scholarly journals Effects of ungulate density and sociality on landscape heterogeneity: a mechanistic modeling approach

Ecography ◽  
2021 ◽  
Author(s):  
Kristy M. Ferraro ◽  
Oswald J. Schmitz ◽  
Matthew A. McCary
Keyword(s):  
Landscape Heterogeneity ◽  
Mechanistic Modeling ◽  
Modeling Approach

Mechanistic Modeling of the Ball End Milling Process for Multi-Axis Machining of Free-Form Surfaces

2000 ◽  
Vol 123 (3) ◽  
pp. 369-379 ◽  
Author(s):  
Rixin Zhu ◽  
Shiv G. Kapoor ◽  
Richard E. DeVor
Keyword(s):  
End Milling ◽  
Chip Thickness ◽  
Mechanistic Modeling ◽  
Free Form ◽  
Surface Geometry ◽  
Workpiece Surface ◽  
Ball End Milling ◽  
Modeling Approach ◽  
Free Form Surfaces ◽  
Cutting Point

A mechanistic modeling approach to predicting cutting forces is developed for multi-axis ball end milling of free-form surfaces. The workpiece surface is represented by discretized point vectors. The modeling approach employs the cutting edge profile in either analytical or measured form. The engaged cut geometry is determined by classification of the elemental cutting point positions with respect to the workpiece surface. The chip load model determines the undeformed chip thickness distribution along the cutting edges with consideration of various process faults. Given a 5-axis tool path in a cutter location file, shape driving profiles are generated and piecewise ruled surfaces are used to construct the tool swept envelope. The tool swept envelope is then used to update the workpiece surface geometry employing the Z-map method. A series of 3-axis and 5-axis surface machining tests on Ti6A14V were conducted to validate the model. The model shows good computational efficiency, and the force predictions are found in good agreement with the measured data.

scholarly journals Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1625
Author(s):  
Maximilian Krippl ◽  
Ignasi Bofarull-Manzano ◽  
Mark Duerkop ◽  
Astrid Dürauer
Keyword(s):  
Film Theory ◽  
Hybrid Models ◽  
Mechanistic Modeling ◽  
Time Resolved ◽  
Process Stage ◽  
Modeling Approach ◽  
Crossflow Ultrafiltration ◽  
Wide Range ◽  
Rejection Factor ◽  
The Impact

Ultrafiltration is a powerful method used in virtually every pharmaceutical bioprocess. Depending on the process stage, the product-to-impurity ratio differs. The impact of impurities on the process depends on various factors. Solely mechanistic models are currently not sufficient to entirely describe these complex interactions. We have established two hybrid models for predicting the flux evolution, the protein rejection factor and two components’ concentration during crossflow ultrafiltration. The hybrid models were compared to the standard mechanistic modeling approach based on the stagnant film theory. The hybrid models accurately predicted the flux and concentration over a wide range of process parameters and product-to-impurity ratios based on a minimum set of training experiments. Incorporating both components into the modeling approach was essential to yielding precise results. The stagnant film model exhibited larger errors and no predictions regarding the impurity could be made, since it is based on the main product only. Further, the developed hybrid models exhibit excellent interpolation properties and enable both multi-step ahead flux predictions as well as time-resolved impurity forecasts, which is considered to be a critical quality attribute in many bioprocesses. Therefore, the developed hybrid models present the basis for next generation bioprocessing when implemented as soft sensors for real-time monitoring of processes.

scholarly journals Automatic recognition of tool wear on a face mill using a mechanistic modeling approach

Wear ◽  
1992 ◽  
Vol 157 (2) ◽  
pp. 305-323 ◽  
Author(s):  
Daniel J. Waldorf ◽  
Shiv G. Kapoor ◽  
Richard E. DeVor
Keyword(s):  
Tool Wear ◽  
Automatic Recognition ◽  
Mechanistic Modeling ◽  
Modeling Approach ◽  
Face Mill

Food Product Design: A Hybrid Machine Learning and Mechanistic Modeling Approach

2019 ◽  
Vol 58 (36) ◽  
pp. 16743-16752 ◽  
Author(s):  
Xiang Zhang ◽  
Teng Zhou ◽  
Lei Zhang ◽  
Ka Yip Fung ◽  
Ka Ming Ng
Keyword(s):  
Machine Learning ◽  
Product Design ◽  
Food Product ◽  
Mechanistic Modeling ◽  
Modeling Approach ◽  
Hybrid Machine
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