Rough sets and maintenance in a production line

Municio, Ángel Manuel Gento; Castán, Alfonso Redondo

doi:10.1111/1468-0394.00251

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…The function and usage of W QR resemble the Risk Priority Number (RPN) in FMEA (see Arccidiacono and Campatelli 16 and Srikesh and Robert 17 ). We propose an approach to satisfy these requirements by means of the data mining method of Rough Set (see Angel et al 18 and Zeng 19 ) and the multiattribute decision-making technique of Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) (see Facchinetti 20 and Braglia 21 ) based on the quality data collected. Benefited from the advantages of rough set and Fuzzy TOPSIS, the approach can fulfill objective and systematic assessment of each node from macro indexes and micro characters both based on the original quality data.…”

Section: Relational Tree Of Ctqspmentioning

confidence: 99%

Technical decomposition approach of critical to quality characteristics for Product Design for Six Sigma

He¹,

Tang²,

Chang³

2009

Quality & Reliability Eng

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Product Design for Six Sigma (DFSS) approach is a structural and disciplined methodology driven by critical to quality characteristics (CTQs). How to identify and decompose the CTQs is the kernel part in the DFSS process. Traditional method only depends on the quality function deployment (QFD) matrix to flow down CTQs roughly. The paper puts forward a novel technical approach for CTQs decomposition from customer requirements into critical technical parameters based on the relational tree. Specifically, this approach emphasizes the systematic process and quantitative computation on quality relation weight. In order to specify the object of product DFSS, the connotation and evolution model of CTQs are created first. Then along the product development process, a decomposition measure for relational tree of CTQs is studied based on the functional and physical trees in Axiomatic Design (AD). And the quality relation weight computation of its nodes by means of Rough Set and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is explored. Finally, an application on a car body noise vibration harshness (NVH) improvement, as an example, is given, and the decomposition process of NVH related with the functional and physical trees as well as its node weights computation algorithm are expounded in detail.

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Section: Relational Tree Of Ctqspmentioning

confidence: 99%

Technical decomposition approach of critical to quality characteristics for Product Design for Six Sigma

He¹,

Tang²,

Chang³

2009

Quality & Reliability Eng

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“…The preventive maintenance actions increase the availability of the machines over the long term reducing sudden machine breakdowns (Gento and Redondo 2003).…”

Section: Introductionmentioning

confidence: 99%

Adaptive policy of buffer allocation and preventive maintenance actions in unreliable production lines

Renna

2018

J Ind Eng Int

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The buffer allocation problem is an NP-hard combinatorial optimization problem, and it is an important design problem in manufacturing systems. The research proposed in this paper concerns a product line consisting of n unreliable machines with n − 1 buffers and a preventive maintenance policy. The focus of the research is to obtain a better trade-off between the buffer level and the preventive maintenance actions. This paper proposes a dynamic control of the buffers' level and the interval between two consecutive preventive actions. The set of the parameter of the proposed policy allows choosing the reduction in the costs or the increment of the throughput rate. A simulation model is developed to test the proposed model to the solution proposed in the literature. The proposed policy leads to better results in terms of total costs reduction keeping high production rate, while the design of a fixed level of buffer works better for lower production rates required.

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“…Another important method of increasing production line throughput is to improve the availability of machines through preventive maintenance (Patchong & Willaeys, 2001;Northworthy & Abdul-Kader, 2004). Preventive maintenance is a proactive approach to increase the reliability of machines over the long term by scheduling planned maintenance actions aimed at the reduction or prevention of sudden breakdowns and failures (Gento & Redondo, 2003).…”

Section: Introductionmentioning

confidence: 99%

Flexibility configurations and preventive maintenance impact on job-shop manufacturing systems

Renna¹

2017

10.5267/j.ijiec

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Manufacturing systems need to be able to work under the dynamic and uncertain production environment. Machine and routing flexibility combined with preventive maintenance actions can improve the performance of the manufacturing systems under dynamic conditions. This paper evaluates different levels of machine and routing flexibility combined with different degrees of preventive maintenance policy. The performance measures considered are throughput, work in process and throughput. The performance measures are compared with a system without any flexibility and no preventive maintenance actions. Different levels of flexibility and preventive maintenance actions are examined under a simulation environment. The simulation results highlight more important factors for the performance measures and the best combination of the factors to improve the performance.

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Rough sets and maintenance in a production line

Cited by 8 publications

References 28 publications

Technical decomposition approach of critical to quality characteristics for Product Design for Six Sigma

Technical decomposition approach of critical to quality characteristics for Product Design for Six Sigma

Adaptive policy of buffer allocation and preventive maintenance actions in unreliable production lines

Flexibility configurations and preventive maintenance impact on job-shop manufacturing systems

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