scholarly journals Development of Prediction System of Environmental Burden for Machine Tool Operation

2008 ◽  
Vol 3 (2) ◽  
pp. 307-315 ◽  
Author(s):  
Hirohisa NARITA ◽  
Hiroshi KAWAMURA ◽  
Lian-yi CHEN ◽  
Hideo FUJIMOTO ◽  
Takashi NORIHISA ◽  
...  
Keyword(s):  
Machine Tool ◽  
Prediction System ◽  
Environmental Burden

Prediction System of Environmental Burden for Machine Tool Operation

2004 ◽  
Vol 2004.4 (0) ◽  
pp. 319-320
Author(s):  
Hirohisa NARITA ◽  
Takashi NORIHISA ◽  
Lian-yi CHEN ◽  
Hideo FUJIMOTO ◽  
Takao HASEBE
Keyword(s):  
Machine Tool ◽  
Prediction System ◽  
Environmental Burden

scholarly journals Development of Prediction System for Environmental Burden for Machine Tool Operation

2006 ◽  
Vol 49 (4) ◽  
pp. 1188-1195 ◽  
Author(s):  
Hirohisa NARITA ◽  
Hiroshi KAWAMURA ◽  
Takashi NORIHISA ◽  
Lian-yi CHEN ◽  
Hideo FUJIMOTO ◽  
...  
Keyword(s):  
Machine Tool ◽  
Prediction System ◽  
Environmental Burden

scholarly journals An Integrated Curvature Surface Inspection and Prediction System for 5-Axis Synchronization Machining

2021 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Wei-Han Weng
Keyword(s):  
Surface Roughness ◽  
Machine Tool ◽  
Surface Quality ◽  
Surface Texture ◽  
High Speed ◽  
Free Form ◽  
Inspection System ◽  
Prediction System ◽  
Roughness Prediction ◽  
Five Axis

Abstract There is an urgent demand for free form products in industry at the present time because of their superior appearance and the wide variety of functions they perform. Five-axis high-speed CNC machining technology has developed to satisfy this demand, but further improvement in surface quality metric inspection technology is the big challenge it now faces. In this study, the effects of jerk on the performance of five-axis synchronous high-speed CNC ball nose end mills on a freeform turbine mold were investigated. The relationships of characteristics of the images of 14 jerk-cluster finished workpieces with different jerk setting values were established, allowing surface texture features to be analyzed and surface roughness predicted. In addition, machine learning methods were integrated with the surface feature analysis to construct a virtual machining module that acts as a performance prediction system, merging the virtual machine tool functions, surface texture processor and AI roughness prediction processor. Using the geometric information of the workpiece, cutting parameters and machine tool parameters as inputs, product performance metrics combining surface roughness and machining time can be predicted as outputs of the system. The integrated system provides users with a way to evaluate manufacturing performance before performing actual operations and to reduce test time for cutting parameter development. The model is suitable for complex surface finishes as well as for the production of small batches with high parametric variance. In addition, the partial set of image processing and roughness prediction modules can be used alone as an effective intelligent surface quality inspection system.

scholarly journals Analysis of Environmental Impact due to Machine Tool Operation

2009 ◽  
Vol 3 (1) ◽  
pp. 49-55 ◽  
Author(s):  
Hirohisa Narita ◽  
◽  
Hideo Fujimoto
Keyword(s):  
Environmental Impact ◽  
Machine Tool ◽  
Human Toxicity ◽  
Impact Categories ◽  
Dry Machining ◽  
Environmental Burden ◽  
Minimum Quantity Lubricant ◽  
Machining Operations ◽  
The Impact ◽  
Photochemical Oxidants

The algorithm we propose calculates the environmental burden due to machine tool operations. The environmental burden analyzer we then developed determines the environmental impact of dry machining, minimum quantity lubricant (MQL) machining, and wet machining on the impact categories of global warming, acidification, eutrophication, photochemical oxidants, human toxicity, and ecotoxicity. We explain the features of impact categories as they relate to machining operations and discuss machining methods for lower the environmental burden of machine tool operations.

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