Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

Lv, Shengping; Zheng, Binbin; Kim, Hoyeol; Yue, Qiangsheng

doi:10.1155/2018/1852938

Cited by 2 publications

(4 citation statements)

References 32 publications

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“…BPN is to establish a single BPN prediction model without pre-classification and takes the selected 16 attributes marked with " " in the column "All" of Table 4 as inputs. MSC-ANN [2] considered only required panel to classify the records and divide the samples into six groups. The FCM-GABPN w/o aggregation only applies the BPN to which the membership belonging is the highest and no BPN aggregation will be conducted.…”

Section: Resultsmentioning

confidence: 99%

“…If the number of final qualified set/unit (feeding set/unit minus the scrap set/unit) is larger than the demand number, it brings surplus sets/units; conversely, it causes supplemental feeding. On this basis, 30,117 samples of the orders were collected, multivariate boxplots [2] were conducted to detect the outliers, and, finally, 29,157 samples were left for this study. Performances of the proposed FCM-GABPN are compared to the other five approaches based on the same samples.…”

Section: Variables and Samplementioning

confidence: 99%

“…It is necessary to remove irrelevant and redundant attributes to reduce the complexity of analysis and the generated models, and also improve the efficiency of the whole modelling processes [2,32]. In this study, LC [20], MIC [21], RFE [22], LR [23], lasso regression [24], ridge regression [23], and RFR [24] seven feature selection approaches were employed to select critical attributes related to the scrap rate for each category of samples.…”

Section: Attributes Selection For Each Bpn Prediction Modelmentioning

confidence: 99%

“…The indicators surplus rate (Surpr) and supplemental feeding rate (Supfr) in the PCB template workshop were also considered. The predicted surplus rate (Surpr_Pd) and predicted supplemental feeding rate Supfr_Pd can be computed with Equations ( 10) and ( 11) in [2], respectively. The final performance is evaluated by the MSE, MAE, MAPE, Supfr_Pd, and Surpr_Pd.…”

Section: Performance Indicatorsmentioning

confidence: 99%

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An FCM–GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template

Xian

et al. 2019

Applied Sciences

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Accurate prediction of material feeding before production for a printed circuit board (PCB) template can reduce the comprehensive cost caused by surplus and supplemental feeding. In this study, a novel hybrid approach combining fuzzy c-means (FCM), feature selection algorithm, and genetic algorithm (GA) with back-propagation networks (BPN) was developed for the prediction of material feeding of a PCB template. In the proposed FCM–GABPN, input templates were firstly clustered by FCM, and seven feature selection mechanisms were utilized to select critical attributes related to scrap rate for each category of templates before they are fed into the GABPN. Then, templates belonging to different categories were trained with different GABPNs, in which the separately selected attributes were taken as their inputs and the initial parameter for BPNs were optimized by GA. After training, an ensemble predictor formed with all GABPNs can be taken to predict the scrap rate. Finally, another BPN was adopted to conduct nonlinear aggregation of the outputs from the component BPNs and determine the predicted feeding panel of the PCB template with a transformation. To validate the effectiveness and superiority of the proposed approach, the experiment and comparison with other approaches were conducted based on the actual records collected from a PCB template production company. The results indicated that the prediction accuracy of the proposed approach was better than those of the other methods. Besides, the proposed FCM–GABPN exhibited superiority to reduce the surplus and/or supplemental feeding in most of the case in simulation, as compared to other methods. Both contributed to the superiority of the proposed approach.

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Section: Resultsmentioning

confidence: 99%

Section: Variables and Samplementioning

confidence: 99%

Section: Attributes Selection For Each Bpn Prediction Modelmentioning

confidence: 99%

Section: Performance Indicatorsmentioning

confidence: 99%

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An FCM–GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template

Xian

et al. 2019

Applied Sciences

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Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0

et al. 2020

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Because of their cross-functional nature in the company, enhancing Production Planning and Control (PPC) functions can lead to a global improvement of manufacturing systems. With the advent of the Industry 4.0 (I4.0), copious availability of data, high-computing power and large storage capacity have made of Machine Learning (ML) approaches an appealing solution to tackle manufacturing challenges. As such, this paper presents a state-of-the-art of ML-aided PPC (ML-PPC) done through a systematic literature review analyzing 93 recent research application articles. This study has two main objectives: contribute to the definition of a methodology to implement ML-PPC and propose a mapping to classify the scientific literature to identify further research perspectives. To achieve the first objective, ML techniques, tools, activities, and data sources which are required to implement a ML-PPC are reviewed. The second objective is developed through the analysis of the use cases and the addressed characteristics of the I4.0. Results suggest that 75% of the possible research domains in ML-PPC are barely explored or not addressed at all. This lack of research originates from two possible causes: firstly, scientific literature rarely considers customer, environmental, and human-in-the-loop aspects when linking ML to PPC. Secondly, recent applications seldom couple PPC to logistics as well as to design of products and processes. Finally, two key pitfalls are identified in the implementation of ML-PPC models: the complexity of using Internet of Things technologies to collect data and the difficulty of updating the ML model to adapt it to the manufacturing system changes.

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Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

Cited by 2 publications

References 32 publications

An FCM–GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template

An FCM–GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template

Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0

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