## 1 Introduction  
Fully automated control systems as the flexible manufacturing systems (FMSs) widely applied to mass production can reach the requirements of productivity and quality of products [1]. The FMSs activate the automatic operations using pre-instructed programs. Therefore, these FMSs have difficulty reaching the requirements of flexibility, adaptability, and reliability. The manufacturing systems operated by human workers with cognitive capability such as perception, learning, and reasoning to make a decision can satisfy these requirements. However, the practical applications of these systems are low due to the high production costs as well as only being suitable for the production of prototypes or small lot sizes. So, it is necessary to integrate cognitive capability to the FMSs to achieve the biology inspired FMS systems with advanced characteristics [1; 2; 3]. Currently, manufacturing demands are changing from mass production to mass customization, which is focused on exclusive and individualized demand [2; 3]. This trend requires the manufacturing systems to have the ability to adapt to the fast change of the manufacturing environment. To face this challenge, the new trend of manufacturing system development is to apply autonomous behaviors inspired from living systems and nature to have the sustainable manufacturing systems. Industry 4.0 enables the realization of sustainable manufacturing using the ubiquitous information and communication technology (ICT) infrastructure [4]. National strategies for developing smart manufacturing systems are focused on by both developing and developed countries [5; 6]. The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4; 7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8; 9; 10; 11; 12; 13; 14; 15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19]. In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20; 21; 22] and advance in ICT [23] such as cloud computing [24], internet of things (IoT), internet of service (IoS) [25; 26; 27; 28; 29], agent technology [30; 31; 32; 33; 34; 35], cognitive technology [36; 37], big data [38; 39], data mining [40; 41], and artificial intelligence [42; 43; 44; 45; 46; 47]. Smart manufacturing systems (SMS) built with intelligent cyber-physical systems (CPSs) are the heart of the fourth industrial revolution [21; 48]. SMS aim at improving the levels of intelligence and autonomy due to the CPSs for adapting to the manufacturing changes. Industry 4.0 is expected to generate new business models, as well as increase the efficiency of manufacturing processes in which CPS based SMS can react flexibly to changes [49]. SMSs have autonomic self-characteristics such as self-configuration, self-monitoring, and self-healing [9; 10; 13]. The paper presents a smart cyber-physical manufacturing system (Smart-CPMS) for adapting to the manufacturing changes. The Smart-CPMS has the advanced characteristics as follows:  
* Each element in the Smart-CPMS is an autonomous entity equipped with the cognitive capabilities such as perception, reasoning for making a decision, communication, and cooperation. Each CPS has the ability to make a decision autonomously;
* Improving the component and system level in intelligence and autonomy of the current FMS;
* The Smart-CPMS has the ability to adapt to the manufacturing changes in short and immediate times. The contribution of this research is to develop a Smart-CPMS for adapting the manufacturing changes in an autonomous and intelligent way. For realizing the Smart-CPMS, ICT infrastructure and artificial intelligence techniques are proposed. The functionalities of the Smart-CPMS such as self-adjustment and negotiation of machines in the network are proven. The remainder of the paper is organized as follows.