Topic
Soft computing
About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.
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TL;DR: A soft computing system used to optimize deep drilling operations under high-speed conditions in the manufacture of steel components is presented and is capable of approximating the optimal model used to control deep drilling tasks on steel components for industrial use.
Abstract: 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.
32 citations
Book•
01 Mar 2001
32 citations
28 Jun 1993
TL;DR: The past three years have witnessed a significant increase in the rate of growth of MIQ (Machine Intelligence Quotient) of consumer products and industrial systems.
Abstract: The past three years have witnessed a significant increase in the rate of growth of MIQ (Machine Intelligence Quotient) of consumer products and industrial systems.
32 citations
TL;DR: The proposed deep swarm-optimized classifier is a feature-boosted DT, which learns features using a deep convolution net and an optimal feature set built using a metaheuristic WSA, which outperforms the other considered algorithms in terms of the classification accuracy.
Abstract: To compete in the current data-driven economy, it is essential that industrial manufacturers leverage real-time tangible information assets and embrace big data technologies. Data classification is one of the most proverbial analytical techniques within the cognitively capable manufacturing industries for finding the patterns in the structured and unstructured data at the plant, enterprise, and industry levels. This article presents a cognition-driven analytics model, CNN-WSADT, for the real-time data classification using three soft computing techniques, namely, deep learning [convolution neural network (CNN)], machine learning [decision tree (DT)], and swarm intelligence [wolf search algorithm (WSA)]. The proposed deep swarm-optimized classifier is a feature-boosted DT, which learns features using a deep convolution net and an optimal feature set built using a metaheuristic WSA. The performance of CNN-WSADT is studied on two benchmark datasets and the experimental results depict that the proposed cognition model outperforms the other considered algorithms in terms of the classification accuracy.
32 citations
TL;DR: The motive of this work is to review the researches being carried out in the field of I.C.c. engine on different types of engines with various alternative fuels using these soft computing techniques.
Abstract: It is well known that fossil fuels are depleting day by day, and with the increase in the number of vehicles the pollution has reached at an alarming stage. The need of the hour is to find an alternate fuel as well as to demote the exhaust emission and enhance the performance parameters of the internal combustion (I.C.) engine. Researches on I.C. engines are being conducted in order to come to a feasible solution. Since performing experiments on an I.C. engine is both time consuming and costly therefore many soft computing techniques are being adopted in this field. The term soft computing refers to find the solution of an inexact problem. Different soft computing techniques being used in this field are Artificial Neural Network, Fuzzy Based Approach, Adaptive Neuro Fuzzy Inference System, Gene Expression Programming, Genetic Algorithm and Particle Swarm Optimization. The motive of this work is to review the researches being carried out in the field of I.C. engine on different types of engines with various alternative fuels using these soft computing techniques.
32 citations