Intelligent data-driven monitoring of high dimensional multistage manufacturing processes

2020 ◽  
Vol 13 (4) ◽  
pp. 299
Author(s):  
Mohammadhossein Amini ◽  
Shing I. Chang
Keyword(s):  
Data Driven ◽  
High Dimensional ◽  
Manufacturing Processes ◽  
Multistage Manufacturing

Engineering-Driven Factor Analysis for Variation Source Identification in Multistage Manufacturing Processes

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Jian Liu ◽  
Jianjun Shi ◽  
S. Jack Hu
Keyword(s):  
Factor Analysis ◽  
Source Identification ◽  
Domain Knowledge ◽  
Measurement Data ◽  
Factor Loading ◽  
Data Driven ◽  
Manufacturing Processes ◽  
Multistage Manufacturing ◽  
Engineering Models

Variation source identification is an important task of quality assurance in multistage manufacturing processes (MMPs). However, existing approaches, including the quantitative engineering-model-based methods and the data-driven methods, provide limited capabilities in variation source identification. This paper proposes a new methodology that does not depend on accurate quantitative engineering models. Instead, engineering domain knowledge about the interactions between potential variation sources and product quality variables is represented as qualitative indicator vectors. These indicator vectors guide the rotation of the factor loading vectors that are derived from factor analysis of the multivariate measurement data. Based on this engineering-driven factor analysis, a procedure is presented to identify multiple variation sources that are present in a MMP. The effectiveness of the proposed methodology is demonstrated in a case study of a three-stage assembly process.

scholarly journals Metal Machining—Recent Advances, Applications, and Challenges

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 580
Author(s):  
Francisco J. G. Silva
Keyword(s):  
Surface Finish ◽  
Manufacturing Process ◽  
Dimensional Accuracy ◽  
Machining Process ◽  
High Dimensional ◽  
Manufacturing Processes ◽  
Recent Advances ◽  
Metal Machining ◽  
Rapid Manufacture

Though new manufacturing processes that revolutionize the landscape regarding the rapid manufacture of parts have recently emerged, the machining process remains alive and up-to-date in this context, always presenting itself as a manufacturing process with several variants and allowing for high dimensional accuracy and high levels of surface finish [...]

scholarly journals Identification of Granule Growth Regimes in High Shear Wet Granulation Processes Using a Physics-Constrained Neural Network

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 737
Author(s):  
Chaitanya Sampat ◽  
Rohit Ramachandran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Data Driven ◽  
Wet Granulation ◽  
Manufacturing Processes ◽  
High Shear ◽  
Process Data ◽  
Physical Constraints ◽  
Granule Growth

The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulation process. The physics-constrained neural network (PCNN) was better at predicting granule growth regimes when compared to other neural networks with no physical constraints. When input data that violated physics-based boundaries was provided, the PCNN identified these points more accurately compared to other non-physics constrained neural networks, with an error of <1%. A sensitivity analysis of the PCNN to the input variables was also performed to understand individual effects on the final outputs.

High-Dimensional Data-Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

2018 ◽  
Vol 89 (7) ◽  
pp. 1800015 ◽  
Author(s):  
Siwei Wu ◽  
Xiaoguang Zhou ◽  
Guangming Cao ◽  
Naian Shi ◽  
Zhenyu Liu
Keyword(s):  
Optimal Design ◽  
Microalloyed Steel ◽  
High Dimensional Data ◽  
Data Driven ◽  
High Dimensional ◽  
Hot Strip Rolling ◽  
Strip Rolling ◽  
Hot Strip

Data-driven quality improvement approach to reducing waste in manufacturing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rose Clancy ◽  
Dominic O'Sullivan ◽  
Ken Bruton
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Quality Management ◽  
Process Improvement ◽  
Six Sigma ◽  
Data Driven ◽  
Manufacturing Processes ◽  
Management Performance ◽  
Chain Management ◽  
Content Type

PurposeData-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.Design/methodology/approachMethodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.FindingsUpon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.Practical implicationsValuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.Originality/valueThis study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.

Process-Oriented Tolerance Synthesis for Multistage Manufacturing Systems

2000 ◽  
Author(s):  
Yu Ding ◽  
Jionghua Jin ◽  
Dariusz Ceglarek ◽  
Jianjun Shi
Keyword(s):  
Product Design ◽  
Process Design ◽  
Manufacturing Systems ◽  
Minimum Cost ◽  
Manufacturing Processes ◽  
Industrial Case Study ◽  
Multistage Manufacturing ◽  
Process Oriented ◽  
Design Characteristics

Abstract In multistage manufacturing systems, quality of final products is strongly affected not only by product design characteristics but also by key process design characteristics. However, historically, tolerance research has primarily focused on allocating tolerances based on product design characteristics for each component. Currently, there is no analytical approach for multistage manufacturing processes to optimally allocate tolerances to integrate product and process characteristics at minimum cost. One of the major obstacles is that the relationship between tolerances of process and product characteristics is not well understood and modeled. Under this motivation, this paper aims at presenting a framework addressing the process-oriented (rather than product-oriented) tolerancing technique for multistage manufacturing processes. Based on a developed state space model, tolerances of process design characteristics at each fabrication stage are related to the quality of final product. All key elements in the framework are described and then derived for a multistage assembly process. An industrial case study is used to illustrate the proposed approach.

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