<p>The capability of continuously producing
good quality products with high productivity and low cost is critical for
manufacturers. Generally, products are made up of components, which enable the
product to perform its purpose. A complex product may be assembled from many
components through multiple assembly stages. Any quality defects in a component
may build up in the product. A good understanding of how the quality of
components impacts the quality of products in a complex manufacturing system is
essential for keeping the competitiveness of a manufacturer. </p>
<p>In this research, a series of
quality management models are proposed based on studying the relationship between
component quality and product quality. Optimal quality control leads to
increased competitiveness of a manufacturer, since it helps reduce cost, increase
production, and limit environmental impact. The research starts from studying the
tolerance allocation problem, which is fundamental of managing the tradeoff
between quality, productivity, cost, and waste. First, a tolerance allocation
method that minimizes cost is proposed. This model jointly considers process
variation and tolerance specifications. The relation between manufacturer,
user, design, and processing are embedded in the cost model. To solve the
tolerance allocation problem from the root cause, i.e., the variations in production
processes, a second tolerance allocation model is then provided. This model
considers both product design (tolerance selection) and operation planning (or
production rate selection). Relations among production rate, production cost,
processing precision, and waste are considered. Furthermore, a new process
control model that extends traditional statistical process control techniques is
proposed. Data acquired from a manufacturing system are usually in the forms of
time series, and anomalies in the time series are generally related to quality defects.
A new method that can detect anomalies in time series data with long length and
high dimensionality is developed. This model is based on recurrent neural
networks, and the parameters of the neural networks can be trained using data
acquired during routine operation of a manufacturing system. This is very
beneficial because often, there are few data labeled as anomalies, since
anomalies are hopefully rare events in a well-managed system. Last, quality
control of remanufacturing is studied. A component-oriented reassembly model is
proposed to manage the varied quality of returned component and varied needs of
customers. In this model, returned components are inspected and assigned scores
according to their quality/function, and categorized in a reassembly inventory.
Based on the reassembly inventory, components are paired under the control of a
reassembly strategy. A reassembly-score iteration algorithm is developed to
identify the optimal reassembly strategy. The proposed model can reassemble
products to meet a larger variety of customer needs, while simultaneously
producing better remanufactured products.</p>
In summary, this dissertation presents a series of novel
quality management models to keep manufacturers’ competitiveness. These models
are based on studying factors that impact component and product quality at
multiple stages of a product life cycle. It was found that analyzing the
relationship between component and product quality is a very effective way of
improving product quality, saving cost, and reducing environmental impact of
manufacturing.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14495772 |
| Date | 27 April 2021 |
| Creators | Yue Wang (10710720) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/THE_RELATIONSHIP_BETWEEN_COMPONENT_AND_PRODUCT_QUALITY_IN_MANUFACTURING_WITH_EMPHASIS_ON_COMPETITIVENESS/14495772 |
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