Tool life prediction model of uncoated carbide tool in high speed drilling of Al-Si alloy using response surface methodology

2012 ◽  
Vol 6 (1/2) ◽  
pp. 112 ◽  
Author(s):  
S. Sharif ◽  
A. Akhavan Farid ◽  
M.H. Idris
Keyword(s):  
Response Surface Methodology ◽  
Prediction Model ◽  
Response Surface ◽  
Tool Life ◽  
Life Prediction ◽  
High Speed ◽  
Carbide Tool ◽  
High Speed Drilling ◽  
Life Prediction Model

Research on Commercial Vehicle Clutch Release Bearing Fatigue Life Prediction Model

2013 ◽  
Vol 423-426 ◽  
pp. 1853-1857
Author(s):  
Guo Liang Chen ◽  
Xiao Yang Chen
Keyword(s):  
Fatigue Crack ◽  
Fatigue Life ◽  
Prediction Model ◽  
Contact Stress ◽  
Life Prediction ◽  
High Speed ◽  
Commercial Vehicle ◽  
Smelting Process ◽  
Fatigue Life Analysis ◽  
Life Prediction Model

Commercial vehicle clutch release bearings working at high speed, strong vibration,high temperature, damp and easy pollution conditions. Fatigue life analysis is based on the release bearing rings or rolling body began to appear fatigue spalling, in which this kind of phenomenon is under cyclic stress. The contact stress distribution is not uniform, the contact stress is mainly concentrated near the surface; influenced by the geometry and physical properties and lubrication of the surface significantly. Contact between the two types of fatigue crack extension methods: fatigue crack surface under expansion and surface fatigue crack propagation. The surface crack growth mainly originated from two kinds of cases: crack caused by surface pre crack and contact between the two surface asperity each other. New life prediction model for the release bearing based on L-P theory and Tallian model ,in which influence factors of fatigue life is introduced on the smelting process, surface defect, surface roughness, residual stress, elastohydrodynamic lubrication oil film,environmental cleanliness, temperature, the effect of varying load characteristics and other factors of fatigue life. The results show that: the clutch release bearing life prediction model of new and more close to the real conditions of automobile clutch, provide the theory basis for the development of a new generation high-speed heavy-duty clutch release bearing of the commercial vehicle.

Multi-Objective Optimization Using Box-Behken of Response Surface Methodology for High-Speed Machining of Inconel 718

2014 ◽  
Vol 629 ◽  
pp. 487-492 ◽  
Author(s):  
Mohd Shahir Kasim ◽  
Che Hassan Che Haron ◽  
Jaharah Abd Ghani ◽  
E. Mohamad ◽  
Raja Izamshah ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Tool Life ◽  
Feed Rate ◽  
Inconel 718 ◽  
High Speed ◽  
Cutting Speed ◽  
High Speed Milling

This study was carried out to investigate how the high-speed milling of Inconel 718 using ball nose end mill could enhance the productivity and quality of the finish parts. The experimental work was carried out through Response Surface Methodology via Box-Behnken design. The effect of prominent milling parameters, namely cutting speed, feed rate, depth of cut (DOC), and width of cut (WOC) were studied to evaluate their effects on tool life, surface roughness and cutting force. In this study, the cutting speed, feed rate, DOC, and WOC were in the range of 100 - 140 m/min, 0.1 - 0.2 mm/tooth, 0.5 - 1.0 mm and 0.2 - 1.8 mm, respectively. In order to reduce the effect of heat generated during the high speed milling operation, minimum quantity lubrication of 50 ml/hr was used. The effect of input factors on the responds was identified by mean of ANOVA. The response of tool life, surface roughness and cutting force together with calculated material removal rate were then simultaneously optimized and further described by perturbation graph. Interaction between WOC with other factors was found to be the most dominating factor of all responds. The optimum cutting parameter which obtained the longest tool life of 60 mins, minimum surface roughness of 0.262 μm and resultant force of 221 N was at cutting speed of 100 m/min, feed rate of 0.15 mm/tooth, DOC 0.5 m and WOC 0.66 mm.

