Preface

The rise of artificial intelligence, the Internet, and other emerging technologies has promoted the deep integration of new information and advanced communication and manufacturing technologies. It has also encouraged the transformation of manufacturing and services to digitalization, networking, and intelligence, which is undoubtedly a revolution in manufacturing methods and technical means. This transformation inevitably affects all aspects of the strategic orientation, resource allocation, and management of enterprises.

As one of the key factors of enterprise management, quality control will also change along with changes in the manufacturing environment and factors affecting product quality (man, machine, material, method, and environment). A series of changes will occur in management contents, methods, capabilities, and real-time management effectiveness and efficiency. Some of these changes are revolutionary.

The several following features will have a significant impact on quality management.

Individual customer needs and rapid response. Identifying customer needs is the first step in quality management. Customer needs will gradually change from small varieties and large batches to multiple varieties and small batches; individualized and differentiated customer needs will become the norm. The basic quality management concept of "customer-focused" will be further strengthened with rapid response to customer needs and continuous satisfaction.

Customer participation in product design and development. The interconnection in the era of intelligent manufacturing provides the technical means for customers to participate in product design and development. Customers can participate in reviewing, verifying, and validating product design and development, as well as product type testing, reliability testing, and other activities either remotely or on-site, providing the technical means to fully implement customers' individual needs.

Further improved process capability. The technical means and control capabilities of process control are continuously improved to make production more convenient and flexible. Process management based on big data is more accurate, optimized, and mature, so that the predictive maintenance of equipment is gradually realized. Product identification, traceability, and process error prevention are more thorough through technical means such as electronic labels and QR codes. Quality monitoring (including statistical process control) is a real-time process, and quality tools and methods are convenient to use.

Easy-to-use quality management tools and methods based on statistical technology. Quality management tools based on mathematical and statistical technology, such as the seven basic quality tools, statistical process control (SPC), and others, involve a large amount of data statistics and calculations. As such, an intelligent manufacturing system will automatically collect, analyze, and share this data, which needs to be collected in real-time according to different conditions set in advance, making the use of quality management tools more convenient.

The fields of traditional manufacturing, which were once well defined and distinct, have been completely overturned by new technologies, and each field extends, covers, and overlaps with the other. While intelligent technologies and digitalization strategies have brought about big changes in the manufacturing industry, quality control remains an important area that needs close attention. If a manufacturer has completed the intelligent transformation in design, production, testing, packaging, and other processes, but has not updated the quality control system accordingly, there will be a big gap between the current process and the product vision. This will ultimately lead to failure to realize the product and service promises made to customers. To optimize and upgrade the entire manufacturing process, quality control methods must be simultaneously optimized as well.

Therefore, quality control is an essential factor in the era of intelligent manufacturing. Real and comprehensive innovations are needed and, fortunately, many researchers and institutes have developed several innovative technologies to optimize existing quality control systems and manage complex and mass production details.

Focused on new trends and developments in quality control from a worldwide perspective, this book presents the latest results on novel approaches to current problems in quality control. Written by academicians, researchers, and practicing engineers, this volume contains thirteen chapters organized around three topics: intelligent manufacturing, robust design, and control charts. The information contained herein is useful for technical experts and entrepreneurs.

This book would not have been possible without the generous assistance of many colleagues. The authors and I would like to express our sincere appreciation to my co-editors, Professor Helena Navas and Dr. Paulo Pereira. We would also like to thank Ms. Anja Filipovic and Ms. Jasna Bozic at IntechOpen for their patient and careful organization and promotion in the publication of this book.

**Pengzhong, LI** Tongji University (School of Mechanical Engineering; Chinesisch-Deutsches Hochschulkolleg), Shanghai, China

> **Paulo António Rodrigues Pereira** Portuguese Institute of Blood and Transplantation, Portugal

> > **Helena Navas** Universidade Nova de Lisboa, Portugal

> > > **1**

**Chapter 1**

**Abstract**

Management

management, quality improvements

**1. Introduction**

*Ercan Oztemel*

Introduction to Intelligent Quality

Intelligent manufacturing is becoming more and more attractive for industrial societies especially after the introduction of industry 4.0 where most of industrial operations are to be carried by robots equipped with intelligent capabilities. This explicitly implies that the manufacturing systems will entirely be integrated and all manufacturing functions including quality control and management will have to be made as much intelligent as possible in operating with minimum human intervention. This Chapter will present a brief overview of some implications about intelligent quality systems. It intends to provide the readers of the book to understand how the concept of artificial intelligence is to be embedded into quality functions. It is known that the interoperability is the rapid transformation requirement of industry specific operations. This requires the integration of quality functions to other manufacturing functions for sharing the quality related knowledge with other manufacturing functions in order to sustain total intelligent collaboration. Achieving this, on the other hand, ensures the improvement of manufacturing processes for better performance in an integrated manner. Note that, although some general information about intelligent manufacturing systems are given, this chapter

is particularly focused on discussing intelligent quality related issues.

**Keywords:** intelligent quality, intelligent manufacturing, integrated quality, quality

Intelligent manufacturing is becoming more and more attractive for industrial societies especially after the emergence of industry 4.0 where most of industrial operations are to be carried by robots equipped with intelligent capabilities. Since digital transformation is increasing every day. The manufacturing societies are enforced not only to increase the development speed of manufacturing systems but also to improve the functionality, flexibility, usability and interoperability of the system developed. For each manufacturing function, different methods and methodologies are developed to sustain the level of intelligence due to the nature of the operations carried out within the scope. This applies to quality operations as well. Introducing artificial intelligence in quality systems were considered since so many years. Pham and Oztemel (1996) Published a book on intelligent quality systems and took the attention of both research and industrial communities on this issue [1]. They presented real life applications and highlighted possible areas of future developments. Oztemel and Tekez (2008) took this initiative one step ahead and proposed a multi agent quality management system where each quality function is considered to be
