Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

Bai, Yun; Sun, Zhuo; Deng, Jun; Li, Lin; Long, Jianyu; Li, Chuan

doi:10.3390/su10010085

Cited by 38 publications

(13 citation statements)

References 26 publications

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“…In the future, we will consider other model combination strategies together with other types of base learners, such as neural networks with the fuzzy system [12] [16]. Meanwhile, with the massive data sources, some machine learning strategies, such as deep learning, will be incorporated in the next stage.…”

Section: Discussionmentioning

confidence: 99%

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Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Dong

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Recent advances in the steel industry have encountered challenges in soliciting decision making solutions for quality control of products based on data mining techniques. In this paper, we present a steel quality control prediction system encompassing with real-world data as well as comprehensive data analysis results. The core process is cautiously designed as a regression problem, which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets. The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared. The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques. Furthermore, real-world deployment of this system is demonstrated and discussed. Finally, future directions and the performance expectation are pointed out.

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Section: Discussionmentioning

confidence: 99%

“…SPC is a quality control method, based on cause-effect relationships, for monitoring and controlling the quality of the manufacturing processes through the reduction of variability [14] [15] [16]. It plays a vital role in improving the competitiveness of their products in the steel industry [17].…”

Section: B Statistical Process Controlmentioning

confidence: 99%

Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Dong

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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“…It means that the deep learning techniques considered can be applied to establish accurate manufacturing fields. Similarly, deep feature learning is beneficial to explore sophisticated relationships between multiple features of manufacturing and quality [48].…”

Section: Autoencoder-based Modelmentioning

confidence: 99%

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

et al. 2021

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With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.

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“…In the production process, real-time data should facilitate the monitoring of the production process, so the manufacturers could keep up to date with the production deviations to generate optimal operational control plans [ 24 ]. Fault diagnosis and operation process optimization can be accomplished by storing and analyzing data for active preventive maintenance, repair, and overhaul (MRO), through Big Data from IIoT.…”

Section: Industrial Big Data Analyticsmentioning

confidence: 99%

An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions

Hinojosa-Palafox

Rodríguez-Elias

Hoyo-Montaño

et al. 2021

Sensors

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The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

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Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

Cited by 38 publications

References 26 publications

Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions

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