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19 pages, 4322 KiB

Open AccessArticle

Algorithm for Correlation Diagnosis in Multivariate Process Quality Based on the Optimal Typical Correlated Component Pair Group

by Qing Niu, Shujie Cheng and Zeyang Qiu

Processes 2024, 12(4), 652; https://doi.org/10.3390/pr12040652 - 25 Mar 2024

Viewed by 588

Abstract

Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new approach based on the optimal typical correlated component pair group (OTCCPG) is proposed. Firstly, the theorem of correlation decomposition is proved to decompose the correlation [...] Read more.

Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new approach based on the optimal typical correlated component pair group (OTCCPG) is proposed. Firstly, the theorem of correlation decomposition is proved to decompose the correlation of all the quality components as serial correlations of component pairs, and then according to the transitivity of correlations of component pairs, the decomposition result is represented by a correlation set of typical correlated component pairs. Finally, an algorithm for OTCCPG based on the maximum correlation spanning tree (MCST) is proposed, and T² control charts to monitor the correlations of component pairs in OTCCPG are established to form the correlation diagnostic system. Theoretical analysis and practice prove that the proposed method could reduce the space complexity of the diagnostic system greatly. Full article

(This article belongs to the Special Issue Advances in Intelligent Manufacturing Systems and Process Control)

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20 pages, 3626 KiB

Open AccessArticle

Determining Optimal Assembly Condition for Lens Module Production by Combining Genetic Algorithm and C-BLSTM

by Hyegeun Min, Yeonbin Son and Yerim Choi

Processes 2024, 12(3), 452; https://doi.org/10.3390/pr12030452 - 23 Feb 2024

Viewed by 704

Abstract

Mobile camera modules are manufactured by aligning and assembling multiple differently shaped part lenses. Therefore, selecting the part lenses to assemble from candidates (called cavities) and determining the directional angle of each part lens for assembly have been important issues to maximize production [...] Read more.

Mobile camera modules are manufactured by aligning and assembling multiple differently shaped part lenses. Therefore, selecting the part lenses to assemble from candidates (called cavities) and determining the directional angle of each part lens for assembly have been important issues to maximize production yield. Currently, this process is manually conducted by experts at the manufacturing site, and the manual assembly condition optimization carries the risk of reduced production yield and increased failure cost as it largely depends on one’s expertise. Herein, we propose an AI framework that determines the optimal assembly condition including the combination of part lens cavities and the directional angles of part lenses. To achieve this, we combine the genetic algorithm with convolutional bidirectional long-term short-term memory (C-BLSTM). To the best of our knowledge, this is the first study on lens module production finding the optimal combination of part lens cavities and directional angles at the same time using machine learning methods. Based on experimental results using real-world datasets collected by lens module manufacturers, the proposed framework outperformed existing algorithms with an F1 score of 0.89. Moreover, the proposed method (S2S-AE) for predicting the directional angles exhibited the best performance compared to existing algorithms with an accuracy of 78.19%. Full article

(This article belongs to the Special Issue Advances in Intelligent Manufacturing Systems and Process Control)

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14 pages, 3113 KiB

Open AccessArticle

Application of Three-Dimensional Printing Technology to the Manufacture of Petroleum Drill Bits

by Baxian Liu, Yifei Wang, Junjie Jiang, Bihui Zhang, Jian Zhou and Kuilin Huang

Processes 2023, 11(9), 2706; https://doi.org/10.3390/pr11092706 - 10 Sep 2023

Viewed by 1133

Abstract

Drill bits are the main rock-breaking tools in the petroleum and gas industry. Their performance directly affects the quality, efficiency, and cost of drilling. Drill bit manufacturing mainly employs traditional mold forming processes such as milling molding and press molding, which have low [...] Read more.

Drill bits are the main rock-breaking tools in the petroleum and gas industry. Their performance directly affects the quality, efficiency, and cost of drilling. Drill bit manufacturing mainly employs traditional mold forming processes such as milling molding and press molding, which have low production efficiency and long processing cycles and are not conducive to rapid responses to field requirements. Inadequate production accuracy makes it difficult to produce drill bits with complex structures. Three-dimensional (3D) printing technology has fast molding speeds and high molding accuracy. In this paper, 3D printing was applied for the first time to the manufacture of molds for carcass polycrystalline diamond compact (PDC) drill bits and PDC–cone hybrid drill bits. In comparison with forging and milling molding, 3D printing improved production efficiency. The manufactured molds had higher machining accuracy. The ability of 3D printing to make molds with complex surfaces enables the development of drill bits with complex structures. A field experiment was conducted on a PDC drill bit produced by 3D printing, which had a higher rate of penetration and was more efficient in breaking rocks than bits manufactured by traditional processes. The ROP of the drill bit increased by 20.1–25.8%, and the drilling depth increased by 7.7–29.5%. It is therefore feasible to apply 3D printing to the manufacture of petroleum drill bits. Full article

