scholarly journals Comparative Study on the Influence of U-Shaped Self-Organizing Production Line Configuration on Capacity

2021 ◽  
Author(s):  
Shilong Liao ◽  
Wenwen Zhang
Keyword(s):  
Comparative Study ◽  
Production Line ◽  
Line Configuration ◽  
Self Organizing

scholarly journals Using Self Organizing Maps to Achieve Lithium-Ion Battery Cells Multi-Parameter Sorting Based on Principle Components Analysis

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2980 ◽  
Author(s):  
Bizhong Xia ◽  
Yadi Yang ◽  
Jie Zhou ◽  
Guanghao Chen ◽  
Yifan Liu ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Production Line ◽  
Lithium Ion ◽  
Principal Component ◽  
Sorting Algorithm ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Principle Components Analysis ◽  
Battery Module ◽  
Self Organizing

Battery sorting is an important process in the production of lithium battery module and battery pack for electric vehicles (EVs). Accurate battery sorting can ensure good consistency of batteries for grouping. This study investigates the mechanism of inconsistency of battery packs and process of battery sorting on the lithium-ion battery module production line. Combined with the static and dynamic characteristics of lithium-ion batteries, the battery parameters on the production line that can be used as a sorting basis are analyzed, and the parameters of battery mass, volume, resistance, voltage, charge/discharge capacity and impedance characteristics are measured. The data of batteries are processed by the principal component analysis (PCA) method in statistics, and after analysis, the parameters of batteries are obtained. Principal components are used as sorting variables, and the self-organizing map (SOM) neural network is carried out to cluster the batteries. Group experiments are carried out on the separated batteries, and state of charge (SOC) consistency of the batteries is achieved to verify that the sorting algorithm and sorting result is accurate.

Text mining with emergent self organizing maps and multi-dimensional scaling: A comparative study on domestic violence

2011 ◽  
Vol 11 (4) ◽  
pp. 3870-3876 ◽  
Author(s):  
Jonas Poelmans ◽  
Marc M. Van Hulle ◽  
Stijn Viaene ◽  
Paul Elzinga ◽  
Guido Dedene
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Comparative Study ◽  
Self Organizing Maps ◽  
Dimensional Scaling ◽  
Multi Dimensional Scaling ◽  
Self Organizing

A comparative study of disintegration actions of various disintegrants using Kohonen's self-organizing maps

2018 ◽  
Vol 43 ◽  
pp. 141-148 ◽  
Author(s):  
Yoshinori Onuki ◽  
Atsushi Kosugi ◽  
Masashi Hamaguchi ◽  
Yuki Marumo ◽  
Shungo Kumada ◽  
...  
Keyword(s):  
Comparative Study ◽  
Self Organizing Maps ◽  
Self Organizing

Autonomous Assembly Process Planning According to the Production Line Configuration

2015 ◽  
Vol 9 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Yasuhiro Sudo ◽  
◽  
Michiko Matsuda
Keyword(s):  
Production Line ◽  
Degrees Of Freedom ◽  
Assembly Line ◽  
Assembly Process ◽  
Production Lines ◽  
Virtual Production ◽  
Line Configuration ◽  
Autonomous Assembly ◽  
Simulation Results ◽  
Rewriting Rules

In this study, a virtual production line is used to present a method for generating assembly process-relational plans for a product according to the configurations of the production line and verify the effectiveness of the proposed method. In an autonomous production system, process-relational plans are generated dynamically by agents based on process-relation graphs. Usually, such process-relation graphs are not determined uniquely and often have some degrees of freedom. Therefore, more practical and efficient assembly process-relational plans would be obtained if process-relation graphs were rewritten according to changes in the configurations of actual production lines. In the proposed method, process-relation graphs are rewritten dynamically by agents using two simple rewriting rules. The results from simulations on a virtual assembly line provided that the progress of the assembly job differs with the quantities of invested jobs and machine layouts. Accordingly, the simulation results prove the usefulness of rewriting process-relation graphs according to the configurations of actual shop floors.

scholarly journals Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7366
Author(s):  
Yuchang Won ◽  
Seunghyeon Kim ◽  
Kyung-Joon Park ◽  
Yongsoon Eun
Keyword(s):  
Monitoring System ◽  
Continuous Improvement ◽  
Production System ◽  
Production Line ◽  
Production Systems ◽  
Productivity Improvement ◽  
Fault Monitoring ◽  
Line Configuration ◽  
Automotive Parts

This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not.

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