Classification knowledge discovery in mold tooling test using decision tree algorithm

Yeh, Duen-Yian; Cheng, Ching‐Hsue; Hsiao, Shih-Chuan

doi:10.1007/s10845-009-0321-7

Cited by 21 publications

(7 citation statements)

References 27 publications

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“…Generally speaking, this practice can improve or worsen the mold's performance, so validating the CAD models through a Computer-Aided Engineering (CAE) stage is necessary to improve cooling parts, plastic flow, robustness, and others. As is mentioned in [29], each mold designed has its process and specific knowledge. The initial stage design defines costs, delivery time, and mold manufacturability [30].…”

Section: Methodology To Implement Cae Validationmentioning

confidence: 99%

Methodology to implement CAE validation in repair & redesign parts process of plastic injection molds

et al. 2022

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Repairs and redesigns for plastic injection molding parts generally is a short and straightforward process consisting of inspection, modeling, and evaluation. The present paper implements a methodology for redesigning and repairing injection molding parts based on the frontal process for developing concepts. The proposed methodology helps in the validation through Computer-Aided Engineering (CAE) in the designing stage using CAD software, ensuring the quality of the repair. Furthermore, the redesigned development has been carried out in the best way to obtain a better cooling, robustness, or plastic flow. In this research is implemented the proposed methodology in a Hot Runner System. Furthermore, a numerical simulation for three cases to evaluate the heat transfer and cooling times performed, finding the main differences in heat transfer due to drilled or milled rectangular channels, minimizing the time to reach ejection temperature and mold/part temperatures.

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Section: Methodology To Implement Cae Validationmentioning

confidence: 99%

Methodology to implement CAE validation in repair & redesign parts process of plastic injection molds

et al. 2022

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“…The above steps are repeated for each attribute. We select the attribute with the largest information gain as the attribute branch (Yeh et al , 2011; Huang and Lin, 2013): …”

Section: Methodsmentioning

confidence: 99%

Applying data mining methodology to establish an intelligent decision system for PCBA process

Huang

Ruano

Chen

et al. 2019

SSMT

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Purpose This paper aims to consider the practical production environment of electronics manufacturing industry firms, and the large quantities of information collected on machine processes, testing data and production reports, while simultaneously taking into account the properties of the processing environment, in conducting analysis to obtain valuable information. Design/methodology/approach This research constructs a prediction model of the circuit board assembly process yield. A decision tree is used to extract the key attributes. The authors also integrate association rules to determine the relevance of key attributes of undesirable phenomena. Findings The results assure the successful application of the methodology by reconfirming the rules for solder skip and short circuit occurrence and their causes. Originality/value Measures for improvement are recommended, production parameters determined and debugging suggestions made to improve the process yield when the new process is implemented.

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“…Figure 2 depicts the construction of the decision tree, which consists of the root node (R), the internal nodes (A), and the leaf nodes (B), and all nodes are connected via branches. The root node has logic statements that help to determine the flow of the decisions, and each internal node identifies a new decision path based on new logic statements before reaching to the leaf node that expresses the final decision or the predicted response by considering a numeric class [25,26].…”

Section: Decision Trees (Dts)mentioning

confidence: 99%

Determination of Transformers’ Insulating Paper State Based on Classification Techniques

Ghoneim

2021

Processes

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The continuity of transformer operation is very necessary for utilities to maintain a continuity of power flow in networks and achieve a desired revenue. Most failures in a transformer are due to the degradation of the insulating system, which consists of insulating oil and paper. The degree of polymerization (DP) is a key detector of insulating paper state. Most research in the literature has computed the DP as a function of furan compounds, especially 2-furfuraldehyde (2-FAL). In this research, a prediction model was constructed based on some of most periodical tests that were conducted on transformer insulating oil, which were used as predictors of the insulating paper state. The tests evaluated carbon monoxide (CO), carbon dioxide (CO2), breakdown voltage (VBD), interfacial tension (IF), acidity (ACY), moisture (M), oil color (OC), and 2-furfuraldehyde (2-FAL). The DP, which was used as the key indicator for the paper state, was categorized into five classes labeled 1, 2, 3, 4, and 5 to express the insulating paper normal aging rate, accelerating aging rate, excessive aging danger zone, high risk of failure, and the end of expected life, respectively. The classification techniques were applied to the collected data samples to construct a prediction model for the insulating paper state, and the results revealed that the fine tree was the best classifier of the data samples, with a 96.2% prediction accuracy.

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Classification knowledge discovery in mold tooling test using decision tree algorithm

Cited by 21 publications

References 27 publications

Methodology to implement CAE validation in repair & redesign parts process of plastic injection molds

Methodology to implement CAE validation in repair & redesign parts process of plastic injection molds

Applying data mining methodology to establish an intelligent decision system for PCBA process

Determination of Transformers’ Insulating Paper State Based on Classification Techniques

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