Reliability Assessment of CNC Machining Center Based on Weibull Neural Network

CNC machining centers, as the key device in modern manufacturing industry, are complicated electrohydraulic products. The reliability is the most important index of CNC machining centers. However, simple life distributions hardly reflect the true law of complex system reliability with many kinds of failure mechanisms. Due to Weibull model’s versatility and relative simplicity and artificial neural networks’ (ANNs) high capability of approximating, they are widely used in reliability engineering and elsewhere. Considering the advantages of these two models, this paper defined a novel model: Weibull neural network (WNN). WNN inherits the hierarchical structure from ANNs which include three layers, namely, input layer, hidden layer, and output layer. Based on more than 3000 h field test data of CNC machining centers, WNN has been successfully applied in comprehensive operation data analysis. The results show that WNN has good approximation ability and generalization performance in reliability assessment of CNC machining centers

Drilling Induced Fatigue Damage in Ti-6Al-4V

The objective of this work is to develop an understanding of the relationship between hole drilling processes and the fatigue performance of the resulting part in Ti-6Al-4V. This problem is significant, as on the order of one-hundred thousand to a million holes are created in a typical large aircraft, and the limiting performance criterion is usually the fatigue lifetime. The path between the drilling process parameters and the fatigue performance has two main steps: characterization of the thermo-mechanical drill process and assessment of the relationship between the hole integrity left by the drill process and the fatigue performance. Development has been limited by the robustness of previously available thermal characterization systems, poor correlation between drill processes and physical observations of metallic effects, and limited success identifying the key hole integrity characteristics. This work develops robust novel thermal methods which enable integration into current drill process development techniques. The key integrity drivers in the hole wall are identified, characterized, and a system to assess is presented. The thermal and hole integrity trends are presented as guidance for drill process development providing significant opportunities to optimize processes. Thus, this work advances knowledge of the process to fatigue lifetime relationship by correlating the thermo-mechanical drill process to fatigue life in Ti-6Al-4V

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

A Reliability Case Study on Estimating Extremely Small Percentiles of Strength Data for the Continuous Improvement of Medium Density Fiberboard Product Quality

The objective of this thesis is to better estimate extremely small percentiles of strength distributions for measuring failure process in continuous improvement initiatives. These percentiles are of great interest for companies, oversight organizations, and consumers concerned with product safety and reliability. The thesis investigates the lower percentiles for the quality of medium density fiberboard (MDF). The international industrial standard for measuring quality for MDF is internal bond (IB, a tensile strength test). The results of the thesis indicated that the smaller percentiles are crucial, especially the first percentile and lower ones. The thesis starts by introducing the background, study objectives, and previous work done in the area of MDF reliability. The thesis also reviews key components of total quality management (TQM) principles, strategies for reliability data analysis and modeling, information and data quality philosophy, and data preparation steps that were used in the research study. Like many real world cases, the internal bond data in material failure analysis do not follow perfectly the normal distribution. There was evidence from the study to suggest that MDF has potentially different failure modes for early failures. Forcing of the normality assumption may lead to inaccurate predictions and poor product quality. We introduce a novel, forced censoring technique that closer fits the lower tails of strength distributions, where these smaller percentiles are impacted most. In this thesis, such a forced censoring technique is implemented as a software module, using JMP® Scripting Language (JSL) to expedite data processing which is key for real-time manufacturing settings. Results show that the Weibull distribution models the data best and provides percentile estimates that are neither too conservative nor risky. Further analyses are performed to build an accelerated common-shaped Weibull model for these two product types using the JMP® Survival and Reliability platform. The use of the JMP® Scripting Language helps to automate the task of fitting an accelerated Weibull model and test model homogeneity in the shape parameter. At the end of modeling stage, a package script is written to readily provide the field engineers customized reporting for model visualization, parameter estimation, and percentile forecasting. Furthermore, using the powerful tools of Splida and S Plus, bootstrap estimates of the small percentiles demonstrate improved intervals by our forced censoring approach and the fitted model, including the common shape assumption. Additionally, relatively more advanced Bayesian methods are employed to predict the low percentiles of this particular product type, which has a rather limited number of observations. Model interpretability, cross-validation strategy, result comparisons, and habitual assessment of practical significance are particularly stressed and exercised throughout the thesis. Overall, the approach in the thesis is parsimonious and suitable for real time manufacturing settings. The approach follows a consistent strategy in statistical analysis which leads to more accuracy for product conformance evaluation. Such an approach may also potentially reduce the cost of destructive testing and data management due to reduced frequency of testing. If adopted, the approach may prevent field failures and improve product safety. The philosophy and analytical methods presented in the thesis also apply to other strength distributions and lifetime data

Heavy vehicle propulsion system materials program: Semiannual progress report, April 1996--September 1996

The purpose of the Heavy Vehicle Propulsion System Materials Program is the development of materials: ceramics, intermetallics, metal alloys, and metal and ceramic coatings, to support the dieselization of class 1-3 trucks to realize a 35% fuel-economy improvement over current gasoline-fueled trucks and to support commercialization of fuel-flexible LE-55 low-emissions, high-efficiency diesel engines for class 7-8 trucks. The Office of Transportation Technologies, Office of Heavy Vehicle Technologies (OTT OHVT) has an active program to develop the technology for advanced LE-55 diesel engines with 55% efficiency and low emissions levels of 2.0 g/bhp-h NO{sub x} and 0.05 g/bhp-h particulates. The goal is also for the LE-55 engine to run on natural gas with efficiency approaching that of diesel fuel. The LE-55 program is being completed in FY 1997 and, after approximately 10 years of effort, has largely met the program goals of 55% efficiency and low emissions. However, the commercialization of the LE-55 technology requires more durable materials than those that have been used to demonstrate the goals. Heavy Vehicle Propulsion System Materials will, in concert with the heavy duty diesel engine companies, develop the durable materials required to commercialize the LE-55 technologies. OTT OHVT also recognizes a significant opportunity for reduction in petroleum consumption by dieselization of pickup trucks, vans, and sport utility vehicles. Application of the diesel engine to class 1, 2, and 3 trucks is expected to yield a 35% increase in fuel economy per vehicle. The foremost barrier to diesel use in this market is emission control. Once an engine is made certifiable, subsequent challenges will be in cost; noise, vibration, and harshness (NVH); and performance. Separate abstracts have been submitted to the database for contributions to this report