A Knowledge-Based Model for Capturing and Managing the Knowledge of CNC Operators for Integrating CAM-CNC Operation

2011 ◽  
Vol 5 (4) ◽  
pp. 575-586 ◽  
Author(s):  
Wikan Sakarinto ◽  
◽  
Hiroshi Narazaki ◽  
Keiichi Shirase ◽  
Keyword(s):  
Tool Wear ◽  
Machining Process ◽  
Main Process ◽  
Machining Parameters ◽  
Cnc Milling ◽  
Product Data ◽  
Know How ◽  
Knowledge Based ◽  
Milling Operations

This study is aimed at filling the gap between CAM and CNC operations. The problem is important in practice but has been rarely addressed in the resarch community. In practice, the machining parameters designed by a CAM operator are not always applicable to the machining process by a CNC operator due to several reasons such as tool wear, in-availability, inefficiency, etc. This is mainly due to the discrepancy of knowledge between CAM and CNC operators. To deal with this situation, this study proposes a knowledgebased model for capturing the know-how of CNC operators in the assessment of the product data (CAM files) produced by CAM operators. The assessment determines whether the designed machining parameters are appropriate or not before proceeding further to the machining process. This assessment is the main process where the know-how of a CNC operator is actualized. Based on the data extracted from CAM files, this study discusses a method that captures the knowledge of CNC operators in the process of an assessment. In this work, the discussion is focused on common CNC milling operations.

scholarly journals The Optimization of Machining Parameters for Milling Operations by Using the Nelder–Mead Simplex Method

2020 ◽  
Vol 4 (3) ◽  
pp. 66
Author(s):  
Yubin Lee ◽  
Alin Resiga ◽  
Sung Yi ◽  
Chien Wern
Keyword(s):  
Simplex Method ◽  
Surface Characteristics ◽  
Initial Guess ◽  
Numerical Control ◽  
Machining Process ◽  
Main Process ◽  
Machining Parameters ◽  
Cnc Milling ◽  
Milling Operations ◽  
Machining Operations

The purpose of machining operations is to make specific shapes or surface characteristics for a product. Conditions for machining operations were traditionally selected based on geometry and surface finish requirements. However, nowadays, many researchers are optimizing machining parameters since high-quality products can be produced using more expensive and advanced machines and tools. There are a few methods to optimize the machining process, such as minimizing unit production time or cost or maximizing profit. This research focused on maximizing the profit of computer numerical control (CNC) milling operations by optimizing machining parameters. Cutting speeds and feed are considered as the main process variables to maximize the profit of CNC milling operations as they have the greatest effect on machining operation. In this research, the Nelder–Mead simplex method was used to maximize the profit of CNC milling processes by optimizing machining parameters. The Nelder–Mead simplex method was used to calculate best, worst, and second-worst value based on an initial guess. The possible range of machining parameters was limited by several constraints. The Nelder–Mead simplex method yielded a profit of 3.45 ($/min) when applied to a commonly used case study model.

A Decision Support System for Capturing CNC Operator Knowledge

2011 ◽  
Vol 5 (5) ◽  
pp. 655-662 ◽  
Author(s):  
Wikan Sakarinto ◽  
◽  
Hiroshi Narazaki ◽  
Keiichi Shirase
Keyword(s):  
Decision Support ◽  
Cnc Machining ◽  
Numerical Control ◽  
Cnc Milling ◽  
Machining Processes ◽  
Product Data ◽  
Know How ◽  
Milling Operations ◽  
Data Assessment ◽  
Computer Aided

The main job of Computer Numerical Control (CNC) operators is to capture and use knowledge to assess product data. CNC operators assess Computer-Aided Manufacturing (CAM) files before proceeding to CNC machining processes. Decision Support Systems (DSS), for these operators, is provided by Expert Systems (ES) designed to manage and learn intelligently from previous data and information and produce recommended actions and decisions. The purpose of the DSS is (i) to assist inexperienced operators in assessment using stored know-how of experienced operators and to collect additional knowledge in interaction between the DSS and experienced operators during semiautomatic assessment, and (ii) to present collected knowledge to users based on contexts or constraints the user must deal with in product data assessment. After outlining the DSS, the discussion is about its usefulness in dealing information and knowledge discrepancies between CAM and CNC operators - an important problem in practice that has been rather neglected so far - focusing on CNC milling operations.