(This article belongs to the Special Issue Advances in Intelligent Manufacturing Systems and Process Control)

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19 pages, 48488 KiB

Open AccessArticle

YOLOv7-Based Anomaly Detection Using Intensity and NG Types in Labeling in Cosmetic Manufacturing Processes

by Seunghyo Beak, Yo-Han Han, Yeeun Moon, Jieun Lee and Jongpil Jeong

Processes 2023, 11(8), 2266; https://doi.org/10.3390/pr11082266 - 27 Jul 2023

Cited by 1 | Viewed by 2120

Abstract

The advent of the Fourth Industrial Revolution has revolutionized the manufacturing sector by integrating artificial intelligence into vision inspection systems to improve the efficiency and quality of products. Supervised-learning-based vision inspection systems have emerged as a powerful tool for automated quality control in [...] Read more.

The advent of the Fourth Industrial Revolution has revolutionized the manufacturing sector by integrating artificial intelligence into vision inspection systems to improve the efficiency and quality of products. Supervised-learning-based vision inspection systems have emerged as a powerful tool for automated quality control in various industries. During visual inspection or final inspection, a human operator physically inspects a product to determine its condition and categorize it based on their know-how. However, the know-how-based visual inspection process is limited in time and space and is affected by many factors. High accuracy in vision inspection is highly dependent on the quality and precision of the labeling process. Therefore, supervised learning methods of 1-STAGE DETECTION, such as You Only Look Once (YOLO), are utilized in automated inspection to improve accuracy. In this paper, we proposed a labeling method that achieves the highest inspection accuracy among labeling methods such as NG intensity and NG intensity when performing anomaly detection using YOLOv7 in the cosmetics manufacturing process. Full article

(This article belongs to the Special Issue Advances in Intelligent Manufacturing Systems and Process Control)

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14 pages, 7870 KiB

Open AccessArticle

Assembly Sequence Validation with Feasibility Testing for Augmented Reality Assisted Assembly Visualization

by M. V. A. Raju Bahubalendruni and Bhavasagar Putta

Processes 2023, 11(7), 2094; https://doi.org/10.3390/pr11072094 - 13 Jul 2023

Cited by 1 | Viewed by 1174

Abstract

The recent advances in Industry 4.0 have promoted manufacturing industries towards the use of augmented reality (AR), virtual reality (VR), and mixed reality (MR) for visualization and training applications. AR assistance is extremely helpful in assembly task visualization during the stages of product [...] Read more.

The recent advances in Industry 4.0 have promoted manufacturing industries towards the use of augmented reality (AR), virtual reality (VR), and mixed reality (MR) for visualization and training applications. AR assistance is extremely helpful in assembly task visualization during the stages of product assembly and in disassembly plan visualization during the repair and maintenance of a product/system. Generating such assembly and disassembly task animations consume a lot of time and demands skilled user intervention. In assembly or disassembly processes, each operation must be validated for geometric feasibility regarding its practical implementation in the real-time product. In this manuscript, a novel method for automated assembly task simulation with improved geometric feasibility testing is proposed and verified. The proposed framework considers the assembly sequence plan as input in the form of textual instructions and generates a virtual assembly task plan for the product; furthermore, these instructions are used to ensure there are no collisions using a combination of multiple linear directions. Once the textual instructions achieve geometric feasibility for the entire assembly operation, the visual animations of the assembly operations are successively produced in a game engine and are integrated with the AR platform in order to visualize them in the physical environment. The framework is implemented on various products and validated for its correctness and completeness. Full article

(This article belongs to the Special Issue Advances in Intelligent Manufacturing Systems and Process Control)

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15 pages, 5959 KiB

Open AccessArticle

System Design and Mechanism Study of Ultrasonic-Assisted Electrochemical Grinding for Hard and Tough Materials

by Chen Hu and Yongwei Zhu

Processes 2023, 11(6), 1743; https://doi.org/10.3390/pr11061743 - 7 Jun 2023

Viewed by 831

Abstract

In this study, an ultrasonic-assisted electrochemical grinding (UAECG) system was designed to improve the low efficiency and tool wear in conventional grinding of hard and tough materials. In this system, multiple-field energy consisting of ultrasonic, electrochemical and mechanical grinding was used. The processing [...] Read more.

