A study on major factors influencing dry cutting temperature of AISI 304 stainless steel

He, Hui-Bo; Li, Huaying; Yang, Jun; Xianyin, Zhang; Yue, Qi-Bin; Jiang, Xue; Lyu, Sung-Ki

doi:10.1007/s12541-017-0165-6

Cited by 25 publications

(11 citation statements)

References 15 publications

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“…Stainless steel, for instance, belongs to the difficult machining material, which is easy to stick tool leading to increasing the cutting temperature and intensifying the abrasion of the tool nose. Dry turning of AISI 304 stainless steel (85 HRB) at v = 75÷265 m/min; f = 0.1÷0.3 mm/rev; a p = 0.8÷1.6 mm with two different groups of inserts: uncoated and TiN coated carbide inserts (rake angle γ o = 15°, relief angle α o = 8°, inclination angle λ s = − 4°, and side cutting-edge angle k r = 75°) [14].…”

Section: The Effects On Cutting Temperaturementioning

confidence: 99%

Micro/Nanofluids in Sustainable Machining

Duc¹

2018

Microfluidics and Nanofluidics

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Micro/nanofluids are the recent alternative solutions for cooling lubrication that can be defined as the fluids containing microparticles or nanoparticles, which own superior lubrication and cooling characteristics. For these reasons, they have gained significant attention in industrial applications, such as automotive, machining, and biomedical industries. In this chapter, the authors mainly present the recent progress and applications of nanofluids in machining processes, as well as some initial researches about microfluids. Nanofluids provide an excellent media in cutting zone for enhancing the thermal conductivity and tribological characteristics. Therefore, they help to enhance the cutting performance by reducing the coefficient of friction, cutting temperature tool wear, and improving the surface quality. Moreover, the application of nanoparticles in vegetable oils, which are inherently nontoxic as well as biodegradable, gives them superior lubrication properties suitable for MQL application, especially for difficult-to-cut materials. The novel green technology definitely brings out many new solutions in machining practice.

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Section: The Effects On Cutting Temperaturementioning

confidence: 99%

Micro/Nanofluids in Sustainable Machining

Duc¹

2018

Microfluidics and Nanofluidics

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“…Flank wear analysis demonstrate that the combination of the lowest cutting speed and highest feed per tooth are the best parameters set for both dry and MQL machining, as expected [7]. Lower cutting speeds tend to generate lower temperatures on the cutting zone, as higher cutting speed intensify frictional heat produced by the bottom of chip and at tool rake [8]. Therefore, the cutting temperature increases according to cutting speed, which may decrease tool wear.…”

Section: Resultsmentioning

confidence: 63%

Evaluation of Machining Forces and Surface Integrity on Aisi 304 Steel Top Milling Process Under Different Cutting Conditions

Hamano¹,

Costa²,

Chuvas³

2019

ITEGAM- Journal of Engineering and Technology for Industrial Applications (Itegam-Jetia)

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Stainless steels are widely applied in corrosive environments requiring high mechanical strength. Thus, this material is applicable to the petroleum, gas and petrochemical industries in general. Because they are widely used, it is important to work these materials in a different manufacturing process, in order to reach the desired shapes and dimensions for parts and components. With regard to the behavior of this class of materials when subjected to machining processes, they are defined as low machinability and due to this characteristics, it becomes essential to determine machining forces evaluated in each process, since they define the power required for machines. In addition, it is possible to optimize cutting parameters in order to increase tool life and /or improve surface quality of the machined parts based on the cutting forces. Therefore, the purpose of this work is to analyze how different cutting parameters (cutting speed and feed per tooth), lubrication condition and tool wear might affect machining forces and surface roughness of the AISI 304 in top milling operations. The results demonstrate that the feed per tooth has a greater impact on the increase of the machining forces. In terms of lubrication, Minimum Quantity Lubrication (MQL) has shown to be the best choice upon the dry machining setup, since lower milling forces have been acquired and tool life has been expanded.

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“…Friction is a significant factor affecting the cutting force, cutting temperature, and specific cutting energy. This paper applies a simple shear friction hypothesis model in finite element Deform-3D [35]: τ = µτ 0 , τ indicates the shear stress, µ denotes the friction coefficient, and τ 0 represents the shear yield stress. Due to the low thermal conductivity shown by difficult-to-machine materials, ductile-fracture fractures and saw-toothed chips are formed under certain conditions of a strain-stress relationship [36][37][38][39][40][41], for which Deform's default Normalized Cockroft and Latham [42] fracture criteria are applied in this paper:…”

Section: Microgroove Tool Preparationmentioning

confidence: 99%

Prediction of Surface Roughness of 304 Stainless Steel and Multi-Objective Optimization of Cutting Parameters Based on GA-GBRT

Zhou

Lin

et al. 2019

Applied Sciences

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Establishing and controlling the prediction model of a machined surface quality is known as the basis for sustainable manufacturing. An ensemble learning algorithm—the gradient boosting regression tree—is incorporated into the surface roughness modeling. In order to address the problem of a high time cost and tendency to fall into a local optimum solution when the grid search and conjugate gradient method is adopted to obtain the super-parameters of the ensemble learning algorithm, a genetic algorithm is employed to search for the optimal super-parameters in the training process, and a genetic-gradient boosting regression tree (GA-GBRT) algorithm is developed. A fitting goodness of fit is taken as the fitness function value of the genetic algorithm and combined with k-fold cross-validation, as such, the initial model parameters of the gradient boosting regression tree are optimized. Compared to the optimized artificial neural network (ANN) and support vector regression (SVR) and combined with the cutting experiment of 304 stainless steel with a micro-groove tool, a genetic algorithm multi-objective optimization model with the highest cutting efficiency and a supreme surface quality was constructed by applying the GA-GBRT model. The response relationship reveals the non-linear interaction that occurs between the cutting parameters and the surface roughness of 304 stainless steel that is machined by the micro-groove tool. As indicated by the results obtained from the multi-objective optimization, the cutting efficiency can be enhanced by increasing the cutting speed and depth within a small range of surface quality variations. The GA-GBRT model is validated to be reliable in making a prediction of the surface roughness and optimizing the cutting parameters with turning and milling data.

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A study on major factors influencing dry cutting temperature of AISI 304 stainless steel

Cited by 25 publications

References 15 publications

Micro/Nanofluids in Sustainable Machining

Micro/Nanofluids in Sustainable Machining

Evaluation of Machining Forces and Surface Integrity on Aisi 304 Steel Top Milling Process Under Different Cutting Conditions

Prediction of Surface Roughness of 304 Stainless Steel and Multi-Objective Optimization of Cutting Parameters Based on GA-GBRT

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