scholarly journals Findings of performance evaluation of EDM for different materials of electrodes and work pieces- a review

2018 ◽  
Vol 7 (4.5) ◽  
pp. 542
Author(s):  
Harshalkumar R. Mundane ◽  
Dr. A. V. Kale ◽  
Dr. J. P. Giri
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Performance Measure ◽  
Machining Process ◽  
Machining Parameters ◽  
Spark Erosion ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time ◽  
Time Pulse

EDM (Spark erosion) is non-conventional machining process which uses as removing unwanted material by electrical spark erosion. EDM Machining parameters affecting to the performance and the industries goal is to produce high quality of product with less time consuming and cost. To achieve these goals, optimizing the machining parameters such as pulse on time, pulse off time, cutting speed, depth of cut, duty cycle, arc gap, voltage etc. The performance measure of EDM is calculated on the basis of Material Remove Rate(MRR), Tool Wear Rate(TWR), and Surface Roughness(SR).The main objective of present work is to investigate of the influence of input EDM (Electro Discharge Machining) parameters on machining characteristics like surface roughness and the effects of various EDM process parameters such as pulse on time, pulse off time, servo voltage, peak current, dielectric flow rate, on different process response parameters such as material removal rate (MRR), surface roughness (Ra), Kerf (width of Cut), tool wear ratio(TWR)and surface integrity factors. In this paper few selected research paper related to Die-sinker EDM with effect of MRR, TWR, surface roughness (SR) and work piece material have been discussed.   

scholarly journals Experimental Study on Tool Wear and Delamination in Milling CFRPs with TiAlN- and TiN-Coated Tools

Coatings ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 623 ◽  
Author(s):  
Dervis Ozkan ◽  
Peter Panjan ◽  
Mustafa Sabri Gok ◽  
Abdullah Cahit Karaoglanli
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Failure Mechanisms ◽  
Cutting Tools ◽  
Machining Process ◽  
Dynamic Loads ◽  
Fiber Reinforced Polymers ◽  
Machining Parameters ◽  
Coated Tool ◽  
Cfrp Composite

Carbon fiber-reinforced polymers (CFRPs) have very good mechanical properties, such as extremely high tensile strength/weight ratios, tensile modulus/weight ratios, and high strengths. CFRP composites need to be machined with a suitable cutting tool; otherwise, the machining quality may be reduced, and failures often occur. However, as a result of the high hardness and low thermal conductivity of CFRPs, the cutting tools used in the milling process of these materials complete their lifetime in a short cycle, due to especially abrasive wear and related failure mechanisms. As a result of tool wear, some problems, such as delamination, fiber breakage, uncut fiber and thermal damage, emerge in CFRP composite under working conditions. As one of the main failure mechanisms emerging in the milling of CFRPs, delamination is primarily affected by the cutting tool material and geometry, machining parameters, and the dynamic loads arising during the machining process. Dynamic loads can lead to the breakage and/or wear of cutting tools in the milling of difficult-to-machine CFRPs. The present research was carried out to understand the influence of different machining parameters on tool abrasion, and the work piece damage mechanisms during CFRP milling are experimentally investigated. For this purpose, cutting tests were carried out using a (Physical Vapor Deposition) PVD-coated single layer TiAlN and TiN carbide tool, and the abrasion behavior of the coated tool was investigated under dry machining. To understand the wear process, scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectroscopy (EDS) was used. As a result of the experiments, it was determined that the hard and abrasive structure of the carbon fibers caused flank wear on TiAlN- and TiN-coated cutting tools. The best machining parameters in terms of the delamination damage of the CFRP composite were obtained at high cutting speeds and low feed rates. It was found that the higher wear values were observed at the TiAlN-coated tool, at the feed rate of 0.05 mm/tooth.

