scholarly journals An Intelligent Tool Selection System Based on CRB and RBR

2014 ◽  
Vol 8 (1) ◽  
pp. 795-802 ◽  
Author(s):  
Rui Wang ◽  
Wu Zhao ◽  
Cunjing Luo ◽  
Zhiyong Wang ◽  
Tao Luo
Keyword(s):  
Information Management ◽  
Cutting Parameters ◽  
Database System ◽  
Database Management System ◽  
Manufacturing Industries ◽  
Machining Process ◽  
Basic Information ◽  
Selection System ◽  
Tool Selection ◽  
Manufacturing Enterprises

The manufacturing industries dealing with machining and tools' manufacturing generate a large amount of data concerning the machining process. However, in most cases, this information is not used efficiently as there is no system dedicated for storing and manipulating such data. In order to use these data to improve the efficiency and increase the benefit of machining, the cutting database system is built for these manufacturing enterprises, which can provide rational cutting parameters and tool solutions. JAVA and MYSQL are used as the base of database management system, combining XML and JavaScript technology. The intelligent tool selection model was designed, and the functions of basic information management and query were realized. It was concluded that it can commend reasonable cutting parameters and tool solutions according to machining condition input.

CBR-Based Cutting Parameter Selection System and Searching Algorithm for Metal Machining Operations

2013 ◽  
Vol 710 ◽  
pp. 554-557 ◽  
Author(s):  
Yan Cao ◽  
Sen Cao ◽  
Yu Bai ◽  
Xiao Fei Yang
Keyword(s):  
Management System ◽  
Nearest Neighbor ◽  
Initial Conditions ◽  
Cutting Parameters ◽  
Parameter Selection ◽  
Database Management System ◽  
Case Based Reasoning ◽  
Selection System ◽  
Cutting Parameter ◽  
Searching Method

At present, most factories choose cutting parameters only depending on experience or handbooks in production. To improve the intelligent level of cutting parameter selection, Case-Based Reasoning (CBR) is used to search rational cutting parameters. The structure of a CBR-based cutting parameter selection system is presented and its functional modules are discussed. CBR-based cutting parameter selection system is mainly composed of case base management system, cutting parameter database management system, application, interface, etc. A complete case is composed of four parts, i.e., objectives, initial conditions, constraints and results. The system adopts hierarchical searching method in combination with nearest neighbor method. It provides a scientific approach for selecting cutting parameters in actual production.

Studying The Effect of a New Mixed Cutting Fluid on Surface Roughness

2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Machining Processes ◽  
Dry Cutting ◽  
Cutting Speeds

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

scholarly journals Temperature monitoring in the subsurface during single lip deep hole drilling

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.

scholarly journals Energy efficient cutting parameter optimization

2021 ◽  
Author(s):  
Xingzheng Chen ◽  
Congbo Li ◽  
Ying Tang ◽  
Li Li ◽  
Hongcheng Li
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Parameter Optimization ◽  
Energy Efficient ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Current Status ◽  
Cutting Parameter ◽  
Cutting Parameter Optimization

AbstractMechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

scholarly journals Effect of Cutting Parameters and Tool Geometry on the Performance Analysis of One-Shot Drilling Process of AA2024-T3

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 854
Author(s):  
Muhammad Aamir ◽  
Khaled Giasin ◽  
Majid Tolouei-Rad ◽  
Israr Ud Din ◽  
Muhammad Imran Hanif ◽  
...  
Keyword(s):  
Feed Rate ◽  
Surface Defects ◽  
Thrust Force ◽  
Cutting Tools ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Machining Process ◽  
Tool Geometry ◽  
Drilling Process

Drilling is an important machining process in various manufacturing industries. High-quality holes are possible with the proper selection of tools and cutting parameters. This study investigates the effect of spindle speed, feed rate, and drill diameter on the generated thrust force, the formation of chips, post-machining tool condition, and hole quality. The hole surface defects and the top and bottom edge conditions were also investigated using scan electron microscopy. The drilling tests were carried out on AA2024-T3 alloy under a dry drilling environment using 6 and 10 mm uncoated carbide tools. Analysis of Variance was employed to further evaluate the influence of the input parameters on the analysed outputs. The results show that the thrust force was highly influenced by feed rate and drill size. The high spindle speed resulted in higher surface roughness, while the increase in the feed rate produced more burrs around the edges of the holes. Additionally, the burrs formed at the exit side of holes were larger than those formed at the entry side. The high drill size resulted in greater chip thickness and an increased built-up edge on the cutting tools.

Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations

2011 ◽  
Vol 383-390 ◽  
pp. 1062-1070
Author(s):  
Adeel H. Suhail ◽  
N. Ismail ◽  
S.V. Wong ◽  
N.A. Abdul Jalil
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Machining Process ◽  
Data Selection ◽  
Adaptive Network ◽  
Inference System ◽  
Single Input ◽  
Machinability Data

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

Effect of Cutting Parameters on Surface Roughness in Turning of Bone

2013 ◽  
Vol 845 ◽  
pp. 708-712 ◽  
Author(s):  
P.Y.M. Wibowo Ndaruhadi ◽  
S. Sharif ◽  
M.Y. Noordin ◽  
Denni Kurniawan
Keyword(s):  
Surface Roughness ◽  
Bone Tissue ◽  
Influencing Factor ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Machining Process ◽  
Turning Operation ◽  
Cutting Direction

Surface roughness indicates the damage of the bone tissue due to bone machining process. Aiming at inducing the least damage, this study evaluates the effect of some cutting conditions to the surface roughness of machined bone. In the turning operation performed, the variables are cutting speed (26 and 45 m/min), feed (0.05 and 0.09 mm/rev), tool type (coated and uncoated), and cutting direction (longitudinal and transversal). It was found that feed did not significantly influence surface roughness. Among the influencing factor, the rank is tool type, cutting speed, and cutting direction.

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