Application of data mining and process knowledge discovery in sheet metal assembly dimensional variation diagnosis

2002 ◽  
Vol 129 (1-3) ◽  
pp. 315-320 ◽  
Author(s):  
J Lian ◽  
X.M Lai ◽  
Z.Q Lin ◽  
F.S Yao
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Sheet Metal ◽  
Process Knowledge ◽  
Dimensional Variation ◽  
Sheet Metal Assembly

Variation Analysis for Compliant Assembly

2000 ◽  
Author(s):  
S. Jack Hu ◽  
Yufeng Long ◽  
Jaime Camelio
Keyword(s):  
Sheet Metal ◽  
Assembly Line ◽  
Assembly Process ◽  
Variation Analysis ◽  
Assembly Lines ◽  
Compliant Assembly ◽  
Single Station ◽  
Dimensional Variation ◽  
Sheet Metal Assembly

Abstract Assembly processes for compliant non-rigid parts are widely used in manufacturing automobiles, furniture, and electronic appliances. One of the major issues in the sheet metal assembly process is to control the dimensional variation of assemblies throughout the assembly line. This paper provides an overview of the recent development in variation analysis for compliant assembly. First, the unique characteristics of compliant assemblies are discussed. Then, various approaches to variation modeling for compliant assemblies are presented for single station and multi-station assembly lines. Finally, examples are given to demonstrate the applications of compliant assembly variation models.

A genetic algorithm-based fixture locating positions and clamping schemes optimization

2003 ◽  
Vol 217 (8) ◽  
pp. 1075-1083 ◽  
Author(s):  
Y G Liao
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Optimization Method ◽  
Form Accuracy ◽  
Dimensional Variation ◽  
Stationary Set ◽  
Machining Fixtures ◽  
Sheet Metal Assembly ◽  
Industrial Part ◽  
Selection Of

Optimizing the locator positions and clamping schemes of a fixture was proven to improve the dimensional and form accuracy of a workpiece significantly. A number of approaches have been developed to optimize the designs of sheet-metal assembly fixtures and machining fixtures. However, in these previous works, the optimal selection of the positions of locators and clamps were based on a stationary set of locator and clamp conditions; i.e. the numbers of the locators and clamps were fixed during optimization. This paper proposes a genetic algorithm (GA)-based optimization method to select automatically the optimal numbers of locators and clamps as well as their optimal positions in sheet-metal assembly fixtures, such that the workpiece deformation due to the gravity effect and resulting variation due to part dimensional variation are simultaneously minimized. The application result of a real industrial part demonstrated that the proposed algorithm effectively achieves the objective.

Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

2013 ◽  
Vol 4 (1) ◽  
pp. 18-27
Author(s):  
Ira Melissa ◽  
Raymond S. Oetama
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

The AI Delusion

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Industrial Revolution ◽  
The Real ◽  
Intelligent Machines ◽  
Black Boxes ◽  
Real Danger ◽  
The Way

We live in an incredible period in history. The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before. But our love of computers should not cloud our thinking about their limitations. We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are useless in judging whether the unearthed patterns are sensible because computers do not think the way humans think. We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions for us. The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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