A Theoretical Foundation for the Development of Process Capability Indices and Process Parameters Optimization under Truncated and Censoring Schemes

Process capability indices (PCIs) provide a measure of the output of an in-control process that conforms to a set of specification limits. These measures, which assume that process output is approximately normally distributed, are intended for measuring process capability for manufacturing systems. After implementing inspections, however, non-conforming products are typically scrapped when units fail to meet the specification limits; hence, after inspections, the actual resulting distribution of shipped products that customers perceive is truncated. In this research, a set of customer-perceived PCIs is developed focused on the truncated normal distribution, as an extension of traditional manufacturer-based indices. Comparative studies and numerical examples reveal considerable differences among the traditional PCIs and the proposed PCIs. The comparison results suggest using the proposed PCIs for capability analyses when non-conforming products are scrapped prior to shipping to customers. The confidence interval approximations for the proposed PCIs are also developed. A simulation technique is implemented to compare the proposed PCIs with its traditional counterparts across multiple performance scenarios. The robust parameter design (RPD), as a systematic method for determining the optimum operating conditions that achieve the quality improvement goals, is also studied within the realm of censored data. Data censoring occurs in time-oriented observations when some data is unmeasurable outside a predetermined study period. The underlying conceptual basis of the current RPD studies is the random sampling from a normal distribution, assuming that all the data points are uncensored. However, censoring schemes are widely implemented in lifetime testing, survival analysis, and reliability studies. As such, this study develops the detailed guidelines for a new RPD method with the consideration of type I-right censoring concepts. The response functions are developed using nonparametric methods, including the Kaplan-Meier estimator, Greenwood\u27s formula, and the Cox proportional hazards regression method. Various response-surface-based robust parameter design optimization models are proposed and are demonstrated through a numerical example. Further, the process capability index for type I-right censored data using the nonparametric methods is also developed for assessing the performance of a product based on its lifetime

Applied Markovian Approach for Determining Optimal Process Means in Single Stage SME Production System

The determination of optimum process mean has become one of the focused research area in order to improve product quality. Depending on the value of quality characteristic of juice filling in the bottle, an item can be reworked, accepted or accepted with penalty cost by the system which is successfully transform to the finishing product by using the Markovian model. By assuming the quality characteristic is normally distributed, the probability of rework, accept and accept with penalty cost is obtained by the Markov model and next the optimum of process mean is determined which maximizes the expected profit per item. In this paper, we present the analysis of selecting the process mean in the filling process. By varying the rework and accept with penalty cost, the analysis shown the sensitivity of the Markov approach to determine the process mean

An Integrated Probability-Based Approach for Multiple Response Surface Optimization

Nearly all real life systems have multiple quality characteristics where individual modeling and optimization approaches can not provide a balanced compromising solution. Since performance, cost, schedule, and consistency remain the basics of any design process, design configurations are expected to meet several conflicting requirements at the same time. Correlation between responses and model parameter uncertainty demands extra scrutiny and prevents practitioners from studying responses in isolation. Like any other multi-objective problem, multi-response optimization problem requires trade-offs and compromises, which in turn makes the available algorithms difficult to generalize for all design problems. Although multiple modeling and optimization approaches have been highly utilized in different industries, and several software applications are available, there is no perfect solution to date and this is likely to remain so in the future. Therefore, problem specific structure, diversity, and the complexity of the available approaches require careful consideration by the quality engineers in their applications

Inspection by exception: a new machine learning-based approach for multistage manufacturing

Manufacturing processes usually consist of multiple different stages, each of which is influenced by a multitude of factors. Therefore, variations in product quality at a certain stage are contributed to by the errors generated at the current, as well as preceding, stages. The high cost of each production stage in the manufacture of high-quality products has stimulated a drive towards decreasing the volume of non-added value processes such as inspection. This paper presents a new method for what the authors have referred to as ‘inspection by exception’ – the principle of actively detecting and then inspecting only the parts that cannot be categorized as healthy or unhealthy with a high degree of certainty. The key idea is that by inspecting only those parts that are in the corridor of uncertainty, the volume of inspections are considerably reduced. This possibility is explored using multistage manufacturing data and both unsupervised and supervised learning algorithms. A case study is presented whereby material conditions and time domain features for force, vibration and tempering temperature are used as input data. Fuzzy C-Means (FCM) clustering is implemented to achieve inspection by exception in an unsupervised manner based on the normalized Euclidean distances between the principal components and cluster centres. Also, deviation vectors for product health are obtained using a comparator system to train neural networks for supervised learning-based inspection by exception. It is shown that the volume of inspections can be reduced by as much as 82% and 93% using the unsupervised and supervised learning approaches, respectively

Concise Process Improvement Methods

This thesis reviews two methodologies for process improvement; Six Sigma and the Shainin System. A strengthened methodology is developed following the 12-step Six Sigma DMAIC cycle with an added Shainin loop in the Analyse phase to narrow down sources of variation. This Hybrid Six Sigma framework is used to develop a sampling strategy known as the Process Variation Diagnostic Tool (PVDT). The PVDT allows a Gage R&R and a Provisional Process Capability study to be carried out with just 20 samples. It also allows for an IsoplotSM and a Shainin Multi-Vari study. The method was then reviewed in three different industrial situations to demonstrate its effectiveness. Applying the PVDT allowed the project teams involved to quickly produce Gage R&R and Provisional Process Capability Studies. It reduced samples required from the combined 110 measurements from 60 products typically taken in industry to 60 measurements from 20 products. A significant advantage was the ability to extract a Shainin Multi-Vari Study from measurements taken for the PVDT. This technique allowed the project team the ability to categorise the most significant families of variation. From these case studies it can be seen that at the border of the Measure/Analyse phase in Six Sigma the proposed PVDT offers an efficient method of collecting Six Sigma metrics and steering the course of an improvement project. A teaching vehicle known as the PIM game is introduced to demonstrate and facilitate the teaching of a number Process improvement Method. These methods are directly related to Six Sigma and Shainin methods developed in this thesis. The historical development and need for a teaching game are discussed. Finally the thesis proposes a new method of destructive measurement system analysis (MSA). An industrial problem is used to benchmark the method against a traditional approach to destructive MSA. The project highlights when there is a second non-destructive test a conservative estimate of Gage R&R can be determined for destructive test equipment

ARMD Workshop on Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation

This report documents the goals, organization and outcomes of the NASA Aeronautics Research Mission Directorates (ARMD) Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation Workshop. The workshop began with a series of plenary presentations by leaders in the field of structures and materials, followed by concurrent symposia focused on forecasting the future of various technologies related to rapid manufacturing of metallic materials and polymeric matrix composites, referred to herein as composites. Shortly after the workshop, questionnaires were sent to key workshop participants from the aerospace industry with requests to rank the importance of a series of potential investment areas identified during the workshop. Outcomes from the workshop and subsequent questionnaires are being used as guidance for NASA investments in this important technology area

Spacecraft high-voltage power supply construction

The design techniques, circuit components, fabrication techniques, and past experience used in successful high-voltage power supplies for spacecraft flight systems are described. A discussion of the basic physics of electrical discharges in gases is included and a design rationale for the prevention of electrical discharges is provided. Also included are typical examples of proven spacecraft high-voltage power supplies with typical specifications for design, fabrication, and testing