An Intelligent Knowledge Management for Machining System
Abstract
Today, information has become more important. Even data, information and knowledge are often used as if they have same meaning. This problem raises difficulties in engineering. It is necessary to exist a knowledge management system to avoid increased costs, waste of time and increased errors. Knowledge management is a comprehensive process of knowledge creation, knowledge validation, knowledge presentation, knowledge distribution and knowledge application. In this paper, knowledge management has been explained in general. Then as an example for this study, machining system has been considered, and an application of Knowledge Management in engineering has been attempted to explain. The paper proposes a knowledge management to achieve a competitive control of the machining systems. The model can be used by the manager for the choosing of competitive orders.
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Introduction
A general definition has been „getting the right information to the right people at the right time‟ in order for them to make better decisions.
Due to technology facilitates the rapid exchange of information, the pace of acquisition is growing exponentially in both large and small enterprises. The vast amounts of knowledge possessed by the enterprises are spread across countless structured and unstructured sources.
To improve processes and bring new products to the market faster and more cheaply, the enterprises have to identify, make available and apply this knowledge.
It is necessary to exist a knowledge management system and coordination between disciplines to avoid increased costs, waste of time and increased errors. Thus, information must be understood, organized and transformed for problems solving.
Consequently, information transformed in product is knowledge and coordination of this kind of knowledge is made by means of knowledge management.
The manufacturing industry faces the challenge of responding quickly to the ever-changing requirements of customers. It is necessary that in these high competitive environments, enterprises to control production system dynamics of such as:
- change in the product types and variants;
- change in the production quantities.
Enterprises have to develop and implement more responsive and flexible manufacturing systems based on knowledge. By this way, they can respond to outgoing and difficult to predict change in production requirements and make products with high quality, low cost and fast delivery.
The market dynamics is further passed to the mode of operation and management. In a knowledge-based society and economy, operations such as determining the relevant information and aggregating them into pieces of knowledge must be automated, because in such a complex and unpredictable environment, they are indispensable tools for creating, searching and structuring knowledge. The interaction between the economic environment and the manufacturing system is a major source of knowledge about the economic environment and the manufacturing system themselves [3].
Conclusion
In this paper the architecture of the knowledge management of the machining system was achieved.
Using and comparing marketing knowledge with stored and updated ones the machining model is carried out, analyzed and on its basis are generated instructions regarding the progress of the machining process in order to obtain maximum competitiveness.
By modeling and simulations, the manager can decide if the order is accepted and control the machining system to satisfy the customer demands.
To achieve these objectives, the competitive control uses the reinforcement learning to get to know the market and the unsupervised on-line learning technique to get to know the machining system.
Note that we propose to give managers a knowledge management model, so that they can interact with the economic environment (market).
This knowledge management model represents a technical-economic model that can be used for competitive control of the manufacturing process without requesting experiments and based on the extraction of the knowledge from the previous experience.