Automatic Virtual Metrology and Target Value Adjustment for Mass Customization

Chen, Chun Fang; Cheng, Fan‐Tien; Yang, Haw Ching

doi:10.1109/lra.2016.2645507

Cited by 20 publications

(10 citation statements)

References 16 publications

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“…The required sample size must be defined, as it is used it to determine the number of conversions of the AVM method; experts recommend about 3-5 per group. The target value (TV) of the operational sample is determined by the average measurement value, as shown in Formula (1), where y j represents the actual measurement value of the j number of elements, and n = the size of the TV [3].…”

Section: Automatic Virtual Metrology Control Methodsmentioning

confidence: 99%

“…This article discussed the use of a deformation fusion (DF) method to deal with component deformation issues. In 2016, Tieng et al [3] required a screw design unit with MC capability to be developed with a high degree of rapid response capability to better accurately manufacture various types of wheels where processing conditions are changing. To meet the requirements of MC production, this work proposed the application of an AVM system along with a target value adjustment (TVA) method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In 2012, Cheng et al defined [2] Virtual Metrology (VM) as a method to conjecture the manufacturing quality of a process tool based on data sensed from the process tool and without physical metrology operations. In addition, in 2016, Tieng et al [3] mentioned VM as a method to conjecture manufacturing quality of a process tool based on data sensed from the process tool and without physical metrology operation. In other words, VM can convert sampling inspections with metrology delay into real-time and on-line total inspection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Automatic Virtual Metrology Control Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chang

Zhang

et al. 2021

Applied Sciences

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“…Constant system identification has been applied for VM (Pan et al 2011;Tieng et al 2017). However, it remains very limited as very few products differ from a simple constant.…”

Section: Adaptability Featurementioning

confidence: 99%

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Dreyfus

Psarommatis

May

et al. 2021

International Journal of Production Research

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Virtual metrology (VM) involves estimating a product's quality directly from production process data without physically measuring it. This enables the product quality of each unit of production to be monitored in real time, while preserving the process efficiency. Initially developed for the semiconductor industry, VM has recently been examined for use in other industrial fields. VM is enabled by components such as quality estimators and drift detectors. It enhances various industrial applications such as machine control and sampling decision systems. The literature lacks a comprehensive summary and a systematic review of the state of the art in VM. To fill this gap, this study followed a systematic methodology to conduct a complete and structured literature review of VM. This paper presents a detailed analysis of the 199 papers that we identified based on our search and selection criteria. Following our methodological framework, we assigned these papers to categories and highlighted shortcomings. Finally, we discussed VM's use in various industrial fields, underlining its potential for every manufacturing industry. This study and the proposed framework are useful for both practitioners and academicians since they raise crucial theoretical and managerial questions as well as provide suggestions for further research on this critical topic.

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“…Currently, there is a lack of structured methods for mapping human and product component interactions in consumer products (e.g., cell phone, sunglasses, utensil, glucometer, helmet, shoes, remote control, hair dryer, etc.) [21][22][23][24][25]. The Interaction Variability method seeks to address the baseline necessity of users for product interaction instead of solely customer preference, since customers may not always be sensitive to their musculoskeletal needs.…”

Section: Human Interaction Factorsmentioning

confidence: 99%

Design and Validation of a Method to Characterize Human Interaction Variability

Cage¹,

Vaughn-Cooke²,

Fuge³

2020

Systems

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Human interactions are paramount to the user experience, satisfaction, and risk of user errors. For products, anthropometry has traditionally been used in product sizing. However, structured methods that accurately map static and dynamic capabilities (e.g., functional mapping) of musculoskeletal regions for the conceptualization and redesign of product applications and use cases are limited. The present work aims to introduce and validate the effectiveness of the Interaction Variability method, which maps product components and musculoskeletal regions to determine explicit design parameters through limiting designer variation in the classification of human interaction factors. This study enrolled 16 engineering students to evaluate two series of interactions for (1) water bottle and (2) sunglasses applications enabling method validity and designer consistency assessments. For each interaction series, subjects identified and characterized product applications, components, and human interaction factors. Primary interactions, product mapping, and application identification achieved consensus between ranges of 31.25% and 100.00%, with significance (p < 0.1) observed at consensus rates of ≥75.00%. Significant levels of consistency were observed amongst designers, for at least one measure in all phases except anthropometric mapping for the sunglasses application indicating method effectiveness. Interaction variability was introduced and validated in this work as a standardized approach to identify, define, and map human and product interactions, which may reduce unintended use cases and user errors, respectively, in consumer populations.

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Automatic Virtual Metrology and Target Value Adjustment for Mass Customization

Cited by 20 publications

References 16 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

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Design and Validation of a Method to Characterize Human Interaction Variability

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