Industrial Quality Prediction System through Data Mining Algorithm

Based on an assessment of production capabilities, manufacturing sectors' core competency is increased. The importance of product quality in this aspect cannot be overstated. Several academics have introduced Deming's 14 principles, Shewhart cycle, total quality management, and other approaches to decrease the external failure costs and enhance product yield rates. Analysis of industrial data and process monitoring is becoming increasingly important as a part of the Industry 4.0 paradigm. In order to reduce the internal failure cost and inspection overhead, quality control (QC) schemes are utilized by industries. The final product quality has an interactive and cumulative effect of various parameters like operators and equipment in multistage manufacturing processes (MMP). In other cases, the final product is inspected in a single workstation with QC. It's challenging to do a cause analysis in MMP whenever a failure occurs. Several industries are looking for the optimal quality prediction model in order to achieve flawless production. The majority of current approaches solely handles single-stage manufacturing and is inadequate in dealing with MMP quality concerns. To overcome this issue, this paper proposes an industrial quality prediction system with a combination of multiple Program Component Analysis (PCA) and Decision Stump (DS) algorithm for MMP quality prediction. A SECOM (SEmiCOnductor Manufacturing) dataset is used for verification and validation of the proposed model. Based on the findings, it is clear that this model is capable of performing accurate classification and prediction in the field of industrial quality.

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Production rescheduling through product quality prediction

Procedia Manufacturing ◽

10.1016/j.promfg.2021.07.022 ◽

2021 ◽

Vol 54 ◽

pp. 142-147

Author(s):

Maik Frye ◽

Dávid Gyulai ◽

Júlia Bergmann ◽

Robert H. Schmitt

Keyword(s):

Product Quality ◽

Quality Prediction

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Model and optimal operational windows for hydrodynamic fiber fractionation

Nordic Pulp & Paper Research Journal ◽

10.1515/npprj-2020-0059 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Thomas Schmid ◽

Stefan Radl

Keyword(s):

Product Quality ◽

Energy Demand ◽

Optimization Procedure ◽

Product Yield ◽

Short Fiber ◽

Design Tool ◽

Quality Product ◽

Fractionation Model ◽

Micro Hole ◽

Fiber Fractionation

AbstractBased on fitted experimental data, an empirical fractionation model for mini-channel hydrodynamic fiber fractionation (miniFrac) is presented. This model, combined with an optimization procedure, is then used as a design tool to synergize competing fractionation performance characteristics, i. e., product quality, product yield and energy demand. Based on this model, miniFrac is compared to state-of-the-art fiber fractionation technology with respect to (i) long fiber-short fiber fractionation and (ii) fines-fiber fractionation. In terms of fines-fiber fractionation, miniFrac is outperformed by typical micro-hole pressure screening regarding the purity of fines fraction. However, a comparison with a slotted (slot width of 0.2 mm) and a smooth-holed pressure screen (hole diameter of 0.8 mm) shows, that miniFrac is capable of outperforming both systems regarding product quality and energy demand at a comparable product yield. If, in the case of fines-fiber fractionation, reject purity (i. e., fines exclusion) is more important than fines purity (i. e., long fiber remain in the reject), miniFrac is an interesting tool with some key advantages over pressure screens.

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Assessment of variance & distribution in data for effective use of statistical methods for product quality prediction

at - Automatisierungstechnik ◽

10.1515/auto-2017-0115 ◽

2018 ◽

Vol 66 (4) ◽

pp. 344-355 ◽

Cited By ~ 1

Author(s):

Iris Weiß ◽

Birgit Vogel-Heuser

Keyword(s):

Data Mining ◽

Product Quality ◽

Statistical Methods ◽

Analysis Of Variance ◽

Production System ◽

Production Systems ◽

High Potential ◽

Quality Prediction ◽

Automated Production ◽

Effective Use

AbstractData mining in automated production systems provide high potential to increase the Overall Equipment Effectiveness. Nevertheless, data of such machines/plants include specific characteristics regarding the variance and distribution of the dataset. For modelling product quality prediction, these characteristics have to be analysed to interpret the results correctly. Therefore, an approach for the analysis of variance and distribution of datasets is proposed. The evaluation of this approach validates the developed guidelines, which identify the reasons for inconsistent prediction results based on two different datasets of the same production system.

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Designof Quality Prediction System of Laser Cutting Based on MATLAB Neural Network

Applied laser ◽

10.3788/al20143402.122 ◽

2014 ◽

Vol 34 (2) ◽

pp. 122-125

Author(s):

李建敏 Li Jianmin ◽

李国柱 Li Guozhu ◽

王春明 Wang Chunming ◽

胡席远 Hu Xiyuan ◽

闫飞 Yan Fei ◽

...

Keyword(s):

Neural Network ◽

Laser Cutting ◽

Quality Prediction ◽

Prediction System

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Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues

Geoscientific Model Development ◽

10.5194/gmd-13-1055-2020 ◽

2020 ◽

Vol 13 (3) ◽

pp. 1055-1073 ◽

Cited By ~ 3

Author(s):

Kyunghwa Lee ◽

Jinhyeok Yu ◽

Sojin Lee ◽

Mieun Park ◽

Hun Hong ◽

...

Keyword(s):

Kalman Filter ◽

Air Quality ◽

Initial Conditions ◽

Optimal Interpolation ◽

Quality Prediction ◽

Prediction System ◽

Monitoring Networks ◽

Alkyl Nitrates ◽

Mixing Ratios ◽

Air Quality Prediction

Abstract. For the purpose of providing reliable and robust air quality predictions, an air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multiscale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (index of agreement IOA =0.60; mean bias MB =-13.54) and PM2.5 (IOA =0.71; MB =-2.43) as compared to the BASE RUN for PM10 (IOA =0.51; MB =-27.18) and PM2.5 (IOA =0.67; MB =-9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3 and peroxyacetyl nitrates – PANs – in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLHs) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free-parameter scheme, dust episode prediction and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the 3D variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean air quality prediction system (KAQPS).

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Study on Quality Prediction Technology of Manufacturing Supply Chain

Supply Chain and Logistics Management ◽

10.4018/978-1-7998-0945-6.ch102 ◽

2020 ◽

pp. 2128-2148

Author(s):

Genbao Zhang ◽

Yan Ran ◽

Dongmei Luo

Keyword(s):

Quality Control ◽

Supply Chain ◽

Customer Satisfaction ◽

Product Quality ◽

Manufacturing Industry ◽

Quality Prediction ◽

Full Life Cycle ◽

Manufacturing Supply Chain ◽

Full Life ◽

Ensure Product Quality

Supply chain quality is the assurance of product quality in its full life-cycle. Although supply chain quality control is a hot topic among researchers, supply chain quality prediction is actually an important but unsolved problem in manufacturing industry. In this paper, an approach of manufacturing supply chain quality prediction based on quality satisfaction degree is proposed to control supply chain better, in order to help ensure product quality. Supply chain quality prediction 3D model and model based on customer satisfaction and process control are established firstly. And then technologies used in quality prediction are studied, including quality prediction index system established on Expert scoring -AHP and prediction workflow built on ABPM. Finally an example is given to illustrate this approach. The customer satisfaction prediction result of supply chain quality can help supply chain management, and the quality prediction software system can make it easier, which provides a new direction for the product quality control technology research.

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