Real-Time Monitoring and Fault Diagnostics in Roll-To-Roll Manufacturing Systems

A roll-to-roll (R2R) process is a manufacturing technique involving continuous processing of a flexible substrate as it is transferred between rotating rolls. It integrates many additive and subtractive processing techniques to produce rolls of product in an efficient and cost-effective way due to its high production rate and mass quantity. Therefore, the R2R processes have been increasingly implemented in a wide range of manufacturing industries, including traditional paper/fabric production, plastic and metal foil manufacturing, flexible electronics, thin film batteries, photovoltaics, graphene films production, etc. However, the increasing complexity of R2R processes and high demands on product quality have heightened the needs for effective real-time process monitoring and fault diagnosis in R2R manufacturing systems. This dissertation aims at developing tools to increase system visibility without additional sensors, in order to enhance real-time monitoring, and fault diagnosis capability in R2R manufacturing systems. First, a multistage modeling method is proposed for process monitoring and quality estimation in R2R processes. Product-centric and process-centric variation propagation are introduced to characterize variation propagation throughout the system. The multistage model mainly focuses on the formulation of process-centric variation propagation, which uniquely exists in R2R processes, and the corresponding product quality measurements with both physical knowledge and sensor data analysis. Second, a nonlinear analytical redundancy method is proposed for sensor validation to ensure the accuracy of sensor measurements for process and quality control. Parity relations based on nonlinear observation matrix are formulated to characterize system dynamics and sensor measurements. Robust optimization is designed to identify the coefficient of parity relations that can tolerate a certain level of measurement noise and system disturbances. The effect of the change of operating conditions on the value of the optimal objective function – parity residuals and the optimal design variables – parity coefficients are evaluated with sensitivity analysis. Finally, a multiple model approach for anomaly detection and fault diagnosis is introduced to improve the diagnosability under different operating regimes. The growing structure multiple model system (GSMMS) is employed, which utilizes Voronoi sets to automatically partition the entire operating space into smaller operating regimes. The local model identification problem is revised by formulating it into an optimization problem based on the loss minimization framework and solving with the mini-batch stochastic gradient descent method instead of least squares algorithms. This revision to the GSMMS method expands its capability to handle the local model identification problems that cannot be solved with a closed-form solution. The effectiveness of the models and methods are determined with testbed data from an R2R process. The results show that those proposed models and methods are effective tools to understand variation propagation in R2R processes and improve estimation accuracy of product quality by 70%, identify the health status of sensors promptly to guarantee data accuracy for modeling and decision making, and reduce false alarm rate and increase detection power under different operating conditions. Eventually, those tools developed in this thesis contribute to increase the visibility of R2R manufacturing systems, improve productivity and reduce product rejection rate.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146114/1/huanyis_1.pd

Advanced expander test bed program

The Advanced Expander Test Bed (AETB) is a key element in NASA's Chemical Transfer Propulsion Program for development and demonstration of expander cycle oxygen/hydrogen engine technology component technology for the next space engine. The AETB will be used to validate the high-pressure expander cycle concept, investigate system interactions, and conduct investigations of advanced missions focused components and new health monitoring techniques. The split-expander cycle AETB will operate at combustion chamber pressures up to 1200 psia with propellant flow rates equivalent to 20,000 lbf vacuum thrust

Monobore completion design: Classification, applications, benefits and limitations

A monobore completion is a simple completion design that uses the same internal diameter from the bottom of the well to surface. This may be accomplished by cementing a string of casing in a well, or by having tubing stabbed into a polished bore receptacle on a casing liner the same size as the tubing. Monobore completions have been applied extensively in oil and gas fields around the world, both onshore and offshore, from very low reservoir flow to extremely high production rates, since the late 1980s. They have proven beneficial due to their simplicity and cost savings. This study summarizes an extensive literature review of monobore completions and categorizes the monobore completions as slimhole, big bore or special function applications. This study also evaluates the well inflow impact of the 4 1/2-in. openhole multistage sleeve monobore completion employed in the North Kuwait Jurassic Gas field for HPHT wells compared to the previous completion using 3 1/2-in. tubing and 5 1/2-in. liners. The inflow evaluation was made for both volatile oil and gas condensate fluids found in this reservoir. Reservoir depletion was modeled to determine flowing life for the conventional completion versus the monobore design. The results of the modeling indicate production rate for the volatile oil case is the same in both completion designs, conventional and monobore, while in the gas condensate case the production rate is slightly higher for the monobore completion. As the monobore completion is larger, it reaches an unstable flow condition more quickly than the conventional design. However the multistage completion methodology allows all zones to be stimulated and contribute to flow, and can be equipped with a velocity string to sustain flow --Abstract, page iii

Self-resilient production systems : framework for design synthesis of multi-station assembly systems

