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.

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

2018 ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Experimental Studies ◽  
Precision Machining ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Ultra Precision ◽  
And Control

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing 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 image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision 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 fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

A Bio-Inspired Framework for a Self-Healing Assembly System

2011 ◽  
Author(s):  
Lee J. Wells ◽  
Jaime A. Camelio ◽  
Giovannina Zapata
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
System Level ◽  
Self Healing ◽  
Monitoring And Control ◽  
Statistical Process ◽  
Control Approach ◽  
Process Monitoring And Control ◽  
Manufacturing Assembly ◽  
And Control

Statistical process monitoring and control has been popularized throughout the manufacturing industry as well as various other industries interested in improving product quality and reducing costs. Advances in this field have focused primarily on more efficient ways for diagnosing faults, reducing variation, developing robust design techniques, and increasing sensor capabilities. System level advances are largely dependent on the introduction of new techniques in the listed areas. A unique system level quality control approach is introduced in this paper as a means to integrate rapidly advancing computing technology and analysis methods in manufacturing systems. Inspired by biological systems, the developed framework utilizes immunological principles as a means of developing self-healing algorithms and techniques for manufacturing assembly systems. The principles and techniques attained through this bio-mimicking approach will be used for autonomous monitoring, detection, diagnosis, prognosis, and control of station and system level faults, contrary to traditional systems that largely rely on final product measurements and expert analysis to eliminate process faults.

scholarly journals Pengendalian Kualitas Produksi dengan Statistical Process Control (SPC)

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Mohamad Solihudin
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Process Control ◽  
Statistical Process Control ◽  
Manufacturing Industry ◽  
Product Quality Control ◽  
Standard Size ◽  
Statistical Process ◽  
Analysis Process ◽  
Keywords Quality Assurance

<p>PT. Surya Toto Indonesia Tbk. is one of the processing industry (manufacturing insudtry) with export and domestic market share. To keep the share, the company provides high quality assurance for products offered to consumers and the improvement of quality has always been the main point of fulfilling the production process. This study aims to determine what factors cause the occurrence of reject products are not standard sizes (UTS) and How can quality control with application approach Statitical Process Control (SPC) in machining section PT. Surya Toto Indonesia Tbk. to overcome reject nonstandard size (UTS). The results of this study indicate that the product quality control in machining section on the BNC-1machine, there are the highest reject percentage (11.80%) on part number S23059 with the highest type of rejection is not a standard size (uts) at a size of 9 ± 0,05mm. From the results of field observation and brainstorming, the factors that cause bad part (reject) is a factor of BNC-1 machines are old, worn collet locking bolt and fastener tool insert screws loose. The corrective action taken is to move the process of part number S23059 in production in BNA-DHY2 machine, after analysis process by the method of Statistical Process Control (SPC) concluded for capabilty process (CP) BNA-DHY2 machine excellent is 1,85.</p><p>Keywords: Quality assurance, manufacturing industry, quality control, Statistical Process Control</p>

Deep Learning of Variant Geometry in Layerwise Imaging Profiles for Additive Manufacturing Quality Control

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Farhad Imani ◽  
Ruimin Chen ◽  
Evan Diewald ◽  
Edward Reutzel ◽  
Hui Yang
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Additive Manufacturing ◽  
Computer Aided Design ◽  
Flaw Detection ◽  
Regions Of Interest ◽  
Monitoring And Control ◽  
New Paradigm ◽  
Image Guided ◽  
Process Monitoring And Control

Abstract Additive manufacturing (AM) is a new paradigm in design-driven build of customized products. Nonetheless, mass customization and low-volume production make the AM quality assurance extremely challenging. Advanced imaging provides an unprecedented opportunity to increase information visibility, cope with the product complexity, and enable on-the-fly quality control in AM. However, in situ images of a customized AM build show a high level of layer-to-layer geometry variation, which hampers the use of powerful image-based learning methods such as deep neural networks (DNNs) for flaw detection. Very little has been done on deep learning of variant geometry for image-guided process monitoring and control. The proposed research is aimed at filling this gap by developing a novel machine learning approach that is focused on variant geometry in each layer of the AM build, namely region of interests, for the characterization and detection of layerwise flaws. Specifically, we leverage the computer-aided design (CAD) file to perform shape-to-image registration and to delineate the regions of interest in layerwise images. Next, a hierarchical dyadic partitioning methodology is developed to split layer-to-layer regions of interest into subregions with the same number of pixels to provide freeform geometry analysis. Then, we propose a semiparametric model to characterize the complex spatial patterns in each customized subregion and boost the computational speed. Finally, a DNN model is designed to learn variant geometry in layerwise imaging profiles and detect fine-grained information of flaws. Experimental results show that the proposed deep learning methodology is highly effective to detect flaws in each layer with an accuracy of 92.50 ± 1.03%. This provides a significant opportunity to reduce interlayer variation in AM prior to completion of a build. The proposed methodology can also be generally applicable in a variety of engineering and medical domains that entail customized design, variant geometry, and image-guided process control.

