Modeling and Optimization of Machining Parameters Using Regression and Cuckoo Search in Deep Hole Drilling Process

This study presents the modeling and optimization of the machining parameters in deep hole drilling process using statistical and soft computing technique. Regression analysis is used for modeling and Cuckoo Search, CS algorithm is used for the optimization process. Design of Experiment (DoE), have been carried using a Full Factorial design with added centre point that comprises of machining parameters (feed rate (f), spindle speed (s), depth of hole (d) and minimum quantity lubrication, MQL (m)) and machining performance which is surface roughness, Ra. Next, the mathematical models (Multiple Linear Regression, MLR and 2-factor interaction, 2FI) are developed for the experimental results of Ra and Analysis of variance, ANOVA are used to check the significance of the models developed. The results showed that both of mathematical models (MLR and 2FI) have outperformed the minimum Ra value compared to the experimental result.

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Optimization of Roundness Error in Deep Hole Drilling using Cuckoo Search Algorithm

ELEKTRIKA- Journal of Electrical Engineering ◽

10.11113/elektrika.v18n3.174 ◽

2019 ◽

Vol 18 (3) ◽

pp. 44-48

Author(s):

Azizah Mohamad ◽

Azlan Mohd Zain ◽

Razana Alwee ◽

Noordin Mohd Yusof ◽

Farhad Najarian

Keyword(s):

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Machining Process ◽

Hole Drilling ◽

Machining Parameters ◽

Deep Hole ◽

Roundness Error ◽

Deep Hole Drilling ◽

Minimum Value

In the manufacturing industry, machining is a part of all manufacture in almost all metal products. Machining of holes is one of the most common processes in the manufacturing industries. Deep hole drilling, DHD is classified as a complex machining process .This study presents an optimization of machining parameters in DHD using Cuckoo Search algorithm, CS comprising feed rate (f), spindle speed (s), depth of hole (d) and Minimum Quantity Lubrication MQL, (m). The machining performance measured is roundness error, Re. The real experimentation was designed based on Design of Experiment, DoE which is two levels full factorial with an added centre point. The experimental results were used to develop the mathematical model using regression analysis that used in the optimization process. Analysis of variance (ANOVA) and Fisher‘s statistical test (F-test) are used to check the significant of the model developed. According to the results obtained by experimental the minimum value of Re is 0.0222µm and by CS is 0.0198µm. For the conclusion, it was found that CS is capable of giving the minimum value of Re as it outperformed the result from the experimental.

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Temperature monitoring in the subsurface during single lip deep hole drilling

tm - Technisches Messen ◽

10.1515/teme-2020-0055 ◽

2020 ◽

Vol 87 (12) ◽

pp. 757-767

Author(s):

Robert Wegert ◽

Vinzenz Guski ◽

Hans-Christian Möhring ◽

Siegfried Schmauder

Keyword(s):

Cutting Parameters ◽

Drill Hole ◽

Machining Process ◽

Hole Drilling ◽

Drilling Process ◽

Deep Hole ◽

Machining Processes ◽

Deep Hole Drilling ◽

Feed Force ◽

Cutting Zone

AbstractThe surface quality and the subsurface properties such as hardness, residual stresses and grain size of a drill hole are dependent on the cutting parameters of the single lip deep hole drilling process and therefore on the thermomechanical as-is state in the cutting zone and in the contact zone between the guide pads and the drill hole surface. In this contribution, the main objectives are the in-process measurement of the thermal as-is state in the subsurface of a drilling hole by means of thermocouples as well as the feed force and drilling torque evaluation. FE simulation results to verify the investigations and to predict the thermomechanical conditions in the cutting zone are presented as well. The work is part of an interdisciplinary research project in the framework of the priority program “Surface Conditioning in Machining Processes” (SPP 2086) of the German Research Foundation (DFG).This contribution provides an overview of the effects of cutting parameters, cooling lubrication and including wear on the thermal conditions in the subsurface and mechanical loads during this machining process. At first, a test set up for the in-process temperature measurement will be presented with the execution as well as the analysis of the resulting temperature, feed force and drilling torque during drilling a 42CrMo4 steel. Furthermore, the results of process simulations and the validation of this applied FE approach with measured quantities are presented.

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Effect of ultrasonic vibration on the performance of deep hole drilling process

Procedia Manufacturing ◽

10.1016/j.promfg.2021.06.029 ◽

2021 ◽

Vol 53 ◽

pp. 260-267

Author(s):

J. Rajaguru ◽

N. Arunachalam

Keyword(s):

Ultrasonic Vibration ◽

Hole Drilling ◽

Drilling Process ◽

Deep Hole ◽

Deep Hole Drilling

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Optimization of Surface Roughness in Deep Hole Drilling using Moth-Flame Optimization

ELEKTRIKA- Journal of Electrical Engineering ◽

10.11113/elektrika.v18n3-2.195 ◽

2019 ◽

Vol 18 (3-2) ◽

pp. 62-68

Author(s):

Anis Farhan Kamaruzaman ◽

Azlan Mohd Zain ◽

Razana Alwee ◽

Noordin Md Yusof ◽

Farhad Najarian

Keyword(s):

Surface Roughness ◽

Optimization Algorithm ◽

Feed Rate ◽

Spindle Speed ◽

Hole Drilling ◽

P Value ◽

Machining Parameters ◽

Deep Hole ◽

Deep Hole Drilling ◽

Moth Flame Optimization Algorithm

This study emphasizes on optimizing the value of machining parameters that will affect the value of surface roughness for the deep hole drilling process using moth-flame optimization algorithm. All experiments run on the basis of the design of experiment (DoE) which is two level factorial with four center point. Machining parameters involved are spindle speed, feed rate, depth of hole and minimum quantity lubricants (MQL) to obtain the minimum value for surface roughness. Results experiments are needed to go through the next process which is modeling to get objective function which will be inserted into the moth-flame optimization algorithm. The optimization results show that the moth-flame algorithm produced a minimum surface roughness value of 2.41µ compared to the experimental data. The value of machining parameters that lead to minimum value of surface roughness are 900 rpm of spindle speed, 50 mm/min of feed rate, 65 mm of depth of hole and 40 l/hr of MQL. The ANOVA has analysed that spindle speed, feed rate and MQL are significant parameters for surface roughness value with P-value <0.0001, 0.0219 and 0.0008 while depth of hole has P-value of 0.3522 which indicates that the parameter is not significant for surface roughness value. The analysis also shown that the machining parameter that has largest contribution to the surface roughness value is spindle speed with 65.54% while the smallest contribution is from depth of hole with 0.8%. As the conclusion, the application of artificial intelligence is very helpful in the industry for gaining good quality of products.

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