Product design changes are inevitable in the current trend of time-based competition where product models such as automotive bodies and aircraft fuselages are frequently upgraded and cause assembly process design changes. In recent years, several studies in engineering change management and reconfigurable systems have been conducted to address the challenges of frequent product and process design changes. However, the results of these studies are limited in their applications due to shortcomings in three aspects which are: (i) They rely heavily on past records which might only be a few relevant cases and insufficient to perform a reliable analysis; (ii) They focus mainly on managing design changes in product architecture instead of both product and process architecture; and (iii) They consider design changes at a station-level instead of a multistation level. To address the aforementioned challenges, this thesis proposes three interrelated research areas to simulate the design adjustments of the existing process architecture. These research areas involve: (i) the methodologies to model the existing process architecture design in order to use the developed models as assembly response functions for assessing Key Performance Indices (KPIs); (ii) the KPIs to assess quality, cost, and design complexity of the existing process architecture design which are used when making decisions to change the existing process architecture design; and (iii) the methodology to change the process architecture design to new optimal design solutions at a multi-station level. In the first research area, the methodology in modeling the functional dependence of process variables within the process architecture design are presented as well as the relations from process variables and product architecture design. To understand the engineering change propagation chain among process variables within the process architecture design, a functional dependence model is introduced to represent the design dependency among process variables by cascading relationships from customer requirements, product architecture, process architecture, and design tasks to optimise process variable design. This model is used to estimate the level of process variable design change propagation in the existing process architecture design Next, process yield, cost, and complexity indices are introduced and used as KPIs in this thesis to measure product quality, cost in changing the current process design, and dependency of process variables (i.e, change propagation), respectively. The process yield and complexity indices are obtained by using the Stream-of-Variation (SOVA) model and functional dependence model, respectively. The costing KPI is obtained by determining the cost in optimizing tolerances of process variables. The implication of the costing KPI on the overall cost in changing process architecture design is also discussed. These three comprehensive indices are used to support decision-making when redesigning the existing process architecture. Finally, the framework driven by functional optimisation is proposed to adjust the existing process architecture to meet the engineering change requirements. The framework provides a platform to integrate and analyze several individual design synthesis tasks which are necessary to optimise the multi-stage assembly processes such as tolerance of process variables, fixture layouts, or part-to-part joints. The developed framework based on transversal of hypergraph and task connectivity matrix which lead to the optimal sequence of these design tasks. In order to enhance visibility on the dependencies and hierarchy of design tasks, Design Structure Matrix and Task Flow Chain are also adopted. Three scenarios of engineering changes in industrial automotive design are used to illustrate the application of the proposed redesign methodology. The thesis concludes that it is not necessary to optimise all functional designs of process variables to accommodate the engineering changes. The selection of only relevant functional designs is sufficient, but the design optimisation of the process variables has to be conducted at the system level with consideration of dependency between selected functional designs

Manufacturing variation models in multi-station machining systems

In product design and quality improvement fields, the development of reliable 3D machining variation models for multi-station machining processes is a key issue to estimate the resulting geometrical and dimensional quality of manufactured parts, generate robust process plans, eliminate downstream manufacturing problems, and reduce ramp-up times. In the literature, two main 3D machining variation models have been studied: the stream of variation model, oriented to product quality improvement (fault diagnosis, process planning evaluation and selection, etc.), and the model of the manufactured part, oriented to product and manufacturing design activities (manufacturing and product tolerance analysis and synthesis). This paper reviews the fundamentals of each model and describes step by step how to derive them using a simple case study. The paper analyzes both models and compares their main characteristics and applications. A discussion about the drawbacks and limitations of each model and some potential research lines in this field are also presented

Study of fuel systems for LH2-fueled subsonic transport aircraft, volume 1

Several engine concepts examined to determine a preferred design which most effectively exploits the characteristics of hydrogen fuel in aircraft tanks received major emphasis. Many candidate designs of tank structure and cryogenic insulation systems were evaluated. Designs of all major elements of the aircraft fuel system including pumps, lines, valves, regulators, and heat exchangers received attention. Selected designs of boost pumps to be mounted in the LH2 tanks, and of a high pressure pump to be mounted on the engine were defined. A final design of LH2-fueled transport aircraft was established which incorporates a preferred design of fuel system. That aircraft was then compared with a conventionally fueled counterpart designed to equivalent technology standards

Accounting for Uncertainty in Process Optimization Initiatives with Statistical Convolutions and Stochastic Programming Techniques

