Rule-based cutting condition recommendation system for intelligent machine tools

2009 ◽  
Vol 23 (4) ◽  
pp. 1202-1210 ◽  
Author(s):  
Seung Woo Lee ◽  
Hwa Ki Lee
Keyword(s):  
Machine Tools ◽  
Recommendation System ◽  
Cutting Condition ◽  
Rule Based ◽  
Intelligent Machine

scholarly journals A Study on Drilling Process Control by Intelligent Machine Tools. (1st Report). Determination of Cutting Condition for Drilling.

2000 ◽  
Vol 66 (8) ◽  
pp. 1270-1274 ◽  
Author(s):  
Tomonori SATI ◽  
Yoshiaki KAKINO ◽  
Atsushi MATSUBARA ◽  
Makoto FUJISHIMA ◽  
Isao NISHIURA ◽  
...  
Keyword(s):  
Process Control ◽  
Machine Tools ◽  
Cutting Condition ◽  
Drilling Process ◽  
Intelligent Machine

scholarly journals A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context

2022 ◽  
Vol 12 (1) ◽  
pp. 515
Author(s):  
Amina Ouatiq ◽  
Kamal ElGuemmat ◽  
Khalifa Mansouri ◽  
Mohammed Qbadou
Keyword(s):  
Mental State ◽  
Adaptive Systems ◽  
Recommendation System ◽  
Unified Modeling Language ◽  
Social Situation ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning System ◽  
Holistic View ◽  
Rule Based

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.

Globalisation and Openness of Intelligent Machine Tools Design System

2001 ◽  
Vol 34 (2) ◽  
pp. 93-99
Author(s):  
Jerzy Jędrzejewski ◽  
Zbigniew Kowal ◽  
Wojciech Kwaśny ◽  
Wojciech Modrzycki
Keyword(s):  
Machine Tools ◽  
Design System ◽  
Intelligent Machine ◽  
Machine Tools Design

scholarly journals A Novel Rule based Data Mining Approach towards Movie Recommender System

2020 ◽  
Vol 44 (1) ◽  
pp. 157-170
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Phase 1 ◽  
Research Work ◽  
Rule Learning ◽  
Rule Base ◽  
Individual Member ◽  
Phase 2 ◽  
Group Recommendation ◽  
Rule Based

The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.

Intelligent Machine Tools

1993 ◽  
Vol 96 (901) ◽  
pp. 1010-1014 ◽  
Author(s):  
Toshimichi Moriwaki
Keyword(s):  
Machine Tools ◽  
Intelligent Machine

High Speed, High Productive Tapping by Intelligent Machine Tools

2000 ◽  
Vol 2000.2 (0) ◽  
pp. 35-36 ◽  
Author(s):  
Yoshinori YAMAOKA ◽  
Yoshiaki KAKINO ◽  
Yasuhiko SUZUKI
Keyword(s):  
Machine Tools ◽  
High Speed ◽  
Intelligent Machine

Integration of Adaptive Control Functions for Drilling in Intelligent Machine Tools

2000 ◽  
Vol 2000.2 (0) ◽  
pp. 49-50 ◽  
Author(s):  
Makoto FUJISHIMA ◽  
Yoshiaki KAKINO ◽  
Atsushi Matsubara
Keyword(s):  
Adaptive Control ◽  
Machine Tools ◽  
Intelligent Machine ◽  
Control Functions
Sign in / Sign up

Export Citation Format

Share Document