Data mining algorithm for manufacturing process control

In this paper, a new data mining algorithm based on the rough sets theory is presented for manufacturing process control. The algorithm extracts useful knowledge from large data sets obtained from manufacturing processes and represents this knowledge using “if/then” decision rules. Application of the data mining algorithm developed in this paper is illustrated with an industrial example of rapid tool making (RTM). RTM is a technology that adopts rapid prototyping (RP) techniques, such as spray forming, and applies them to tool and die making. A detailed discussion on how to control the output of the manufacturing process using the results obtained from the data mining algorithm is also presented. Compared to other data mining methods, such decision trees and neural networks, the advantage of the proposed approach is its accuracy, computational efficiency, and ease of use.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45889/1/170_2004_Article_2367.pd

New Trends in the Use of Artificial Intelligence for the Industry 4.0

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0

A framework for real-time product quality monitoring system with consideration of process-induced variations

Department of Human and Systems EngineeringAs industrial technologies develop, the manufacturing industry is globally changing in more automated and complex manners, and the prediction of real-time product quality has become an essential issue. Although many of the physical manufacturing activities are getting more automated than ever, there still exist many uncovered parameters that, either directly or indirectly, affect the product quality. In many manufacturing sites, the quality tests in their processes still rely on few skilled operators and quality experts, which requires a lot of time and human efforts to manage the product quality issues. In this thesis, thus, a real-time/in-process quality monitoring system for small and medium size manufacturing environments is proposed to provide the data-driven product quality monitoring system framework. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using the support vector machine (SVM) algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. Additionally, we propose a framework for analysis of the quality inspection results from the monitoring system with respect to quality costs, including inspection and warranty costs. In addition, this thesis establishes a relationship between the warranty cost and the severity of customer-perceived quality. Finally, we suggest a future work that a prescriptive product quality assessment concept using the Hidden Markov Models (HMM) that analyze and forecast possible future product quality problems using production data from manufacturing processes based on data flow analysis. Also, a door trim production data in an automotive company is illustrated to verify the proposed quality monitoring/prediction model.ope

Topic detection using maximal frequent sequences

Master'sMASTER OF ENGINEERIN

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

A Guide to Additive Manufacturing

This open access book gives both a theoretical and practical overview of several important aspects of additive manufacturing (AM). It is written in an educative style to enable the reader to understand and apply the material. It begins with an introduction to AM technologies and the general workflow, as well as an overview of the current standards within AM. In the following chapter, a more in-depth description is given of design optimization and simulation for AM in polymers and metals, including practical guidelines for topology optimization and the use of lattice structures. Special attention is also given to the economics of AM and when the technology offers a benefit compared to conventional manufacturing processes. This is followed by a chapter with practical insights into how AM materials and processing parameters are developed for both material extrusion and powder bed fusion. The final chapter describes functionally graded AM in various materials and technologies. Throughout the book, a large number of industrial applications are described to exemplify the benefits of AM

Mass Production Processes

It is always hard to set manufacturing systems to produce large quantities of standardized parts. Controlling these mass production lines needs deep knowledge, hard experience, and the required related tools as well. The use of modern methods and techniques to produce a large quantity of products within productive manufacturing processes provides improvements in manufacturing costs and product quality. In order to serve these purposes, this book aims to reflect on the advanced manufacturing systems of different alloys in production with related components and automation technologies. Additionally, it focuses on mass production processes designed according to Industry 4.0 considering different kinds of quality and improvement works in mass production systems for high productive and sustainable manufacturing. This book may be interesting to researchers, industrial employees, or any other partners who work for better quality manufacturing at any stage of the mass production processes