Assembly system synthesis using association rule discovery

ElMaraghy, Hoda A.; Kashkoush, Mohamed

doi:10.1007/s00170-015-7304-y

“…AlGeddawy and ElMaraghy (2010) developed a co-evolutionary model using cladistics adopted from biology to discover relations between product features and manufacturing capabilities. Kashkoush and ElMaraghy proposed an integer programming model for the synthesis of manufacturing and assembly system, respectively (ElMaraghy and Kashkoush, 2015; Kashkoush and ElMaraghy, 2017). This single-stage approach was implemented easily through mathematical programming.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A case study done by ElMaraghy and Kashkoush (2015) is adopted to demonstrate the proposed ARM method. This case uses real historical data about seven different variants of automotive engine belt tensioners/idlers and the corresponding assembly system used to assemble each tensioner/idler variant.…”

Section: The Case Studymentioning

confidence: 99%

“…Eight main system components are used to describe the corresponding seven assembly systems, including level of automation, axial press, twist press, CNC three-axis milling machine (if surface finish is needed), conveyor, screw driver, feeder, and placer. The detailed data of assembly systems and the selected product variants can be found in the literature (ElMaraghy and Kashkoush, 2015). Data are encoded in binary format acceptable by CGSA-ARM.…”

Section: The Case Studymentioning

confidence: 99%

“…Some researchers have recently addressed the particular problem in this study. Kashkoush and ElMaraghy proposed an integer programming model for the synthesis of manufacturing and assembly system, respectively (ElMaraghy and Kashkoush, 2015; Kashkoush and ElMaraghy, 2017). The proposed model was used to reveal the relationships between system components and product features.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Association rule mining using chaotic gravitational search algorithm for discovering relations between manufacturing system capabilities and product features

Kou

¹

2019

Concurrent Engineering

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An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for efficient and cost-effective product development and production. This article proposes a chaotic gravitational search algorithm–based association rule mining method for discovering the hidden relationship between manufacturing system capabilities and product features. The extracted rules would be utilized to predict capability requirements of various machines for the new product with different features. We use two strategies to incorporate chaos into gravitational search algorithm: one strategy is to embed chaotic map functions into the gravitational constant of gravitational search algorithm; the other is to use sequences generated by chaotic maps to substitute random numbers for different parameters of gravitational search algorithm. In order to improve the applicability of chaotic gravitational search algorithm–based association rule mining, a novel overlapping measure indication is further proposed to eliminate those unuseful rules. The proposed method is relatively simple and easy to implement. The rules generated by chaotic gravitational search algorithm–based association rule mining are accurate, interesting, and comprehensible to the user. The performance comparison indicates that chaotic gravitational search algorithm–based association rule mining outperforms other regular methods (e.g. Apriori) for association rule mining. The experimental results illustrate that chaotic gravitational search algorithm–based association rule mining is capable of discovering important association rules between manufacturing system capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

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“…AlGeddawy and ElMaraghy [16] developed a coevolutionary model using cladistics adopted from biology to discover relations between product features and manufacturing capabilities. Kashkoush and ElMaraghy [17,18] proposed an integer programming model for the synthesis of manufacturing and assembly system, respectively. The proposed model was used to reveal relationships between system components and product features.…”

Section: Introductionmentioning

confidence: 99%

Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

Kou

¹

,

Xi

²

2018

Mathematical Problems in Engineering

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An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

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