ECO-Case Based Reasoning Tool for Environmental Product Design

Case Based Reasoning(CBR)is one of the artificial intelligent methodologies that is widely used in problem solving by reusing the most similar previous experiences stored in the library.A framework of ECO-CBR for Life Cycle Assessment data collection has been used and the process was carried out using SolidWorks program.The practicality of the tool has been validated using case study,which then provides solution.The output enable researchers to determine forecast error and forecast accuracy,by valuing the calculation from Total Carbon Footprint,Energy Consumption,Air Acidification,and Water Eutrophication.ECO-CBR is able to assist designers in product design.Due to the limitation of environmental impact consideration in product sustainability,there is a demand to propose a tool that can assist designers to reduce environmental impact of product design at early stage.The model works as an essential guideline to lessen repeated mistakes in the product development process and help designers measure the risks before concluding ideal decisions.Minor errors that occur through the study showed that ECO-CBR is reliable to be implemented in order to find a better solution.

Data Mining and Neural Network Techniques in Case Based System

This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.

Analyzing and Designing Business Processes Management System Based on Transformation of Instantiated Ontology’s State

The paper discusses efforts of finding a simple and transparent method to analyze business processes and developing its management system architecture. By introducing ontology and its state, the approach tried to find a unified representation and a flexible choreography of business processes. The main idea of the paper is the transformation of ontology＇s states, which are the most important scenarios of enterprises. The business activity composition, that is, case composition based on an AI technique, Case Based Reason （CBR）, which is to solve new problems by retrieving solutions to previous problems, and then store the modified solution. The main interest in CBR relies on that it allows a system to avoid past failures and exploit past successes.

Case-based Reasoning in Conflict Negotiation in Concurrent Engineering

Case based reasoning (CBR) is a kind of analogous reasoning that is widely used in artificial intelligence. Conflicts are pervasive in Concurrent Engineering design environment. Conflict negotiation is necessary when conflicts occur. It is difficult to resolve conflicts due to several reasons. An approach to resolving conflicts by case based reasoning is proposed in this paper. The knowledge representation of conflict negotiation cases, the judgment of case similarity, the retrieval model of cases, the management of case bases, and the process of case based conflict negotiation are studied. The implementation structure of the Case based Conflict Solving System (CCSS) is also given.

Modeling of Additive Manufacturing Process Relevant Feature in Layer Based Manufacturing Process Planning

Compared with general machining processes,additive manufacturing(AM) process has stabler planning route and limited process variables and this makes it to be more easily designed and planned with knowledge based systems and computer aided techniques.Case based reasoning approach is applied to the process planning of additive manufacturing in this paper.The concept of "AM process relevant design features" is proposed after the analysis of the characteristics of AM processes.The concept is used as the basis of the knowledge representation,and AM relevant feature graph is used as the case representation schema.The case retrieval method is discussed based on this graph.The case representation of a machine arm is given to illustrate the brief process of the proposed approach.

Automated process parameters tuning for an injection moulding machine with soft computing

In injection moulding production,the tuning of the process parameters is a challenging job,which relies heavily on the experience of skilled operators.In this paper,taking into consideration operator assessment during moulding trials,a novel intelligent model for automated tuning of process parameters is proposed.This consists of case based reasoning (CBR),empirical model (EM),and fuzzy logic (FL) methods.CBR and EM are used to imitate recall and intuitive thoughts of skilled operators,respectively,while FL is adopted to simulate the skilled operator optimization thoughts.First,CBR is used to set up the initial process parameters.If CBR fails,EM is employed to calculate the initial parameters.Next,a moulding trial is performed using the initial parameters.Then FL is adopted to optimize these parameters and correct defects repeatedly until the moulded part is found to be satisfactory.Based on the above methodologies,intelligent software was developed and embedded in the controller of an injection moulding machine.Experimental results show that the intelligent software can be effectively used in practical production,and it greatly reduces the dependence on the experience of the operators.