Quality prediction in manufacturing system design

Nada, Omayma A.; ElMaraghy, Hoda A.; ElMaraghy, Waguih

doi:10.1016/s0278-6125(08)00002-2

Cited by 32 publications

(9 citation statements)

References 12 publications

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“…This study emphasizes the use of poka-yoke devices (PD), a LP practice especially tailored to eliminate the production of defective parts, complementing statistical process control techniques [3,4]. Since this term started being disseminated in the West in the context of spreading the Toyota Production System [5], PD has been a topic of interest, mostly among practitioners, due to the apparent simplicity of how they function and their intuitive design features [6].…”

Section: Introductionmentioning

confidence: 99%

A framework for assessing poka-yoke devices

Saurin

Ribeiro

Vidor

2012

Journal of Manufacturing Systems

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Section: Introductionmentioning

confidence: 99%

A framework for assessing poka-yoke devices

Saurin

Ribeiro

Vidor

2012

Journal of Manufacturing Systems

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“…Second, in terms of the highest TS, the error distributions of the deep framework are concentrated in the range of less than 5% (accounting for 90%) and 10% (accounting for 100%) for Case 1, and 5% (accounting for 92%, 92.5%) and 10% (accounting for 99.5%, 100%) for Case 2. However, the shallow architectures have good performance in TS 1 and worse performance in TS 5 and TS 10 compared with deep learning. Third, in terms of the PCC, the degree of correlation is higher using the deep framework (passed the correlation test at 0.01 (SAE) and 0.05 (DRBM) levels) than that using the shallow architecture.…”

Section: Comparison Studiesmentioning

confidence: 99%

“…Statistical quality control [2] based on cause-effect relationships, e.g., linear regression [3], non-linear regression [4], inference learning [5], and expert systems [6], has been widely used to assess the quality performance of manufacturing processes. The successful application of these approaches is attributed to certain stable or constant production processes, which thus makes them unsuitable for the fast-increasing complexity and high-dimensionality of modern manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

et al. 2017

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Abstract:Under the international background of the transformation and promotion of manufacturing, the Chinese government proposed the "Made in China 2025" strategy, which focused on the improvement of a quality-based innovation ability. Moreover, predicting manufacturing quality is one of the crucial measures for quality management. Accurate prediction is closely related to the feature learning of manufacturing processes. Therefore, two categories of intelligent learning approaches, i.e., shallow learning and deep learning, are investigated and compared for manufacturing quality prediction in this paper. Specifically, the feed forward neural network (FFNN) with one hidden layer and the least squares support vector machine (LSSVM) with no hidden layers are selected as the representatives for shallow learning, and the deep restricted Boltzmann machine (DRBM) and the stack autoencoder (SAE) are chosen as the representatives for deep learning. The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China. The experiments show that the deep framework overwhelms the shallow architecture in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. In addition, the prediction results also indicate that the performances depend on the length of the training data. That is, the bigger the sample size is, the better the performance is.

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“…and many methods to solve these problems such as Progressive Approach (PA) (Lan, 2007), A hierarchical Fuzzy Inference System (FIS) (Nada, Elmaraghy & Elmaraghy, 2006), swarm optimization (PSO) algorithm (Kuo et al, 2011), moderately robust optimization approach (Nazarian & Ko, 2013). But, each studied problem represents one step of production line design.…”

Section: Related Workmentioning

confidence: 99%

Manufacturing system design based on axiomatic design: Case of assembly line

Triki

Hachicha²,

Masmoudi

2017

JIEM

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Abstract:Purpose: In this paper, a combined Production Line Design (PLD) process which includes many design aspects is presented, developed and validated.Design/methodology/approach: The PLD process is based on the SADT (Structured Analysis and Design Technique) diagram and the Axiomatic Design (AD) method. Findings:The results of the validation indicated that the production line designed by this process is outperformed the initial line of the company.Practical implications: For a purpose of validation, this proposed process has been applied in a manufacturing company and it has been validated by simulation. Originality/value:Recently, the problems of production line design (PLD) have attracted the attention of many researchers. However, only a few studies have treated the PLD which includes all design aspects. In this work, a combined PLD porcess is presented. It should be noted that the proposed process is simple and effective.

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Quality prediction in manufacturing system design

Cited by 32 publications

References 12 publications

A framework for assessing poka-yoke devices

A framework for assessing poka-yoke devices

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

Manufacturing system design based on axiomatic design: Case of assembly line

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