The purpose of this dissertation is to explore the uncertainty of process means and variances in order to improve processes with stochastic characteristics due to the complex nature of the underlying probability distributions. First, the gap between the existing conceptual notion of defects per million opportunities (DPMO) as part of process improvement initiatives and its applications to real-world engineering processes is explored. This is important because the current way of obtaining the DPMOs documented in the literature is problematic since it strictly assumes that there will be a shift in the process mean over time, while process variability remains unchanged. Accordingly, it does not account for shifting process standard deviation. This may not be the case in real-world practices. Several unique contributions to the Six Sigma body of knowledge are offered by expanding the existing DPMO and process fallout concepts, ultimately leading to process improvement. Second, convolutions of normal random variables are explored. Convolutions often arise in engineering problems, and the probability densities of the sums of these random variables are known in the literature. There are practical situations where specification limits on a process are imposed externally, and the product is typically scrapped if its performance does not fall in the specification range. The actual distribution after inspection is therefore truncated. Despite the practical importance of the role of truncated distributions, there has been little work on the theoretical foundation of convolutions associated with truncated random variables. This is paramount, since convolutions are often used as an important standard in statistical tolerance analysis. The convolutions of the combinations of truncated normal and truncated skew normal random variables on double and triple truncations are developed. This allows for a more accurate assessment of the mean and variance for a given process. Furthermore, it may not always be possible to define the specification limits and tolerances precisely within the limits of a probability density function for a process due to a relatively inaccurate or unstable process. This situation will be addressed through stochastic constrained programming

Optimum Allocation of Inspection Stations in Multistage Manufacturing Processes by Using Max-Min Ant System

In multistage manufacturing processes it is common to locate inspection stations after some or all of the processing workstations. The purpose of the inspection is to reduce the total manufacturing cost, resulted from unidentified defective items being processed unnecessarily through subsequent manufacturing operations. This total cost is the sum of the costs of production, inspection and failures (during production and after shipment). Introducing inspection stations into a serial multistage manufacturing process, although constituting an additional cost, is expected to be a profitable course of action. Specifically, at some positions the associated inspection costs will be recovered from the benefits realised through the detection of defective items, before wasting additional cost by continuing to process them. In this research, a novel general cost modelling for allocating a limited number of inspection stations in serial multistage manufacturing processes is formulated. In allocation of inspection station (AOIS) problem, as the number of workstations increases, the number of inspection station allocation possibilities increases exponentially. To identify the appropriate approach for the AOIS problem, different optimisation methods are investigated. The MAX-MIN Ant System (MMAS) algorithm is proposed as a novel approach to explore AOIS in serial multistage manufacturing processes. MMAS is an ant colony optimisation algorithm that was designed originally to begin an explorative search phase and, subsequently, to make a slow transition to the intensive exploitation of the best solutions found during the search, by allowing only one ant to update the pheromone trails. Two novel heuristics information for the MMAS algorithm are created. The heuristic information for the MMAS algorithm is exploited as a novel means to guide ants to build reasonably good solutions from the very beginning of the search. To improve the performance of the MMAS algorithm, six local search methods which are well-known and suitable for the AOIS problem are used. Selecting relevant parameter values for the MMAS algorithm can have a great impact on the algorithm’s performance. As a result, a method for tuning the most influential parameter values for the MMAS algorithm is developed. The contribution of this research is, for the first time, a methodology using MMAS to solve the AOIS problem in serial multistage manufacturing processes has been developed. The methodology takes into account the constraints on inspection resources, in terms of a limited number of inspection stations. As a result, the total manufacturing cost of a product can be reduced, while maintaining the quality of the product. Four numerical experiments are conducted to assess the MMAS algorithm for the AOIS problem. The performance of the MMAS algorithm is compared with a number of other methods this includes the complete enumeration method (CEM), rule of thumb, a pure random search algorithm, particle swarm optimisation, simulated annealing and genetic algorithm. The experimental results show that the effectiveness of the MMAS algorithm lies in its considerably shorter execution time and robustness. Further, in certain conditions results obtained by the MMAS algorithm are identical to the CEM. In addition, the results show that applying local search to the MMAS algorithm has significantly improved the performance of the algorithm. Also the results demonstrate that it is essential to use heuristic information with the MMAS algorithm for the AOIS problem, in order to obtain a high quality solution. It was found that the main parameters of MMAS include the pheromone trail intensity, heuristic information and evaporation of pheromone are less sensitive within the specified range as the number of workstations is significantly increased

Pyrolytic graphite collector development program

Pyrolytic graphite promises to have significant advantages as a material for multistage depressed collector electrodes. Among these advantages are lighter weight, improved mechanical stiffness under shock and vibration, reduced secondary electron back-streaming for higher efficiency, and reduced outgassing at higher operating temperatures. The essential properties of pyrolytic graphite and the necessary design criteria are discussed. This includes the study of suitable electrode geometries and methods of attachment to other metal and ceramic collector components consistent with typical electrical, thermal, and mechanical requirements

Third Yearly Activity Report

The calculation work performed during the 3rd project year in WP2 as well as the R&D activities carried out in WP3, WP4 and WP5 are described in this report. In addition, the work dedicated to the project management (WP1) as well as to WP6 regarding the dissemination/communication activities and the education/training program (e.g. the follow-up of the mobility program between different organizations in the consortium, training on simulation tools and activities accomplished by PhD/post-doctoral students) is also reported