Automatic Virtual Metrology and Deformation Fusion Scheme for Engine-Case Manufacturing

Tsai, Tsung-Han; Chen, Chun Fang; Yang, Haw‐Ching; Huang, Jhih Wei; Cheng, Fan‐Tien

doi:10.1109/lra.2018.2792690

Cited by 21 publications

(10 citation statements)

References 19 publications

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“…The AVM system is improved by a feature selection method to solve the problem of limited sample sizes of quality method parts. In 2018, Tieng et al [19] measured long metrology delays and severed component deformations. To overcome these two challenges that are related to the aerospace industry, the article recommends the use of an AVM system to successfully convert sample inspections that have metrological delays into real-time and online general inspections.…”

Section: Literature Reviewmentioning

confidence: 99%

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Chang

Zhang

et al. 2021

Applied Sciences

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One of the important values of Industry 4.0 is to integrate people’s needs into the manufacture of enhanced products, systems, and services to achieve greater levels of product customization. This paper presents a prediction method for predicting screw process parameters; taking crystalline and non-crystalline polymers as the molding material, when there is a lack of sufficient historical screw process data to establish a data-driven method, using various screws and polymer materials to predict tool life under different cutting conditions is a challenge. A screw life prediction method is proposed based on the mixed compound screw process parameters method using a dynamic iteration work. To meet the requirements of mass production, this work proposes the combined application of the automatic virtual metrology (AVM) system with the recognizable performance evaluation (RPE) program. The method predicts the injection of compound screws by extracting given cutting conditions and related process parameters characteristics from the senor data by converting sampling inspections with measurement delays from real-time and online routine inspections to automatically and quickly complete method creation production goals.

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Section: Literature Reviewmentioning

confidence: 99%

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Chang

Zhang

et al. 2021

Applied Sciences

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“…The smart technologies, moreover, could be used to identify when machines are being used improperly and prevent human errors, evading inactivity and workplace injuries [20]. Industry 4.0 related technologies will also transform completely the quality assurance (QA) process by establishing real-time QA through an automated virtual metrology (AVM) system [20,24]. These systems consist of gathering data concerning conditions, quality, and state of the products, and combining them with information related to the machines and processes, and using a real-time element to QA inspections.…”

Section: Digitalisation Of Machine Tool Industrymentioning

confidence: 99%

“…These incorporate basic digital skills, along with advanced technological skills, such as computing, data analysis and evaluation [13,31]. As a result of this demand, data security together with data safety will become more relevant [24]. Social and emotional skills will also be highly demanded due to the implementation of advanced technologies [29,31].…”

Section: General Skills Trends For the Machine Tool Sectormentioning

confidence: 99%

Skills Requirements for the European Machine Tool Sector Emerging from Its Digitalization

Akyazi

Goti

Oyarbide-Zubillaga

et al. 2020

Metals

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The machine tool industry, which is the starting point of all the metal producing activities, is presently undergoing rapid and continuous changes as a result of the fourth industrial revolution Industry 4.0. Manufacturing models are profoundly transforming with emerging digitalization. Smart technologies like artificial intelligence (AI), big data, the Internet of Things (IoT), digital twin, allow the machine tool companies to optimize processes, increase efficiency and reduce waste through a new phase of automation. These technologies, as well, enable the machine tool producers to reach the aim of creating products with improved performance, extended life, high reliability that are eco-efficient. Therefore, Industry 4.0 could be perceived as an invaluable opportunity for the machine tool sector, only if the sector has a competent workforce capable of handling the implementation of new business models and technological developments. The main condition to create this highly qualified workforce is reskilling and upskilling of the current workforce. Once we define the expected evolution of skills requirements, we can clarify the skills mismatch between the workers and job profiles. Only then, we can reduce them by delivering well-developed trainings. For this purpose, this article identifies the current and foreseen skills requirements demanded by the machine tool industry workforce. To this end, we generated an integrated database for the sector with the present and prospective skills needs of the metal processing sector professionals. The presented sectoral database is a fundamental structure that will make the sector acquire targeted industrial reforms. It can also be an essential instrument for machine tool companies, policymakers, academics and education or training centers to build well-designed and effective training programs to enhance the skills of the labor force.

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“…e virtual metrology has been widely applied in diverse high-tech industries, including the semiconductor industry [1], TFT-LCD industry [2], and the solar-cell industry [3]. Moreover, the virtual metrology technology has been applied for processing the CNC machine tool and reasonable results have been achieved accordingly [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Performance Prediction Model for the Machining Process Based on the Interval Type-2 Fuzzy Neural Network

Tian

Zhao

Sun

et al. 2020

Mathematical Problems in Engineering

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The prediction model is the most important part of the virtual metrology system. Predicting the performance of the machining process has been widely applied in manufacturing, which can reduce costs and improve efficiency compared with the manual operation. In this paper, a novel performance prediction model for the machining process is proposed based on the interval type-2 fuzzy neural network. The interval type-2 fuzzy logic system with a complete rule base, type-reduction, and defuzzified output is simplified by the BMM method to meet the requirements of the prediction. The proposed prediction model is trained using a gradient-based optimization algorithm. To evaluate the performance of the proposed approach, it is applied to wire electrical discharge turning process for predicting material removal rate and surface roughness with a published dataset. The results show that the proposed method is an effective scheme in the studied cases.

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Automatic Virtual Metrology and Deformation Fusion Scheme for Engine-Case Manufacturing

Cited by 21 publications

References 19 publications

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Skills Requirements for the European Machine Tool Sector Emerging from Its Digitalization

A Novel Performance Prediction Model for the Machining Process Based on the Interval Type-2 Fuzzy Neural Network

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