Development of a tool life prediction model for plaster machining

2007 ◽  
Vol 2 (2) ◽  
pp. 186
Author(s):  
Timothy J. Coole ◽  
Jose Filipe C.P. Antunes Simoes ◽  
Antonio R. Pires ◽  
David G. Cheshire
Keyword(s):  
Prediction Model ◽  
Tool Life ◽  
Life Prediction ◽  
Life Prediction Model

Response Surface Methodology Modeling of Drill Exiting Damage Factor in High Speed Drilling of GFRP Using HSS Twist Drill Bit

2015 ◽  
Vol 786 ◽  
pp. 323-327
Author(s):  
Tze Keong Woo ◽  
Faiz Ahmad ◽  
Safian Sharif
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
High Speed ◽  
High Speed Steel ◽  
Drill Bit ◽  
Twist Drill ◽  
Damage Factor ◽  
Glass Fiber Reinforced ◽  
Drilling Parameters ◽  
High Speed Drilling

This paper presents a research on experimental and response surface methodology (RSM) approach in evaluating the damage factor of the drilled holes in high speed drilling of glass fiber reinforced polymer (GFRP). From the experiment, the influences of drilling parameters toward damage factor are more prominent in thicker GFRP; where high speed drilling using high speed steel twist drill bit produces lower damage factor in thicker GFRP. Lastly an optimized set of drilling parameters was generated for the use of high speed steel twist drill bit in high speed drilling.

An adaptive procedure for tool life prediction in face milling

2011 ◽  
Vol 225 (11) ◽  
pp. 1130-1136 ◽  
Author(s):  
D D Zhang
Keyword(s):  
Tool Wear ◽  
Prediction Model ◽  
Tool Life ◽  
Life Prediction ◽  
Face Milling ◽  
Accurate Prediction ◽  
Machining Process ◽  
Model Parameters ◽  
Adaptive Procedure ◽  
Life Prediction Model

Accurate prediction of tool life is essential to guarantee surface quality and economics of cutting operations in face milling. This article presents a procedure for tool life prediction through in-process adaptation of tool wear rate based on indirect measures. The procedure effectively accounts for the uncertainty of tool wear progress owing to the complexity of the machining process. First, sensor fusion of spindle motor current AC and DC portions is taken to estimate the actual tool wear through relevance vector machine. Then, a tool life prediction model relating flank wear with cutting time is proposed for tracking the progress of tool wear under certain cutting settings. Further, a recursive least square algorithm is developed to update the parameters of the tool life prediction model by considering the error between the predicted tool wear and the estimated tool wear. Finally, the updated model capturing the uncertainty of tool wear progress is used to predict tool life in face milling. Tool life experiments validate that the adaptive procedure can quickly track the progress of tool wear, and make more accurate prediction of tool life compared with the procedure with constant model parameters.

Tool Life Prediction Model Based on GA-BP Neural Network

2016 ◽  
Vol 836-837 ◽  
pp. 256-262 ◽  
Author(s):  
Zheng Zhang ◽  
Liang Li ◽  
Wei Zhao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Tool Life ◽  
Bp Neural Network ◽  
Life Prediction ◽  
Affecting Factors ◽  
Tool Selection ◽  
Model Based ◽  
Bp Model ◽  
Life Prediction Model

In order to improve the working efficiency of a manufacturing system, tool life estimation is very essential. In this paper, the dominant factors affecting tool life are analyzed by theoretical analysis. According to the nonlinear relationship between affecting factors and tool life, a tool life prediction model based on BP neural network, which is optimized by genetic algorithm (GA), is built up. 15 network patterns are trained to get the best network structure. The accuracy of GA-BP model is verified through computing and compared with the standard BP model. The results show that GA-BP model prediction value is exactly closed to the expected value of tool life and the prediction accuracy can be improved more than 5% compared than the standard BP model. The model is proved to be accuracy and it can be used as an effective method of tool selection decision.

scholarly journals Life Prediction Model of Mineral Admixture Cement Based-Materials under Early Age CO2-Erosion

Coatings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 413
Author(s):  
Saisai Wang ◽  
Jian Chen ◽  
Xiaodong Wen
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Experimental Tests ◽  
Mineral Admixture ◽  
Model Parameters ◽  
Influence Coefficient ◽  
Test Pieces ◽  
Cement Based Materials ◽  
Particle Group ◽  
Life Prediction Model

Most of the existing models of structural life prediction in early carbonized environment are based on accelerated erosion after standard 28 days of cement-based materials, while cement-based materials in actual engineering are often exposed to air too early. These result in large predictions of the life expectancy of mineral-admixture cement-based materials under early CO2-erosion and affecting the safe use of structures. To this end, different types of mineral doped cement-based material test pieces are formed, and early CO2-erosion experimental tests are carried out. On the basis of the analysis of the existing model, the influence coefficient of CO2-erosion of the mineral admixture Km is introduced, the relevant function is given, and the life prediction model of the mineral admixture cement-based material under the early CO2-erosion is established and the model parameters are determined by using the particle group algorithm (PSO). It has good engineering applicability and guiding significance.

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