Designing and implementation of a novel online adaptive control with optimization technique in hard turning

2020 ◽  
pp. 095965182095219
Author(s):  
Vahid Pourmostaghimi ◽  
Mohammad Zadshakoyan
Keyword(s):  
Adaptive Control ◽  
Tool Wear ◽  
Hard Turning ◽  
Cutting Tool ◽  
Optimization Technique ◽  
Cutting Parameters ◽  
Numerical Control ◽  
Machining Process ◽  
Machining Parameters ◽  
Optimization Methodology

Determination of optimum cutting parameters is one of the most essential tasks in process planning of metal parts. However, to achieve the optimal machining performance, the cutting parameters have to be regulated in real time. Therefore, utilizing an intelligent-based control system, which can adjust the machining parameters in accordance with optimal criteria, is inevitable. This article presents an intelligent adaptive control with optimization methodology to optimize material removal rate and machining cost subjected to surface quality constraint in finish turning of hardened AISI D2 considering the real condition of the cutting tool. Wavelet packet transform of cutting tool vibration signals is applied to estimate tool wear. Artificial intelligence techniques (artificial neural networks, genetic programming and particle swarm optimization) are used for modeling of surface roughness and tool wear and optimization of machining process during hard turning. Confirmatory experiments indicated that the efficiency of the proposed adaptive control with optimization methodology is 25.6% higher compared to the traditional computer numerical control turning systems.

scholarly journals OPTIMASI PARAMETER PERMESINAN TERHADAP WAKTU PROSES PADA PEMROGRAMAN CNC MILLING DENGAN BERBASIS CAD/CAM

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
I G.N.K. Yudhyadi ◽  
Tri Rachmanto ◽  
Adnan Dedy Ramadan
Keyword(s):  
Cutting Speed ◽  
Operation Time ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Milling Machine ◽  
Cnc Milling ◽  
Cnc Milling Machine ◽  
Machining Parameter

Milling process is one of many machining processes for manufacturing component. The length of time in the process of milling machining is influenced by selection and design of machining parameters including cutting speed, feedrate and depth of cut. The purpose of this study to know the influence of cutting speed, feedrate and depth of cut as independent variables versus operation time at CNC milling process as dependent variables. Each independent variable consists of three level of factors; low, medium and high.Time machining process is measured from operation time simulation program, feed cut length and rapid traverse length. The results of statistically from software simulation MasterCam X Milling, then do comparison to CNC Milling machine.  The data from experiments was statistical analyzed by Anova and Regression methods by software minitab 16.Results show that the greater feedrate and depth of cut shorten the operation time of machinery, whereas cutting speed is not significant influence. Depth of cut has the most highly contribution with the value of 49.56%, followed by feedrate 43% and cutting speed 0.92%. Optimal time of machining process total is 71.92 minutes, with machining parameter on the condition cutting speed is 75360 mm/minutes, feedrate is 800 mm/minutes and depth of cut = 1 mm. Results of comparison time machining process in software Mastercam X milling with CNC Milling machine indicates there is difference not significant with the value of 0,35%.

Optimizing the Machining Parameters in Glass Grinding Operation on the CNC Milling Machine for Best Surface Roughness

2010 ◽  
Vol 154-155 ◽  
pp. 721-726 ◽  
Author(s):  
Mohd Sayuti ◽  
Ahmed Aly Diaa Mohammed Sarhan ◽  
Mohd Hamdi Bin Abd Shukor
Keyword(s):  
Surface Roughness ◽  
Machining Process ◽  
Depth Of Cut ◽  
Response Analysis ◽  
Machining Parameters ◽  
Milling Machine ◽  
Cnc Milling ◽  
Cnc Machine ◽  
Cnc Milling Machine ◽  
Tool Diameter

Glass is one of the most difficult materials to be machined due to its brittle nature and unique structure such that the fracture is often occurred during machining and the surface finish produced is often poor. CNC milling machine is possible to be used with several parameters making the machining process on the glass special compared to other machining process. However, the application of grinding process on the CNC milling machine would be an ideal solution in generating special products with good surface roughness. This paper studies how to optimize the different machining parameters in glass grinding operation on CNC machine seeking for best surface roughness. These parameters include the spindle speed, feed rate, depth of cut, lubrication mode, tool type, tool diameter and tool wear. To optimize these machining parameters in which the most significant parameters affecting the surface roughness can be identified, Taguchi optimization method is used with the orthogonal array of L8(26). However, to obtain the most optimum parameters for best surface roughness, the signal to noise (S/N) response analysis and Pareto analysis of variance (ANOVA) methods are implemented. Finally, the confirmation test is carried out to investigate the improvement of the optimization. The results showed an improvement of 8.91 % in the measured surface roughness.

