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Fixturing technology and system for thin-walled parts machining: a review

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 4, doi: 10.1007/s11465-022-0711-5

Abstract: Fixture functions corresponding to the issues of machining and workholding are then clearly stated.Fixture categories are classified systematically according to previous research achievements, and theoperation mode, functional characteristics, and structure of each fixture are comprehensively described

Keywords： thin-walled part (TWP) fixture machining fixture categories fixture function

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Robotized machining of big work pieces: Localization of supporting heads

Wojciech SZYNKIEWICZ, Teresa ZIELIŃSKA, Włodzimierz KASPRZAK

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 4, Pages 357-369 doi: 10.1007/s11465-010-0103-0

Abstract: A planner for a self adaptable and reconfigurable fixture system is proposed.

Keywords： fixture robot milling drilling

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RCAnalyzer: visual analytics of rare categories in dynamic networks Research

Jia-cheng PAN, Dong-ming HAN, Fang-zhou GUO, Da-wei ZHOU, Nan CAO, Jing-rui HE, Ming-liang XU, Wei CHEN

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 4, Pages 491-506 doi: 10.1631/FITEE.1900310

Abstract: A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire structure. In this study, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verify the effectiveness of our system.

Keywords： Rare category detection Dynamic network Visual analytics