Joint planning of maintenance, buffer stock and quality control for unreliable, imperfect manufacturing systems

2021 ◽  
pp. 107304
Author(s):  
Seyed Mohammad Hadian ◽  
Hiwa Farughi ◽  
Hasan Rasay
Keyword(s):  
Quality Control ◽  
Manufacturing Systems ◽  
Buffer Stock ◽  
Joint Planning

Location of quality-control stations in manufacturing systems: A simulated annealing approach

1994 ◽  
Vol 7 (4) ◽  
pp. 367-380 ◽  
Author(s):  
Mukesh Taneja ◽  
Shashi M. Sharma ◽  
N. Viswanadham
Keyword(s):  
Quality Control ◽  
Simulated Annealing ◽  
Manufacturing Systems

scholarly journals Allocation of quality control stations in multistage manufacturing systems

2011 ◽  
Vol 60 (4) ◽  
pp. 473-484 ◽  
Author(s):  
Ali G. Shetwan ◽  
Valentin I. Vitanov ◽  
Benny Tjahjono
Keyword(s):  
Quality Control ◽  
Manufacturing Systems ◽  
Multistage Manufacturing

A Model of a Quality Control for Integrated Manufacturing Systems

2008 ◽  
Vol 41 (3) ◽  
pp. 187-192
Author(s):  
P. Korytkowski ◽  
O. Zaikin ◽  
A. Olejnik-Krugly
Keyword(s):  
Quality Control ◽  
Manufacturing Systems ◽  
Integrated Manufacturing ◽  
Integrated Manufacturing Systems

A Vision-Based Framework for Enhanced Quality Control in a Smart Manufacturing System

2019 ◽  
Author(s):  
Zixuan Yang ◽  
Huaiyuan Teng ◽  
Jeremy Goldhawk ◽  
Ilya Kovalenko ◽  
Efe C. Balta ◽  
...  
Keyword(s):  
Computer Vision ◽  
Quality Control ◽  
Computer Aided Design ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Dimensional Metrology ◽  
Design Changes ◽  
Central Controller ◽  
Aided Design ◽  
Smart Manufacturing Systems

Abstract Dimensional metrology is an integral part of quality control in manufacturing systems. Most existing manufacturing systems utilize contact-based metrology, which is time consuming and not flexible to design changes. There have been recent applications of computer vision for performing dimensional metrology in manufacturing systems. Existing computer vision metrology techniques need repeated calibration of the system and are not utilized with data analysis methods to improve decision making. In this work, we propose a robust non-contact computer vision metrology pipeline integrated with Computer Aided Design (CAD) that has the capacity to enable control of smart manufacturing systems. The pipeline uses CAD data to extract nominal dimensions and tolerances. The dimensions are compared to the measured ones, computed using camera images and computer vision algorithms. A quality check module evaluates if the measurements are within admissible bounds and informs a central controller. If a part does not meet a tolerance, the central controller changes a program running on a specific machine to ensure that parts meet the necessary specifications. Results from an implementation of the proposed pipeline on a manufacturing research testbed are given at the end.

Maintenance and quality control integrated strategy for manufacturing systems

2018 ◽  
Vol 12 (3) ◽  
pp. 307 ◽  
Author(s):  
Nadia Bahria ◽  
Anis Chelbi ◽  
Imen Harbaoui Dridi ◽  
Hanen Bouchriha
Keyword(s):  
Quality Control ◽  
Manufacturing Systems ◽  
Integrated Strategy

Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalad Rao ◽  
Hui Yang
Keyword(s):  
Quality Control ◽  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Experimental Studies ◽  
Imaging Data ◽  
Monitoring And Control ◽  
Ultraprecision Machining ◽  
Statistical Process ◽  
Lacunarity Analysis

The modern manufacturing industry faces increasing demands to customize products according to personal needs, thereby leading to the proliferation of complex designs. To cope with design complexity, manufacturing systems are increasingly equipped with advanced sensing and imaging capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in the image stream collected from manufacturing processes. This paper presents the joint multifractal and lacunarity analysis to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics in the manufacturing process. Experimental studies show that the proposed method not only effectively characterizes surface finishes for quality control of ultraprecision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed multifractal method shows strong potentials to be applied for process monitoring and control in a variety of domains such as ultraprecision machining and additive manufacturing.

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