Computer-aided geometrical dimensioning and tolerancing for process-operation planning and quality control

1992 ◽  
Vol 7 (1) ◽  
pp. 11-20 ◽  
Author(s):  
J. R. He ◽  
P. R. Gibson
Keyword(s):  
Quality Control ◽  
Operation Planning ◽  
Dimensioning And Tolerancing ◽  
Computer Aided ◽  
Process Operation ◽  
Geometrical Dimensioning And Tolerancing

Machining Feature-Based CAD/CAPP for STEP-NC

2014 ◽  
Vol 598 ◽  
pp. 591-594 ◽  
Author(s):  
Li Yan Zhang
Keyword(s):  
Process Planning ◽  
Manufacturing Process ◽  
Data Transfer ◽  
Operation Planning ◽  
Computer Aided Process Planning ◽  
Machining Feature ◽  
Computer Aided ◽  
Cad Cam ◽  
Feature Based ◽  
Cam Systems

ISO 14649, known as STEP-NC, is new model of data transfer between CAD/CAM systems and CNC machines. In this paper, the modeling based on machining feature is proposed. The machining feature comes from the manufacturing process considering the restriction of machining technology and machining resource. Then the framework for computer aided process planning is presented, where the algorithms of operation planning is studied. The practical example has been provided and results indicate that machining feature based model can integrate with CAPP and STEP-NC seamlessly.

Intelligent Automated Welding for Shipyard Applications

1992 ◽  
Vol 8 (02) ◽  
pp. 77-88
Author(s):  
S. Madden ◽  
H. H. Vanderveldt ◽  
J. Jones
Keyword(s):  
Neural Networks ◽  
Computer Technology ◽  
Computer Aided Design ◽  
Data Systems ◽  
Fully Integrated ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Process Operation ◽  
Aided Design ◽  
Computerized System

Computer Aided Process Planning (CAPP) integrated with Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) will form the basis of engineering/planning systems of the future. These systems will have the capability to operate in a paperless environment and provide highly optimized process operation plans. The WELDEXCELL System is a prototype of such a system for welding in shipyards. The paper discusses three significant computer technology advances which have been in into the WELDEXCELL prototype. First is a computerized system for allowing multiple knowledge sources (expert systems, humans, data systems, etc.) to work together to solve a common problem (the weld plan). This system is called a "blackboard." The second is a methodology for the blackboard to communicate to the human user. This interface includes full interactive graphics fully integrated to CAD as well as data searches and automatic completion of routine engineering tasks. The third is artificial neural networks (ANS's), which are based on biological neural networks (such as the human brain) and which can do neural reasoning tasks about difficult problems. ANS's offer the opportunity to model highly complex multivariable and nonlinear processes (for example, welding) and provide a means for an engineer to quantitatively assess the process and its operation.

scholarly journals Impact of Variability in Portal Venous Phase Acquisition Timing in Tumor Density Measurement and Treatment Response Assessment: Metastatic Colorectal Cancer as a Paradigm

2017 ◽  
pp. 1-8 ◽  
Author(s):  
Laurent Dercle ◽  
Lin Lu ◽  
Philip Lichtenstein ◽  
Hao Yang ◽  
Deling Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Quality Control ◽  
Metastatic Colorectal Cancer ◽  
Imaging Biomarkers ◽  
Portal Venous Phase ◽  
Computed Tomography Images ◽  
Choi Criteria ◽  
Computer Aided ◽  
Venous Phase ◽  
The Mean

Purpose New response patterns to anticancer drugs have led tumor size–based response criteria to shift to also include density measurements. Choi criteria, for instance, categorize antiangiogenic therapy response as a decrease in tumor density > 15% at the portal venous phase (PVP). We studied the effect that PVP timing has on measurement of the density of liver metastases (LM) from colorectal cancer (CRC). Methods Pretreatment PVP computed tomography images from 291 patients with LM-CRC from the CRYSTAL trial (Cetuximab Combined With Irinotecan in First-Line Therapy for Metastatic Colorectal Cancer; ClinicalTrials.gov identifier: NCT00154102) were included. Four radiologists independently scored the scans’ timing according to a three-point scoring system: early, optimal, late PVP. Using this, we developed, by machine learning, a proprietary computer-aided quality-control algorithm to grade PVP timing. The reference standard was a computer-refined consensus. For each patient, we contoured target liver lesions and calculated their mean density. Results Contrast-product administration data were not recorded in the digital imaging and communications in medicine headers for injection volume (94%), type (93%), and route (76%). The PVP timing was early, optimal, and late in 52, 194, and 45 patients, respectively. The mean (95% CI) accuracy of the radiologists for detection of optimal PVP timing was 81.7% (78.3 to 85.2) and was outperformed by the 88.6% (84.8 to 92.4) computer accuracy. The mean ± standard deviation of LM-CRC density was 68 ± 15 Hounsfield units (HU) overall and 59.5 ± 14.9 HU, 71.4 ± 14.1 HU, 62.4 ± 12.5 HU at early, optimal, and late PVP timing, respectively. LM-CRC density was thus decreased at nonoptimal PVP timing by 14.8%: 16.7% at early PVP ( P < .001) and 12.6% at late PVP ( P < .001). Conclusion Nonoptimal PVP timing should be identified because it significantly decreased tumor density by 14.8%. Our computer-aided quality-control system outperformed the accuracy, reproducibility, and speed of radiologists’ visual scoring. PVP-timing scoring could improve the extraction of tumor quantitative imaging biomarkers and the monitoring of anticancer therapy efficacy at the patient and clinical trial levels.

Geometrical characteristics of machined shape for computer aided operation planning

1998 ◽  
Vol 76 (1-3) ◽  
pp. 109-114 ◽  
Author(s):  
Fumiki Tanaka ◽  
Takeshi Kishinami
Keyword(s):  
Operation Planning ◽  
Geometrical Characteristics ◽  
Computer Aided

Computer-aided tests in the quality control system of aircraft engines

2012 ◽  
Vol 55 (1) ◽  
pp. 101-107
Author(s):  
R. I. Adgamov ◽  
S. V. Dmitriev ◽  
A. Kh. Khairullin
Keyword(s):  
Quality Control ◽  
Control System ◽  
Quality Control System ◽  
Aircraft Engines ◽  
Computer Aided
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