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Excerpt from A Methodology for Manufacturing Process Signature Analysis
Most manufacturing processes are monitored for output quality, either continuously or on a sampled basis. Process monitoring is used to assess whether or not the process is performing its function adequately so that appropriate corrective action can be taken if necessary. Inputs to the process monitor include one or more measured attributes of the product or process which should reflect the product's ability to perform its intended function.
As we strive to improve the quality of our manufacturing processes, we seek better monitoring and control systems. One approach is to utilize more sophisticated measurements. At every stage of even the simplest manufacturing process, there are many opportunities for quality measurements; involving process inputs (material thickness or temperature), process attributes (feed rate, Spindle speed or tool vibration), and measures of the process output (critical dimensions or surface finish). To monitor a larger process or a series of process steps, we could inspect the finished product or assess ultimate customer satisfaction through questionnaires or warranty information. The integration of computers into our manufacturing environment (cim) can certainly facilitate the collection of these different process measurements but the information by itself is not enough. In fact, without an appropriate procedure for the analysis and evaluation of this information, the resultant flood of data can confound process improvement efforts.
Our approach to process monitoring is called manufacturing process signature analysis. During each cycle, we measure one or more process signals and/or parameters over the duration of the process. We then analyze these measurements to determine the quality of the process iteration which just took place. Ideally, this quality classification would then be fed back through an appropriate controller to close the process control loop.
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This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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Excerpt from A Methodology for Manufacturing Process Signature Analysis
Most manufacturing processes are monitored for output quality, either continuously or on a sampled basis. Process monitoring is used to assess whether or not the process is performing its function adequately so that appropriate corrective action can be taken if necessary. Inputs to the process monitor include one or more measured attributes of the product or process which should reflect the product's ability to perform its intended function.
As we strive to improve the quality of our manufacturing processes, we seek better monitoring and control systems. One approach is to utilize more sophisticated measurements. At every stage of even the simplest manufacturing process, there are many opportunities for quality measurements; involving process inputs (material thickness or temperature), process attributes (feed rate, Spindle speed or tool vibration), and measures of the process output (critical dimensions or surface finish). To monitor a larger process or a series of process steps, we could inspect the finished product or assess ultimate customer satisfaction through questionnaires or warranty information. The integration of computers into our manufacturing environment (cim) can certainly facilitate the collection of these different process measurements but the information by itself is not enough. In fact, without an appropriate procedure for the analysis and evaluation of this information, the resultant flood of data can confound process improvement efforts.
Our approach to process monitoring is called manufacturing process signature analysis. During each cycle, we measure one or more process signals and/or parameters over the duration of the process. We then analyze these measurements to determine the quality of the process iteration which just took place. Ideally, this quality classification would then be fed back through an appropriate controller to close the process control loop.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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Excerpt from A Methodology for Manufacturing Process Signature Analysis
One of the fundamental challenges facing manufacturing engineers today is the achievement of continuous improvement through the implementation of better process control systems. We believe that the improvement of control systems entails the collection of more information about the process and/or more effective use of that information. We present manufacturing process signature analysis in order to construct a relationship between the collected information (process signatures) and the quality of the process output, which can be used for on-line monitoring and control. The general procedure applied in this paper consists of three steps: feature extraction, feature selection, and classification.
We have found that the extracting of large sets of features from signatures is straightforward and that several classification schemes are available, with neural networks being the most general and powerful method that we have tried. Feature selection, on the other hand, is generally quite difficult for complex data structures. We present several feature extraction methods and show that neural networks can be quite useful in choosing different feature sets. Using a data set from an automated solder joint inspection system, we demonstrate the unique capabilities of neural networks for both feature selection and classification, using more traditional statistical classification techniques as a benchmark.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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Paperback. Zustand: New. Print on Demand. This book presents a methodology for manufacturing process signature analysis, which aims to enhance process control systems through data analysis. The author argues that improving control systems involves collecting more information about the process and using it effectively. The book proposes a three-step analysis method, which consists of feature extraction, feature selection, and classification, to establish a relationship between the collected information (process signatures) and the quality of the process output. This relationship can be used for online monitoring and control. The book covers the advantages and limitations of various signature analysis tools, ranging from statistical process control to artificial neural networks. The author emphasizes the importance of feature selection in reducing the complexity of classification problems and discusses different feature selection schemes. One of the key contributions is the use of the first layer weights in a neural network topology to identify the most useful components of the input signature. The book concludes by exploring future research directions, highlighting the need for accurate quality metrics, effective sensor selection, and efficient neural network training algorithms. Overall, this book provides a comprehensive overview of process signature analysis and its application in manufacturing process monitoring and control. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Bestandsnummer des Verkäufers 9781332269860_0
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