Application of Automated Context-Based DSS for Determining CNC Machining Parameter

2012 ◽  
Vol 6 (6) ◽  
pp. 765-774
Author(s):  
Wikan Sakarinto ◽  
◽  
Setyawan Bekti Wibowo ◽  
Hiroshi Narazaki ◽  
Keiichi Shirase ◽  
...  
Keyword(s):  
Cnc Machining ◽  
Machining Parameters ◽  
Cnc Milling ◽  
Workpiece Material ◽  
Milling Operations ◽  
Fundamental Information ◽  
To Come ◽  
Parameter Values ◽  
Use Of Knowledge ◽  
Machining Parameter

This paper describes the implementation of proposed KBS automatically aware of context or constraints in which the user has to deal with to come up with intelligent DSS. Context is a fundamental information resource that has to do closely with the use of knowledge. The proposedKBS is equipped with the Expert System (ES) providing Decision Support System (DSS), aimed for realizing effective utilization of captured CNC operator knowledge when they assess machining parameters within product data. The newest module in the proposed KBS is equipped with automated contextbased DSS constructed from incoming task restraints and other related machining aspects, such as machining parameter values, cutting tool, workpiece material, etc. In this work, the discussions are focussing on CNC milling operations. According to the implementation result, CNC operators have shown enhanced accuracy on defining machining parameter values with respect to specific constraints.

scholarly journals Rotary ultrasonic drilling of Ti6Al4V: Effects of machining parameters and tool diameter

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775078 ◽  
Author(s):  
Saqib Anwar ◽  
Mustafa M Nasr ◽  
Abdulrahman Al-Ahmari ◽  
Mohammed Alkahtani ◽  
Basem Abdo ◽  
...  
Keyword(s):  
Tool Wear ◽  
Ti6al4v Alloy ◽  
Machining Process ◽  
High Temperature Strength ◽  
Machining Parameters ◽  
Ultrasonic Power ◽  
Ultrasonic Machining ◽  
Rotary Ultrasonic Machining ◽  
Input Parameters ◽  
Tool Diameter

This article presents the use of the rotary ultrasonic machining process for drilling holes in Ti6Al4V alloy which is regarded as a difficult-to-cut material due to its high-temperature strength and low thermal conductivity. This research presents an experimental investigation on the effect of the key rotary ultrasonic machining input parameters including ultrasonic power, spindle speed, feed rate, and the tool diameter on the main output responses including cutting force, hole cylindricity and overcut errors, and tool wear. No previous reports were found in literature to experimentally investigate the effect of the rotary ultrasonic machining parameters and the tool diameter on tool wear, surface integrity, and the accuracy of the drilled holes in Ti6Al4V alloy. The results showed that the rotary ultrasonic machining input parameters within the current ranges can significantly affect the quality of the drilled holes. Through proper selection of input parameters, holes could be drilled in Ti6Al4V alloy with smoothed surface morphology, low tool wear (0.7 mg) and very low cylindricity (2 µm) and overcut (120 µm) errors. Moreover, it was found that the selected level of any input parameter has the ability to significantly affect the influence of the other input parameters on the output responses.

Optimization of Machining Process Parameters Using Moora Method

2020 ◽  
Vol 402 ◽  
pp. 81-89
Author(s):  
Laxman B. Abhang ◽  
Mohd Iqbal ◽  
M. Hameedullah
Keyword(s):  
Wear Rate ◽  
Tool Wear ◽  
Cutting Force ◽  
Process Parameters ◽  
Machining Process ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Tool Wear Rate ◽  
Moora Method

A multi-response optimization is a popular tool in many economic, managerial, constructional, manufacturing, process design, product design technologies, machinery and system, devices, process parameters etc. This research paper demonstrates the application of a simple multi-objective optimization on the basis of ratio analysis (MOORA) method to solve the multi-criteria (objective) optimization problem in the machining process. In this paper, the chip-tool interface temperature, main cutting force, and tool wear rate were investigated in various machining conditions in turning operations. Various machining parameters, such as the cutting speed, feed rate, and depth of cut and effective tool inserts nose radius, were considered. Composite factorial design (24+8) was used for experimentation. Multiple response values were obtained using actual experimentation. By using these experiments, two different methods were proposed. Machining parameters were optimized by minimizing chip-tool interface temperatures, tool wear rate, and main cutting force during machining of alloy steel. The results obtained using the MOORA method almost agree with the grey relational analysis method which shows the authenticate applicability, potentiality, and flexibility of MOORA method for solving various complex decision-making problems in present-day manufacturing industries.

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