Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics

Jun, Ji-hye; Chang, Tai‐Woo; Jun, Sungbum

doi:10.3390/pr8091068

Cited by 27 publications

(8 citation statements)

References 28 publications

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“…For example a poor quality product could severely damage the image of a company because of the customer dissatisfaction or product contamination (Jun et al, 2020).…”

Section: The Promise Of Zero-defect Manufacturingmentioning

confidence: 99%

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Cameron

Waaler

Fjøsna³

et al. 2022

Front. Manuf. Technol.

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The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

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“…For example a poor quality product could severely damage the image of a company because of the customer dissatisfaction or product contamination (Jun et al, 2020).…”

Section: The Promise Of Zero-defect Manufacturingmentioning

confidence: 99%

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Cameron

Waaler

Fjøsna³

et al. 2022

Front. Manuf. Technol.

View full text Add to dashboard Cite

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“…e calculation results increase given large data values. When MAPE is less than 10, the model is highly accurate [54]:…”

Section: Mean-square Error (Mse)mentioning

confidence: 99%

Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric-Mechanical Equipment Industry

et al. 2022

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Electric-mechanical equipment manufacturing industries focus on the implementation of intelligent manufacturing systems in order to enhance customer services for highly customized machines with high-profit margins such as electric power transformers. Intelligent manufacturing consists in using supply chain data that are integrated for smart decision making during the production life cycle. This research, in cooperation with a large electric power transformer manufacturer, provides an overview of critical intelligent manufacturing (IM) technologies. An ontology schema forms the terminology relationships needed to build two intelligent supply chain management (SCM) modules for the IM system demonstration. The two core modules proposed in this research are the intelligent supplier selection and component ordering module and the product quality prediction module. The intelligent supplier selection and component ordering module dispatches orders that match the best options of suppliers based on combined analytic hierarchy process (AHP) analysis and multiobjective integer optimization. In the case study, the intelligent supplier selection and component ordering module demonstrates several acceptable Pareto solutions based on strict constraints, which is a very challenging task for decision makers without assistance. The second module is the product quality prediction module which uses multivariate regression and ARIMA to predict the quality of the finished products. Results show that the R square values are very close to 1. The module shortens the time for the company to accurately judge whether the two semifinished iron cores for the product meet the quality requirements. The component supplier selection module and the finished product quality prediction module developed in this research can be extended to other IM systems for general high-end equipment manufacturers using mass customization.

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“…Pada sisi lain, Indonesia Sawmill and Woodworking Association (ISWA) pesimistis performa ekspor industri kayu pada tahun ini akan membaik, seiring dengan pengguna kayu global yang belum menaikkan harga produk jadi, sementara biaya produksi on farm terus menanjak. Pabrik pengolahan harus lebih efisien untuk mengimbangi kenaikan harga bahan baku kayu, salah satunya adalah dengan memaksimalkan rendemen produksi [4] .…”

Section: Pendahuluanunclassified

Augmented Reality dan Pehitungan Rendemen pada Produksi Kayu Profil Menggunakan Mesin Router

Hermawan

Wihartiko

Abdillah³

2022

FORMAT

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Salah satu produk olahan kayu yang masih banyak diminati pasar adalah wood profile moulding. Bentuk profil yang tidak mengikuti ruang dasar geometris tertentu dapat dihadirkan melalui Augmented Reality. Pendugaan rendemen produksi kayu profil menjadi kurang akurat dengan bentuknya yang tidak beraturan. Augmented Reality memberikan bentuk nyata dari profil kayu yang akan dibentuk dari pilihan bit router. Augmented Reality dibangun menggunakan penanda (marker) yang diberikan dalam bentuk desain bit router 2D. Aplikasi yang dibuat dengan platform Android bahkan memudahkan pengguna untuk memilih jenis dan spesifikasi bit router, sesuai dengan bentuk profil yang diinginkan. Telah diuji untuk jenis Carbide Cove Router dengan tujuh bit router. Aplikasi dilengkapi dengan perkiraan jumlah profil, volume, hasil produksi, dan limbah kayu yang dihasilkan dari bahan baku kayu yang akan dikerjakan. Percobaan dilakukan pada bahan baku papan berukuran 30x200x2000 mm, menghasilkan jumlah profil cekung yang bervariasi antara 3-5 buah, sesuai dengan ukuran mata bor terluar. Hasil produksi bervariasi dari 42,95 hingga 74,44%. Peningkatan rendemen tersebut sejalan dengan jumlah bilah kayu profil yang diproduksi.

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Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics

Cited by 27 publications

References 28 publications

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric-Mechanical Equipment Industry

Augmented Reality dan Pehitungan Rendemen pada Produksi Kayu Profil Menggunakan Mesin Router

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