Part Agent Advice for Promoting Reuse of the Part Based on Life Cycle Information

Hiraoka, Hiroyuki; Ueno, Tatsuro; Kato, Kei; Ookawa, Hiroshi; Arita, Masayuki; Nanjo, Keisuke; Kawaharada, Hiroshi

doi:10.1007/978-981-4451-48-2_55

Cited by 13 publications

(2 citation statements)

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“…Predicting the occurrence of failure is one of the most important and the most difficult tasks. Considering its probabilistic nature, we applied a Bayesian network to the failure model that represents causal relations between the failure of the part with the operations of its user and the current status of the part [6]. The system estimates possibility of failures based on observed events such as detected status of the part, user inputs and other detected events.…”

Section: Qualitative Prediction Of Failuresmentioning

confidence: 99%

Replacement of Parts by Part Agents to Promote Reuse of Mechanical Parts

Hiraoka

Nagasawa

Fukumashi

et al. 2017

Product Lifecycle Management and the Industry of the Future

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Replacement of parts is a crucial task for the reuse of parts. In this paper, functions of part agent we are developing that support the reuse are described. A part agent manages life cycle of a part using a network agent and an RFID tag attached on the part and generates advices for the user on the maintenance of the part in order to promote the reuse. Part agent we are developing to support the replacement has the following functions. First function is to detect deterioration of the part from its sensory data. As an example, simple 3dof manipulator is developed with modularized link structure. Part agent is assigned for each module and generates advice on its replacement. Second function is to calculate the possibility of failures of part based on causal relation among user operations and detected events. Third function is to support users to disassemble used parts. Part agent generates disassembly procedure of the part and shows it to the user using augmented reality technique. In this paper, the planned scheme for part agent to support reuse of part with these functions is described and preliminary result of the development is shown.

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Section: Qualitative Prediction Of Failuresmentioning

confidence: 99%

Replacement of Parts by Part Agents to Promote Reuse of Mechanical Parts

Hiraoka

Nagasawa

Fukumashi

et al. 2017

Product Lifecycle Management and the Industry of the Future

Self Cite

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“…Using a multi-agent simulation, a method for assembly of reused parts was developed [13]. Also, a framework for a part agent to promote recovery of parts was developed [14]. To achieve an efficient and effective decision in life cycle engineering and assessment, decision support was also researched and developed [15].…”

Section: Introductionmentioning

confidence: 99%

Data driven decision-making methodology : a study of repair decision in remanufacturing

Kanchanasri¹

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Decisions on recovering a low volume product such as a High Pressure Compressor Stage 3 Stator Vane (HPC Stg 3) could make a significant difference in cost saving. By a traditional assessment method considering only physical conditions of End-Of-Life parts, the cost of repairing the end-of-life parts is costly when comparing to replacement with spare parts due to dynamic resource constraints, human and machine limitations. This research is focused on investigating and developing an approach to solving the difficulty of repair decisions under various constraints. The main objective of this work is to provide quantitative decision rules for justification of the repairing of the returned parts. The proposed method consists of a data driven decision-making model that, given a set of parameters, determines the optimal damage point. The application of the system to possible scenarios is demonstrated through numerical studies. By comparing execution time, the proposed model solving by the linear programming provides a better optimal solution than the evolutionary approach. Taking more execution time, the evolution approach can also provide the optimal solution as good as by the linear programming. The numerical study demonstrates how to apply the proposed decision-making model to assess the acceptability of End-Of-Life parts and enable identification of the optimal solution. The optimal solution shows the significant cost saving when comparing repair and replacement options. In this research, there are three major accomplishments which are (1) an investigation of non-trivial decision-making factors, (2) a simulation model of End-Of-Life part repair decision constructed by using ARENA® to examine the concept of part sentencing in remanufacturing and (3) the development of a decision-making model to minimize repair and inventory costs by integrating the concept of data driven decision-making and optimization techniques i.e. the linear programming and the evolutionary approach. This work addresses the repair decision of returned parts. The research concept can be deployed to various industrial applications which include (1) multinational original equipment manufacturers in areas such as aerospace, automotive and marine regarding restoration of components to serviceable condition and remanufacturing of products, (2) small and medium enterprises about improvements of v remanufacturing operational process and (3) optimization of remanufacturing for implementation in various industries. vi Acknowledgements The very first person whom I will like to acknowledge is Her Royal Highness Princess Maha Chakri Sirindhorn together with Professor Chua Chee Kai and Professor Ang Wei Tech who gave me an opportunity to pursue Ph.D. at Nanyang Technological University. I would like to express my gratitude to my advisor, Professor Moon Seung Ki. His guidance and support have made this work done. I will always be thankful for Professor Moon's confidence in me since he accepted me as his doctoral student, a time when I knew nothing about the re...

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