Multi-Objective Design Exploration of Automatic Transmission Casing Using Genetic Algorithm and Data Mining Techniques

2019 ◽  
Author(s):  
Kentaro Toda ◽  
Hiroki Yoshikawa ◽  
Koji Shimoyama
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Automatic Transmission ◽  
Design Exploration ◽  
Data Mining Techniques ◽  
Multi Objective

scholarly journals Multi-Objective Genetic Algorithm for an automatic transmission gear shift map

2016 ◽  
Vol 49 (3) ◽  
pp. 123-128 ◽  
Author(s):  
Adama Fofana ◽  
Olivier Haas ◽  
Vince Ersanilli ◽  
Keith Burnham ◽  
Joe Mahtani ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Automatic Transmission ◽  
Gear Shift ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Shift Map ◽  
Transmission Gear

Multi-Objective Robust Design Optimization and Knowledge Mining of a Centrifugal Fan That Takes Dimensional Uncertainty Into Account

2008 ◽  
Author(s):  
Kazuyuki Sugimura ◽  
Shinkyu Jeong ◽  
Shigeru Obayashi ◽  
Takeshi Kimura
Keyword(s):  
Data Mining ◽  
Robust Optimization ◽  
Robust Design ◽  
Rough Set Theory ◽  
Optimization Techniques ◽  
Robust Design Optimization ◽  
Centrifugal Fan ◽  
Data Mining Techniques ◽  
Multi Objective ◽  
Design Variables

A new design approach named MORDE (multi-objective robust design exploration), in which multi-objective robust optimization techniques and data mining techniques are combined, is proposed in this paper. We first developed a widely applicable design framework for multi-objective robust optimization. In this framework, probabilistic representation of design variables are introduced and Kriging models are used to approximate relations between design variables with uncertainty and multiple design objectives. A multi-objective genetic algorithm optimizes the mean and standard deviation of the responses. We then applied the framework to the real-world design problem of a centrifugal fan used in a washer-dryer. Taking dimensional uncertainty into account, we optimized the means and standard deviations of the resulting distributions of fan efficiency and turbulent noise level. Steady Reynolds-averaged Navier Stokes simulations were used to build Kriging models that approximate these objective functions. With the obtained non-dominated solutions, we demonstrated how to analyze features of solutions and select design candidates. We also attempted to acquire design knowledge by applying several data mining techniques. Self-organizing map was used to visualize and reuse the high dimensional design data. Decision tree analysis and rough set theory were used to extract design rules to improve the product’s performance. We also discussed differences in types of rules, which were extracted by both methods.

scholarly journals Data Mining Techniques and its Uses in different fields: A Review Paper

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Gaurav Dhawan
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Data Warehouse ◽  
Review Paper ◽  
High Standard ◽  
Raw Data ◽  
Data Mining Techniques ◽  
Useful Knowledge

Abstract: The paper introduced the Data Mining and issues related to it. Data mining is a technique by which we can extract useful knowledge from urge set of data. Data mining tasks used to perform various operations and used to solve various problems related to data mining. Data warehouse is the collection of different method and techniques used to extract useful information from raw data. Genetic Algorithm is based upon the Darwin’s Theory in which low standard chromosomes are removed from the population because of their inability to survive the process of selection. The high standard chromosomes survive and are mixed by recombination to form more appropriate individuals. In this urge amount of data is used to predict future result by following several steps.

scholarly journals Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

2020 ◽  
Vol 10 (21) ◽  
pp. 7794
Author(s):  
Heng Zhang ◽  
Xinxin Zhao ◽  
Jue Yang ◽  
Wenming Zhang
Keyword(s):  
Genetic Algorithm ◽  
Energy Loss ◽  
Automatic Transmission ◽  
Multi Objective ◽  
Optimal Analysis ◽  
Multi Objective Genetic Algorithm ◽  
Powertrain System ◽  
Double Transition ◽  
Single Transition ◽  
Transition Shift

In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time.

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