Image-Guided Variant Geometry Analysis of Layerwise Build Quality in Additive Manufacturing

2019 ◽  
Author(s):  
Farhad Imani ◽  
Ruimin Chen ◽  
Evan Diewald ◽  
Edward Reutzel ◽  
Hui Yang
Keyword(s):  
Quality Control ◽  
Additive Manufacturing ◽  
Process Monitoring ◽  
Flaw Detection ◽  
Monitoring And Control ◽  
Image Guided ◽  
Process Monitoring And Control ◽  
And Control ◽  
Geometry Analysis ◽  
The Impact

Abstract Additive manufacturing (AM) is a new paradigm in design-driven build of customized products. Nonetheless, mass customization and low volume production make the AM quality assurance extremely challenging. Advanced imaging provides an unprecedented opportunity to increase information visibility, cope with the product complexity, and enable on-the-fly quality control in AM. However, in-situ images of a customized AM build show a high level of layer-to-layer geometry variation, which hampers the use of powerful image-based learning methods such as deep neural networks (DNNs) for flaw detection. Few, if any, previous works investigated how to tackle the impact of AM customization on image-guided process monitoring and control. The proposed research is aimed at filling this gap by developing a novel real-time and multi-scale process monitoring methodology for quality control of customized AM builds. Specifically, we leverage the computer-aided design (CAD) file to perform shape-to-image registration and delineate the regions of interests in lay-erwise images. Next, a hierarchical dyadic partitioning methodology is developed to split layer-to-layer regions of interest into subregions with the same number of pixels to provide freeform geometry analysis. Then, we propose a semiparametric model to characterize the complex spatial patterns in each customized subregion and boost the computational speed. Finally, a DNN model is designed to learn and detect fine-grained information of flaws. Experimental results show that the proposed process monitoring and control methodology detects flaws in each layer with an accuracy of 92.50±1.03%. This provides an opportunity to reduce inter-layer variation in AM prior to completion of the build. The proposed methodology can also be generally applicable in a variety of engineering and medical domains that entail image-based process monitoring and control with customized designs.

Reducing Bubble Defect on the Outsole Production Process in the Footwear Manufacturing Industry

2014 ◽  
Vol 988 ◽  
pp. 759-763
Author(s):  
Methinee Watcharaphassakorn ◽  
Prasad K.D.V. Yarlagadda
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Six Sigma ◽  
Production Cost ◽  
Manufacturing Industry ◽  
Relevant Literature ◽  
Experimental Studies ◽  
Process Quality ◽  
Mould Temperature ◽  
Significant Factors

The purpose of this study is to discover the significant factors causing the bubble defect on the outsoles manufactured by the Case Company. The bubble defect occurs approximately 1.5 per cent of the time or in 36 pairs per day. To understand this problem, experimental studies are undertaken to identify various factors such as injector temperature, mould temperature; that affects the production of waste. The work presented in this paper comprises a review of the relevant literature on the Six Sigma DMAIC improvement process, quality control tools, and the design of the experiments. After the experimentation following the Six Sigma process, the results showed that the defect occurred in approximately 0.5 per cent of the products or in 12 pairs per day; this decreased the production cost from 6,120 AUD per month to 2,040 AUD per month. This research aimed to reduce the amount of waste in men’s flat outsoles. Hence, the outcome of research presented in this paper should be used as a guide for applying the appropriate process for each type of outsole.

scholarly journals Special Issue of the Manufacturing Engineering Society 2020 (SIMES-2020)

Materials ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3208
Author(s):  
Eva María Rubio ◽  
Ana María Camacho
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Green Manufacturing ◽  
Manufacturing Processes ◽  
Special Issue ◽  
Manufacturing Automation ◽  
Engineering Society ◽  
Manufacturing Engineering

The Special Issue of the Manufacturing Engineering Society 2020 (SIMES-2020) has been launched as a joint issue of the journals “Materials” and “Applied Sciences”. The 17 contributions published in this Special Issue of Materials present cutting-edge advances in the field of Manufacturing Engineering, focusing on additive manufacturing and 3D printing; advances and innovations in manufacturing processes; sustainable and green manufacturing; manufacturing of new materials; manufacturing systems: machines, equipment and tooling; robotics, mechatronics and manufacturing automation; metrology and quality in manufacturing; Industry 4.0; design, modeling and simulation in manufacturing engineering. Among them, this issue highlights that the topic “advances and innovations in manufacturing processes” has collected a large number of contributions, followed by additive manufacturing and 3D printing; sustainable and green manufacturing; metrology and quality in manufacturing.

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

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