Real-time fuzzy logic control for maximising the tool life of small-diameter drills
Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Machine Learning
Maximum Consistency of Incomplete Datavia Non-Invasive Imputation
Artificial Intelligence Review
Benchmarking k-nearest neighbour imputation with homogeneous Likert data
Empirical Software Engineering
Handling incomplete data using evolution of imputation methods
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Prediction-oriented dimensionality reduction of industrial data sets
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Using artificial intelligence to predict surface roughness in deep drilling of steel components
Journal of Intelligent Manufacturing
The evolutionary development of roughness prediction models
Applied Soft Computing
Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing
Journal of Intelligent Manufacturing
Improvement of surface roughness models for face milling operations through dimensionality reduction
Integrated Computer-Aided Engineering
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A soft computing system used to optimize deep drilling operations under high-speed conditions in the manufacture of steel components is presented. The input data includes cutting parameters and axial cutting force obtained from the power consumption of the feed motor of the milling centres. Two different coolant strategies are tested: traditional working fluid and Minimum Quantity Lubrication (MQL). The model is constructed in three phases. First, a new strategy is proposed to evaluate and complete the set of available measurements. The primary objective of this phase is to decide whether further drilling experiments are required to develop an accurate roughness prediction model. An important aspect of the proposed strategy is the imputation of missing data, which is used to fully exploit both complete and incomplete measurements. The proposed imputation algorithm is based on a genetic algorithm and aims to improve prediction accuracy. In the second phase, a bag of multilayer perceptrons is used to model the impact of deep drilling settings on borehole roughness. Finally, this model is supplied with the borehole dimensions, coolant option and expected axial force to develop a 3D surface showing the expected borehole roughness as a function of drilling process settings. This plot is the necessary output of the model for its use under real workshop conditions. The proposed system is capable of approximating the optimal model used to control deep drilling tasks on steel components for industrial use.