraction of the preform. Moreover, this numerical/experimental approach is the best way to improve our knowledge on the resin infusion processes, and finally, to develop simulation tools for the design of advanced composite parts

Effect of Ductile Damage Evolution in Sheet Metal Forming: Experimental and Numerical Investigations

The numerical simulation based on the Finite Element Method (FEM) is widely used in academic institutes and in the industry. It is a useful tool to predict many phenomena present in the classical manufacturing forming processes such as necking, fracture, springback, buckling and wrinkling. But, the results of such numerical model depend strongly on the parameters of the constitutive behavior model. In the first part of this work, we focus on the traditional identification of the constitutive law using oriented tensile tests (0°, 45°, and 90° with respect to the rolling direction). A Digital Image Correlation (DIC) method is used in order to measure the displacements on the surface of the specimen and to analyze the necking evolution and the instability along the shear band. Therefore, bulge tests involving a number of die shapes (circular and elliptic) were developed. In a second step, a mixed numerical–experimental method is used for the identification of the plastic behavior of the stainless steel metal sheet. The initial parameters of the inverse identification were extracted from a uniaxial tensile test. The optimization procedure uses a combination of a Monte-Carlo and a Levenberg-Marquardt algorithm. In the second part of this work, according to some results obtained by SEM (Scaning Electron Microscopy) of the crack zones on the tensile specimens, a Gurson Tvergaard Needleman (GTN) ductile model of damage has been selected for the numerical simulations. This model was introduced in order to give informations concerning crack initiations during hydroforming. At the end of the paper, experimental and numerical comparisons of sheet metal forming applications are presented and validate the proposed approach

Simulation of broaching operations for tool design optimization

In this study, an approach for simulation of broaching operations is presented. The proposed approach calculates the cutting forces, power, maximum stress at the tooth root, chip thickness and rake angle distribution on all teeth, and the workpiece geometry evolution during the process. The developed algorithms are integrated in a computer software that can simulate a whole broaching process to predict various physical parameters and generate some performance measures. The model which is used to optimize the broaching process is briefly presented here together with the industrial applications

Treatment of food processing industrial wastewater using two stages anaerobic system

The wastewater produced by food manufacturing industry is known for its high concentration of COD and suspended solid. In wastewater treatment, anaerobic process is favorable due to its low cost, biogas production, low sludge production and more. In this study, upflow anaerobic sludge bed (UASB) and hybrid-UASB (HUASB) reactors, were combined with anaerobic filter (AF) bioreactors forming two stages system to treat food processing industry wastewater. This study was focused on the performance of UASB-AF (R1) and HUASB-AF (R2) treatment systems and the granules development. Seed sludge was deposited into HUASB column up to a third of the height. Palm oil shells were then packed into the HUASB (above seed sludge) as well as AF reactors to promote growth of microorganisms. The R1 and R2 systems were operated simultaneously, fed with raw food manufacturing wastewater taken from Azhar Food Manufacturing Factory. Parameters measured to evaluate the performance of the process were pH, COD, NH3-N, oil and grease and total phosphorus. The highest average COD removal efficiency, at 99%, were detected in R1 and R2 systems, both at OLR 10.56 g COD/L.d. Moreover, the presence of aggregated bio particles with diameter ranges from 2.934 to 5.00 mm were observed in both UASB and HUASB reactors. The highest percentage of 2.934 to 5.00 mm diameter granules were 7.6 % and 10.7% in the UASB and HUASB respectively. In addition, the highest removal rate coefficient, k values for UASB and HUASB were 2.1981 and 3.3950, occurred at OLR 8.59 and 10.56 g COD/L.d, respectively. Overall, the k values have proved that HUASB reactor had performed better than UASB reactor