Innovative Production Machines and Systems Conference - Intelligent Decision Support Systems

Data mining sales data for Kansei Engineering

Coleman — Mon, 02 Jul 2007 13:28:30 +0000

In Kansei Engineering (KE), customers are asked for their emotional response to a range of products. KE typically produces 3 dimensional data with customers, products and emotional response via semantic scales as the dimensions. Analysing the customer responses can yield insight into the importance of design factors and the relationships between emotional response and design factors. These relationships are the key to the importance of KE in the design process and in providing a broad portfolio of products. The range of products to be assessed in the KE is best selected using a designed experiment of design factors.

Self-organising spiking neural networks trained by weight- and delay-adaptation methods for control chart pattern recognition

charles — Mon, 02 Jul 2007 10:49:52 +0000

Spiking neural networks (SNNs) are being utilised for solving wide range of problems due to their superior computational power. The connections in SNNs are associated either with a weight values as in the case of a conventional Multi Layer Perceptron (MLP) or with a delay value similar to the Time Delay Neural Network (TDNN). In this paper, two self-organising spiking neural network learning models, namely Self-Organising Weight-Adaptation Spiking Neural Network (SOWA_SNN) and Self-Organising Delay-Adaptation Spiking Neural Network (SODA_SNN) are described. It was shown that these models cluster data sets efficiently and with high accuracy. Their performance was also showed to be comparable or better than conventional self organising neural networks commonly known as the Kohonen Self-Organising Maps (SOMs).

Hybrid Decision Support and Justification Methods for Production System Selection

Juhani Heilala — Fri, 22 Jun 2007 13:17:48 +0000

This article presents different decision support and justification method principle for production system selection and development. Focus is on the analytic approaches, discrete event simulation, queue theories and linear programming, the other approcahes, strategic and economics are only listed, even if they are also of great importance. The benefit of discrete event simulation is discussed and also reasons not to use simulation is shown. The analytic methods, queue theories, linear programming, i.e. optimisation are useful and can be used before or parallel to simulation modelling. Use of different methods is compared and compination of different methods, i.e hybrid methods is discussed.

A knowledge diagnostic system for product defects

wongky — Fri, 22 Jun 2007 13:17:48 +0000

The need to fulfill customer satisfaction and increase product quality has motivated many manufacturing firms to investigate and diagnose their product failure. To gain a correct and accurate diagnostic, the entire processing root must be recorded and controlled in every step of the manufacturing process. In this research, a prototype system has been developed for a tile manufacturing company to diagnose tile defects and to recommend actions for improvement. This system consists of two main components, the knowledge base and inference engine. The knowledge base has been developed by capturing data and information that are related to tile defects, such as symptoms, probable causes, types of defects, processes, sub processes, tile classifications, etc.

Assessment of knowledge management activities in manufacturing

oztemel2007 — Fri, 22 Jun 2007 13:17:47 +0000

Knowledge management is one of the most important factors in achieving competitive advantage. In this study, first of all knowledge and knowledge management concepts are discussed and then a set of criteria for assessing effectiveness of the knowledge management activities as well as an assessment model are then described.

Supply chain intelligence

Nenad — Fri, 22 Jun 2007 13:17:46 +0000

Supply chains are complex systems with silos of information that is very difficult to integrate and analyze. In order to effectively analyze these disparate systems, we propose the use of Supply Chain Intelligence (SCI). This paper briefly describes challenges, issues, and trends related to supply chain intelligence. It presents SCI architecture and supply chain metamodel for modelling any supply chain network. The basis of the SCI lifecycle and dimensional modelling are described. Finally, SCI analytical solution based on OLAP technologies and web portal that enables companies to combine and

Computer aided process planning based on ontology and rules system applied to forging domain

Thibault — Fri, 22 Jun 2007 13:17:46 +0000

In order to reduce product and process plan design time and cost, the integration of the relations that exist between product data and manufacturing data is essential. This paper proposes an original approach to support this integration, mix of rule based and classification approaches with ontologies. This approach has been implemented in a computer tool which allows the formalization and the use of manufacturing knowledge, particularly to aid the design of process plan in the forging manufacturing domain.