A process planning system with feature based neural network search strategy for aluminum extrusion die manufacturing

Aluminum extrusion die manufacturing is a critical task for productive improvement and increasing potential of competition in aluminum extrusion industry. It causes to meet the efficiency not only consistent quality but also time and production cost reduction. Die manufacturing consists first of die design and process planning in order to make a die for extruding the customer's requirement products. The efficiency of die design and process planning are based on the knowledge and experience of die design and die manufacturer experts. This knowledge has been formulated into a computer system called the knowledge-based system. It can be reused to support a new die design and process planning. Such knowledge can be extracted directly from die geometry which is composed of die features. These features are stored in die feature library to be prepared for producing a new die manufacturing. Die geometry is defined according to the characteristics of the profile so we can reuse die features from the previous similar profile design cases. This paper presents the CaseXpert Process Planning System for die manufacturing based on feature based neural network technique. Die manufacturing cases in the case library would be retrieved with searching and learning method by neural network for reusing or revising it to build a die design and process planning when a new case is similar with the previous die manufacturing cases. The results of the system are dies design and machining process. The system has been successfully tested, it has been proved that the system can reduce planning time and respond high consistent plans

Multilevel modelling of sustainable consumption for achieving carbon neutral production – case study of the urban in Thailand

Purpose: The research aims to determine a living model that is eco-environmental and sustainable. Climate change and scarcity of resources have led world policymakers, along with the United Nations, to set guidelines in the form of 17 sustainable development goals (SDGs). Sustainable production consumption is one of the major issues that is considered worldwide, including the SDG mission of the University ranking under SDG12. Design/methodology/approach: The paper proposes a new model development for sustainable consumption production using multi-criteria decision-making of the Fuzzy Analytical Hierarchy Process (FAHP) under the 5P principle. The questionnaire was designed and distributed to sample populations in the community, and the analysis was done under the FAHP procedure. The research area focused on green space near Bangkok, Bang Kachao. It is one of six local governmental units (Tambon) in Phra Pradaeng district located in Samut Prakran province, Thailand. Findings: It was found out that people concerned the most sustainable production consumption using natural local materials at 25.09%, followed by making community products by green industry at 12.42% and making local green products at 6.18%. From the development of the multi-modelling framework, the paper proposes a new model of the urban community in Thailand for sustainable production consumption to support SDG12 using FAHP for multi-decision making based on the 5P principle. There are people, porosity, planet, peace, and partnership. Research limitations/implications: However, various factors influence production and consumption and impact the carbon footprint. Practical implications: It was obviously found that people mostly use local materials to make green local products under the local policy of 3R waste management. Innovative design uses community wisdom, knowledge and know-how to make value-added products. Originality/value: The strategic planning and control consist of green industry, zero waste management, zero carbon footprint and innovative product design. The expected outputs from the model are green and homemade products with cleaner production, clean energy, and gain carbon credit

Combining Axiomatic Design and Case-Based Reasoning in a Design Methodology of Mechatronics Products

Organised by: Cranfield UniversityCurrent market environments are volatile and unpredictable. The ability for design products to meet customer’s requirements has become critical to success. The key element to develop such products is identifying functional requirements and knowledge utilization based on a scientific approach to provide both designers of new products and redesigners of existing products with a suitable solution that meets to customer’s needs. This paper presents a method to (re)design mechatronic products by combining the axiomatic design and case-based reasoning approaches. Innovation has increased the new product value, which has improved the product efficiency and the need for new engineered design method.Mori Seiki – The Machine Tool Compan

Case-based reasoning for adaptive aluminum extrusion die design together with parameters by neural networks

Global Product Development 2011, Part 13, 491-496, DOI: 10.1007/978-3-642-15973-2_50 ISBN 978-3-642-15972-5International audienceNowadays Aluminum extrusion die design is a critical task forimproving productivity which involves with quality, time and cost. Case-Based Reasoning (CBR) method has been successfully applied to support the die design process in order to design a new die by tackling previous problems together with their solutions to match with a new similar problem. Such solutions are selected and modified to solve the present problem. However, the applications of the CBR areuseful onlyretrievingprevious features whereas the critical parameters are missing. In additions, the experience learning to such parameters are limited. This chapter proposes Artificial Neural Network(ANN) to associate the CBR in order to learning previous parameters and predict to the new die design according to theprimitive die modification. The most satisfactory is to accommodate the optimal parameters of extrusion processes

Prediction of the process capability for compression rubber part forming in the automotive supply chain

Purpose: The paper proposes predicting production process capability for the compression rubber part in automotive supply chain management. Delivery of parts to tier 1 and OEM on time is the most important part of supply chain management, together with the delivery of on-quality and on-cost control to maintain the competitiveness of the supply chain. There are many suppliers to produce many automotive parts for tier 1. Therefore, the simulation approach properly predicts and prevents the process from getting into trouble during the actual production time. Production process quality control is critical to ensure that the good quality of the parts purchased can be delivered on time. Rubber parts are used widely in automotive, motorcycles, trucks, and other types of vehicles, with small sizes but in huge quantities to support general OEM brands and specific parts. The rubber part manufacturing process is complex and uncertain with compression moulding and rubber curing conditions. Therefore, good conditions can predict the production process's capability to commission and deliver on schedule. Design/methodology/approach: A neuro-fuzzy system is adopted and developed to deal with the uncertain process capability under multi-criteria decision-making. Findings: The methodology development can be used in the actual rubber part manufacturing supply chain environment and can predict uncertain problems that might occur in the subcontractor factories. Research limitations/implications: The prediction of the production process capability of the rubber part supply chain might be more effective on the real-time monitoring control system and can be tracking location part progress for further planning both success or rescheduling. Practical implications: The platform can be applied to audit and test the actual industrial supply chain, and problem and research questions are brought about from the real supply chain in the local country. Originality/value: The methodology development was originally created for the particular supply chain in rubber automotive parts that can replace the existing system to obtain a more efficient performance evaluation process