Genetic algorithm-based drilling burr minimization using adaptive neuro-fuzzy inference system and support vector regression

Mondal, Nripen; Mandal, Madhab Chandra; Dey, B.; Das, Santanu

doi:10.1177/0954405419889183

Cited by 3 publications

(3 citation statements)

References 30 publications

(51 reference statements)

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“…Fuzzy inference system (FIS) is a mathematical method which uses fuzzy set theory to develop the relationship between inputs and outputs by linguistic terms. 37,47 In fuzzy logic, values of the output variables lies between 0 and 1. FIS involves fuzzification, fuzzy rule base, an inference engine and defuzzification of the output responses.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

A grey-fuzzy based multi-response optimisation study on the friction and wear characteristics of titanium diboride reinforced aluminium matrix composite

Dey

Bhowmik²,

Biswas

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Aluminium matrix composites are nowadays used globally as a substitute for other cast alloys because of their augmented properties. The present experiment deals with the fabrication and characterisation of tribological behaviour of titanium diboride reinforced aluminium matrix composite on a pin-on-disc tribotester under dry sliding condition. In this work, an attempt has been made to minimise the friction and wear characteristics of Al2024-TiB2 composite, by optimising the tribological parameters viz. applied load, sliding speed and sliding distance using grey-fuzzy approach. The tests are carried out based on Taguchi’s L25 orthogonal array. Grey-fuzzy analysis is used for converting the multi-objective optimisation problem into a single-objective optimisation. Based on the grey-fuzzy analysis, the optimal combination of tribological parameters is L1S4D2 (10N load, 2.0 m/s sliding speed and 1000 m sliding distance). The optimal combination of parameters has also been validated with a confirmatory experiment. Analysis of variance (ANOVA) for grey fuzzy grade shows that sliding distance is the most significant parameter affecting tribological performance. SEM analysis of the worn surfaces revealed that, increase in sliding distance results in wear transition from mild to severe. The proposed grey-fuzzy method is found to be successful in solving multi-response tribological problems.

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Section: Fuzzy Inference Systemmentioning

confidence: 99%

A grey-fuzzy based multi-response optimisation study on the friction and wear characteristics of titanium diboride reinforced aluminium matrix composite

Dey

Bhowmik²,

Biswas

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Ferna´ndez-Pe´rez et al 35 studied the influence of cutting parameters including cutting speed and feed on tool wear and hole quality for composite fibre reinforced plastics drilling operations using carbide countersink drill bits with diamond coating. Mondal et al 36 focused on drilling experiments and developed Adaptive Neuro-Fuzzy Inference System (ANFIS) and SVR models to predict burr height and burr thickness using drill diameter, point angle and spindle speed. They then applied genetic algorithms to optimise both models and determine the optimum drilling process parameters in order to minimise burr height and thickness.…”

Section: Literature Reviewmentioning

confidence: 99%

A probabilistic framework for product health monitoring in multistage manufacturing using Unsupervised Artificial Neural Networks and Gaussian Processes

Papananias

McLeay

Mahfouf

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The emergence of highly instrumented manufacturing systems has enabled the paradigm of smart manufacturing that provides high levels of prognostics functionality. Of particular interest is to precisely determine geometric conformance or non-conformance of workpieces during manufacturing. This paper presents a new dimensional product health monitoring system that learns from in-process sensor data and updates the prediction of the product quality as the product is manufactured. The system uses data from multiple manufacturing stages, unlike from a single stage at a time, to predict the dimensional quality of the finished product that is updated with subsequent measurements such as On-Machine Measurements (OMMs), in on-line incremental learning fashion. It is based on self-supervised neural networks for dimensionality reduction, Gaussian Process Regression (GPR) models for probabilistic prediction about the end product condition and the associated uncertainty, and Bayesian information fusion for updating the conditional probability distribution of the end product quality in the light of new information. The monitoring approach was tested on the prediction of diameter deviations with validation results showing its ability to achieve an average accuracy better than 5 μm in terms of the Root Mean Squared Error (RMSE). Having obtained a Probability Density Function (PDF) for the measurand of interest, the conformance and non-conformance probabilities given the tolerance specifications are computed to support the principle of inspection by exception. This ability to construct a conformance probability-based product quality monitoring system using probabilistic machine learning methods constitute a step change to manufacturing prognostics.

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“…Similarly, the prediction and control of interlayer burrs and other machining defects has been extensively studied to find suitable process parameters. 22,23 Significantly, different drilling and riveting systems have different positioning methods and optimal ranges of process parameters. Therefore, relevant research should be carried out according to the characteristics of the constructed system.…”

Section: Introductionmentioning

confidence: 99%

An efficiency and quality based automatic drilling-riveting system for aero-structures assembly

Jin

Tian

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Considering the constantly increasing demand for aircraft products, the development of automatic drilling-riveting machine tool systems is discussed in this paper. A structural conceptual design method considering both efficiency and quality is proposed. Particularly, a drilling-riveting machine tool system including single-column machine tool, light-weight end-effector, and assisting fixture system is built. The positioning error coordinates variation model is analyzed, and the ranges of reasonable processing parameters are determined. An experiment is conducted on the developed system to show satisfactory drilling-riveting quality and efficiency: compared with the position error of other similar systems, the position error with 0.082 mm is reduced by 41.42%, and the accuracy of aperture reaches H8, the rivets head height error ≤0.8 mm, and drilling and riveting efficiency can reach 6/min and 8/min.

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Genetic algorithm-based drilling burr minimization using adaptive neuro-fuzzy inference system and support vector regression

Cited by 3 publications

References 30 publications

A grey-fuzzy based multi-response optimisation study on the friction and wear characteristics of titanium diboride reinforced aluminium matrix composite

A grey-fuzzy based multi-response optimisation study on the friction and wear characteristics of titanium diboride reinforced aluminium matrix composite

A probabilistic framework for product health monitoring in multistage manufacturing using Unsupervised Artificial Neural Networks and Gaussian Processes

An efficiency and quality based automatic drilling-riveting system for aero-structures assembly

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