Ngoc-Hien Tran

Autonomy for Smart Manufacturing

Smart manufacturing (SM) considered as a new trend of modern manufacturing helps to meet objectives associated with the productivity, quality, cost and competiveness. It is characterized by decentralized, distributed, networked compositions of autonomous systems. The model of SM is inherited from the organization of the living systems in biology and nature such as ant colony, school of fish, bee’s foraging behaviors, and so on. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as selfadaptation, self-diagnosis, and self-healing. To prove this concept, a cloud machining system is considered as research object in which internet of things and cloud computing are used to integrate, organize and allocate the machining resources. Artificial life tools are used for cooperation among autonomous elements in the cloud machining system.

Development of an Intelligent Agent based Manufacturing System.

The new trend of the manufacturing system development is to apply autonomous behaviours inspired from biology for the manufacturing systems. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and self-optimization. To carry out these characteristics, the paper presents a paradigm about intelligent agent, called the cognitive agent and using cognitive agents for adapting to disturbances such as tool wear, machine breakdown that have happened on the shop floor. Modern manufacturing systems having the distributed control need autonomy and cooperation in solving problems of agents from agent technology, and cognitive capabilities for agents from cognitive technology. Cognitive agents combined from these two technologies are necessary for future manufacturing systems.

A Decision Support System for Selecting Additive Manufacturing Technologies

Layer by layer manufacturing or additive manufacturing has been used in many application fields. Currently, there are a large number of 3D printing methods in the market. Selection of an appropriate method for a printed object is a difficult decision due to lack of bench mark standards and industry experiences with most of these methods. The paper presents a decision support system for selecting an appropriate 3D printing method to print products. From product requirements, printing methods were analyzed and evaluated to decide which one fulfills the product requirements in the best way. For realizing the decision support system, databases about materials as well as 3D printing methods were built; rules for decision making were proposed. The system allows selecting an appropriate 3D printing method from inputted product requirements.

A swarm of cognitive agents for controlling smart manufacturing systems

Disturbances in the manufacturing environment such as machine breakdown, controller malfunction, urgent jobs, and so on reduce the productivity; increase the downtime of the machining system. In order to solve these problems, new strategies and technologies are applied. The paper presents a new technology called cognitive agent to control the machining system. Cognitive agents with intelligent behaviors such as perception, reasoning, and cooperation allow the manufacturing to overcome the disturbances. For cooperation among cognitive agents, ant colony optimization was proposed. The machining system controlled by swarm of cognitive agents was tested successfully in the case of disturbances.

A Smart Machining System

Globalization, unpredictable markets, increased products customization and frequent changes in products, production technologies and machining systems have become a complexity in today’s manufacturing environment. One key strategy for coping with the evolution of this situation is to develop or apply an enable technology such as intelligent manufacturing. Intelligent manufacturing system (IMS) is characterized by decentralized, distributed, networked compositions of heterogeneous and autonomous systems. The model of IMS is inherited from the organization of the living systems in biology and nature so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and selfhealing. To prove this concept, an innovative system with applying the advanced information and communication technology such as internet of things, cognitive agent are proposed to integrate, organize and allocate the machining resources. Innovative system is essential for modern machining system to flexibly and quickly adapt to new challenges of manufacturing environment.

Biologically Inspired Techniques for Autonomous Shop Floor Control

Currently, the conventional manufacturing systems, such as the Flexible Manufacturing Systems (FMSs) are unable to adapt to the complexity and dynamic of the manufacturing environment. These systems activate the automatic operations by using the pre-instructed programs and should be stopped to re-program and re-plan in case of changes of the manufacturing environment, which reduce the flexibility of the systems and increase the downtime. Self-adaptation to disturbances is a crucial issue in the development of intelligent manufacturing systems, which keeps the manufacturing system running and avoids stopping completely. Many methods for the management of changes and disturbances within manufacturing systems were proposed in the literature such as rescheduling (Vieira et al., 2003; Wang et al., 2008), reactive and collaborative approaches (Monostoni et al., 1998; Leitao & Restivo, 2006). These methods can be classified by two criteria: reconfiguration and autonomy (Saadat et al. 2008). Reconfiguration is to rearrange and restructure manufacturing resources that require the rescheduling method (Vieira et al., 2003) and reconfigurable ability of manufacturing systems (Park & H.W. Choi, 2008). A dynamic rescheduling is done when there is an occurrence of disturbances such as the machine breakdown, malfunction of robot or transporter with long recovering time. Here, a new schedule is generated when the current schedule is affected by disturbances (Vieira et al., 2003; Wang et al., 2008). Autonomy allows the system to recover autonomously without modifying scheduling. Reactive and collaborative methods were proposed following this criterion (Monostoni et al., 1998). Reactive method is an autonomous control of an entity to overcome disturbances by itself, while the collaborative method is used for a cooperation of an entity with other entities in order to adapt to disturbances. These methods are suitable for disturbances, which are not necessary to reschedule. In order to implement reactive/collaborative methods, the distributed control architecture is required (Park & Lee, 2000). The control architecture changes from centralized control of non-intelligent entities in hierarchical structures of the FMSs towards decentralized control of intelligent entities in distributed structures.