In this study, an ultrasonic-assisted electrochemical grinding (UAECG) system was designed to improve the low efficiency and tool wear in conventional grinding of hard and tough materials. In this system, multiple-field energy consisting of ultrasonic, electrochemical and mechanical grinding was used. The processing mechanism was investigated to determine the interaction mechanism between ultrasonic, grinding and electrochemical processing. The established theoretical model showed that the processing efficiency was affected by the ultrasonic amplitude, ultrasonic frequency, electrolyte conductivity and other parameters. In verifying the feasibility of UAECG machining and the effect of machining elements on machining, a series of corresponding machining experiments was conducted. Experiments showed that the machining efficiency can be improved by machining through the UAECG system. The material removal rate of W18Cr4V machining was 2.7 times higher than that of conventional grinding and 1.7 times higher than UAG. The processing efficiency of YT15 was increased by 3.2 times when the processing voltage increased from 2 to 6 V. The surface shape and roughness were also affected by these parameters. The surface roughness of the SiCp/Al workpiece reached the best level at 4 V as the machining voltage increased from 2 to 6 V. However, the surface roughness increased significantly when the voltage increased to 6 V. Thus, parameters such as machining voltage must be optimised for efficient and precise machining in practice. Full article

(This article belongs to the Special Issue Advances in Intelligent Manufacturing Systems and Process Control)

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12 pages, 452 KiB

Open AccessArticle

An App-Based Recommender System Based on Contrasting Automobiles

by Hsiu-Wen Liu, Jei-Zheng Wu and Fang-Lin Wu

Processes 2023, 11(3), 881; https://doi.org/10.3390/pr11030881 - 15 Mar 2023

Cited by 2 | Viewed by 2614

Abstract

Product recommendation systems are essential for enhancing customer experience, and integrating them with mobile apps is crucial for improving usability and fostering user engagement. This study proposes a hybrid approach that utilizes comparative facts from pairwise comparison data and comparison lists, with association [...] Read more.

Product recommendation systems are essential for enhancing customer experience, and integrating them with mobile apps is crucial for improving usability and fostering user engagement. This study proposes a hybrid approach that utilizes comparative facts from pairwise comparison data and comparison lists, with association rules as the method to formulate the recommendation system. The study employs a dataset from the New-Cars Database app, comprising 30,867 vehicle comparisons made by 5327 users across 40 car brands and 870 cars from 30 January 2015 to 2 April 2015. Two metrics are developed to measure the system’s output under varying support and confidence thresholds. The findings suggest that adjusting the support and confidence values can improve the breadth and depth of product recommendations. In addition, the unit of analysis can affect the recommendation system’s output, with comparison lists supplementing and expanding the exploration of potential outcomes. The proposed hybrid approach aims to provide more reliable and comprehensive product recommendations by combining both approaches and has implications for both academic and managerial contexts by facilitating the development of effective recommendation systems. Full article

(This article belongs to the Special Issue Advances in Intelligent Manufacturing Systems and Process Control)

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16 pages, 2487 KiB

Open AccessArticle

Modeling Vehicle Insurance Adoption by Automobile Owners: A Hybrid Random Forest Classifier Approach

by Moin Uddin, Mohd Faizan Ansari, Mohd Adil, Ripon K. Chakrabortty and Michael J. Ryan

Processes 2023, 11(2), 629; https://doi.org/10.3390/pr11020629 - 18 Feb 2023

Cited by 3 | Viewed by 2061

Abstract

This study presents a novel hybrid framework combining feature selection, oversampling, and machine learning (ML) to improve the prediction performance of vehicle insurance. The framework addresses the class imbalance problem in binary classification tasks by employing principal component analysis for feature selection, the [...] Read more.

This study presents a novel hybrid framework combining feature selection, oversampling, and machine learning (ML) to improve the prediction performance of vehicle insurance. The framework addresses the class imbalance problem in binary classification tasks by employing principal component analysis for feature selection, the synthetic minority oversampling technique for oversampling, and the random forest ML classifier for prediction. The results demonstrate that the proposed hybrid framework outperforms the conventional approach and achieves better accuracy. The purpose of this study is to provide insurance managers and practitioners with novel insights into how to improve prediction accuracy and decrease financial risks for the insurance industry. Full article

(This article belongs to the Special Issue Advances in Intelligent Manufacturing Systems and Process Control)

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