Application of Data-Mining to Factor Analysis of Micro-Drilled Hole Quality for Multi-Layer PWBs

2005 ◽  
Author(s):  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
Keiji Ogawa ◽  
Kenichi Mori
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Tool Wear ◽  
Printed Wiring Boards ◽  
Hole Quality ◽  
Copper Foils ◽  
Drill Cutting ◽  
Drill Tool

Recently, the demand for miniaturization and enhanced performance of printed wiring boards (PWBs) is growing more and more intense with the rapid popularization of information devices. Processing with micro-drills is generally used to make smaller diameter through-holes in PWBs, which are desired for the miniaturization of the equipment. However, the nail heading is one of the problems in the formation of the micro-through-holes. The purpose of this study is to apply a data-mining, which is used to statistically analyze given factors, to the obtained nail heading data of micro-drilled hole walls, and to elucidate the factors that influence the nail heading of the micro-drilled hole walls. The following conclusions were obtained. (1) It was clear that the height of the nail heading was affected by the drill cutting distance of copper foils and the amount of the drill tool wear more than the number of drilling hits or the drill cutting distance of the PWB number of drilled holes. (2) Data-mining was found to be effective to analyze factors which influence on the micro-drilled hole quality in the manufacturing field of PWBs.

scholarly journals Factor Analysis on Internal Damage around Small Daimeter Drilled Hole of GFRP Printed Wiring Boards by Data Mining Method

2004 ◽  
Vol 70 (693) ◽  
pp. 1535-1541 ◽  
Author(s):  
Toshiki HIROGAKI ◽  
Eiichi AOYAMA ◽  
Tadayuki IKUTA ◽  
Hiromichi NOBE ◽  
Katutoshi YAMAUCHI ◽  
...  
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Mining Method ◽  
Printed Wiring Boards ◽  
Internal Damage ◽  
Data Mining Method

scholarly journals Inequalities in the Exercise and Continuity in Building Engineering in Spain. Factor Analysis Including Gender Perspective

2021 ◽  
Vol 13 (10) ◽  
pp. 5514
Author(s):  
Irantzu Recalde-Esnoz ◽  
Daniel Ferrández ◽  
Carlos Morón ◽  
Guadalupe Dorado
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Gender Inequality ◽  
Sustainable Development Goals ◽  
The Sustainable Development ◽  
Agenda 2030 ◽  
Technical Architecture ◽  
Development Goals ◽  
Building Engineering ◽  
New Jobs

The building sector is one of the most relevant at world level in view of the percentage of gross domestic product (GDP) concerned, as well as the number of new jobs created. Nevertheless, it is a completely male-dominated industry. Different institutions and organisms, such as the Agenda 2030 and the Sustainable Development Goals, struggle to reduce gender inequality in different environments, including the working one. Aligned with these goals, this study provides the data exploited from the first survey regarding gender inequality within the professionals of the building engineering field in the Spanish population as a whole. This survey was developed in 2018 by the Spanish General Council of Technical Architecture and it was sent to its members. The sample involved 1353 cases. For this data mining, bivariate analyses were conducted in order to subsequently carry out a factor analysis and the socio–demographic composition of the dimensions found. Results exposed statistically meaningful differences in the eyes of women and men about those factors which facilitate practice and continuity in the profession. The most relevant conclusions drawn from the factor analysis reflect the existence of three factors: (1) work competences, (2) social capital and (3) physical appearance and being a man, dimensions in which women and men’s opinion was unevenly distributed.

On Miniature Hole Quality and Tool Wear When Mechanical Drilling of Mild Steel

2020 ◽  
Vol 45 (11) ◽  
pp. 8917-8929 ◽  
Author(s):  
Ali Abdelhafeez Hassan ◽  
Mao Jun Li ◽  
Saad Mahmoud
Keyword(s):  
Tool Wear ◽  
Mild Steel ◽  
Hole Quality

THRUST FORCE PREDICTION MODEL FOR DRILLING UNIDIRECTIONAL CARBON PLASTICS WITH WEAR OF THE CUTTING TOOL

2021 ◽  
Vol 2 ◽  
pp. 100-108
Author(s):  
V.I. GOLOVIN ◽  
S.YU. RADCHENKO
Keyword(s):  
Carbon Fiber ◽  
Tool Wear ◽  
Prediction Model ◽  
Cutting Tool ◽  
Thrust Force ◽  
Contact Conditions ◽  
Force Prediction ◽  
Geometric Characteristics ◽  
Drill Tool

One of the most important tasks in the production of carbon fiber products is to ensure the specified accuracy and quality of the drilled holes. A model for predicting the thrust force, taking into account the degree of tool wear, is proposed. First, the geometric characteristics of the wear of the spiral drill tool are analyzed. Further, the contact conditions between the drill and the carbon fiber are determined in accordance with the geometric characteristics of the tool wear. Based on the mechanics of the contact interaction of surfaces, the thrust forces of the main cutting edges and the chisel edge are modeled, taking into account the degree of tool wear. The results obtained are integrated into the thrust force prediction model. An example of an experimental study of the drilling carbon fiber is given. The results obtained show a fairly accurate prediction of the cutting force, taking into account the wear of the cutting tool.

Factor Analysis in Data Mining

2011 ◽  
pp. 498-502 ◽  
Author(s):  
Zu-Hsu Lee ◽  
Richard L. Peterson ◽  
Chen-Fu Chien ◽  
Ruben Xing
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Factor Analysis ◽  
Rapid Growth ◽  
Data Warehousing ◽  
Analytical Techniques ◽  
Information Resources ◽  
Business People ◽  
New Information

The rapid growth and advances of information technology enable data to be accumulated faster and in much larger quantities (i.e., data warehousing). Faced with vast new information resources, scientists, engineers, and business people need efficient analytical techniques to extract useful information and effectively uncover new, valuable knowledge patterns.

Some considerations on data mining from questionnaires by constructing fuzzy signatures based on factor analysis

2019 ◽  
Vol 36 (4) ◽  
pp. 3739-3749 ◽  
Author(s):  
Laszlo T. Koczy ◽  
Ojaras Purvinis ◽  
Dalia Susniene
Keyword(s):  
Data Mining ◽  
Factor Analysis
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