Discovery of path-attribute dependency in manufacturing environments: A process mining approach

2021 ◽  
Vol 61 ◽  
pp. 54-65
Author(s):  
Alexandre Checoli Choueiri ◽  
Eduardo Alves Portela Santos
Keyword(s):  
Process Mining ◽  
Manufacturing Environments

Process Mining - A Comprehensive Review

2018 ◽  
Vol 6 (7) ◽  
pp. 1108-1113
Author(s):  
S. Vijayarani ◽  
A. Sakila ◽  
R. Ramya
Keyword(s):  
Process Mining ◽  
Comprehensive Review

Process Mining im Prototypenbau

2019 ◽  
Vol 114 (11) ◽  
pp. 707-710
Author(s):  
Günther Schuh ◽  
Jan-Philipp Prote ◽  
Andreas Gützlaff ◽  
Sven Cremer ◽  
Seth Schmitz
Keyword(s):  
Process Mining

PCB Related Field Failures with ImAg Surface Finishes

2007 ◽  
Author(s):  
Anil Kurella ◽  
Aravind Munukutla ◽  
J.S. Lewis
Keyword(s):  
Emerging Markets ◽  
Galvanic Corrosion ◽  
Electrochemical Migration ◽  
Copper Sulphide ◽  
Electronic Components ◽  
Related Field ◽  
Surface Finishes ◽  
Immersion Silver ◽  
Manufacturing Environments

Abstract PCB surface finishes like Immersion silver (ImAg) are commonly used in Pb-free manufacturing environments following RoHS legislation. With this transition, however the numbers of field failures associated with electrochemical migration, copper sulphide corrosion, via barrel galvanic corrosion are on a steady rise. More often than not ImAg surfaces seem to assist these failing signatures. As computers penetrate into emerging markets with humid and industrialized environments there is a greater concern on the reliability and functionality of these electronic components.

Process Mining for Clinical Workflows

2015 ◽  
Vol 2 (2) ◽  
pp. 17-10
Author(s):  
Neha Garg ◽  
◽  
Sonali Agarwal ◽  
Keyword(s):  
Process Mining

scholarly journals Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources

2021 ◽  
Vol 11 (7) ◽  
pp. 3188
Author(s):  
Xixiang Wang ◽  
Jiafu Wan
Keyword(s):  
Knowledge Sharing ◽  
Comparative Experiment ◽  
Mixed Flow ◽  
Processing Technologies ◽  
Experiment Platform ◽  
Low Degree ◽  
Manufacturing Resources ◽  
Ontological Model ◽  
Manufacturing Environments ◽  
Similar Processing

The development of multi-variety, mixed-flow manufacturing environments is hampered by a low degree of automation in information and empirical parameters’ reuse among similar processing technologies. This paper proposes a mechanism for knowledge sharing between manufacturing resources that is based on cloud-edge collaboration. The manufacturing process knowledge is coded using an ontological model, based on which the manufacturing task is refined and decomposed to the lowest-granularity concepts, i.e., knowledge primitives. On this basis, the learning process between devices is realized by effectively screening, matching, and combining the existing knowledge primitives contained in the knowledge base deployed on the cloud and the edge. The proposed method’s effectiveness was verified through a comparative experiment contrasting manual configuration and knowledge sharing configuration on a multi-variety, small-batch manufacturing experiment platform.

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