Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

Asl, A.S.; Erdem, A.; Rafighi, Mohammad

doi:10.24200/sci.2017.4247

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…These cutting inserts produce good surface quality and smaller flank wear during the turning of hardened steel [10]. Many studies have been done related to the effect of cutting parameters and workpiece materials on the power consumption [11,12], tool wear [13,14], cutting forces [15,16], and surface roughness [17,18] during hard turning.…”

Section: Introductionmentioning

confidence: 99%

“…Machinability can be defined as the ease of the metal removal process by using proper cutting tools and machining parameters. Many criteria affect machinability, however, power consumption [11], tool wear [13], cutting forces [16], surface quality [18], tool life [23], are the most important ones [24]. Surface finish is used to evaluate the machined surface quality and productivity of machine tools.…”

Section: Introductionmentioning

confidence: 99%

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Machinability of Hardened AISI S1 Cold Work Tool Steel using Cubic Boron Nitride

Şahinoğlu

Rafighi

2021

Scientia Iranica

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Recently, hard turning became an interesting method to the manufacturers as an alternative to the grinding process due to its superior features such as good surface quality, good productivity, lower production costs, lower power consumption, and shorter processing time. Despite its considerable benefits, hard turning is a difficult process that needs advanced cutting inserts such as ceramics and cubic boron nitride. However, these cutting inserts are costly and should be used properly by choosing appropriate machining parameters.In the presented work, the hard turning process was performed to investigate the machinability of AISI S1 cold work tool steel using a cubic boron nitride insert. The relation between machining parameters namely, depth of cut, cutting speed, and feed rate on the responses such as power consumption, surface roughness, and machining sound was found using a full factorial orthogonal array of response surface methodology.In addition, analysis of variance was used to identify the most important machining parameters that influence output parameters. Based on the results, surface roughness was dominantly affected by feed rate, whereas, sound and power consumption were influenced by all machining parameters especially cutting speed and feed rate. A good agreement between the experimental and the predicted values were observed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machinability of Hardened AISI S1 Cold Work Tool Steel using Cubic Boron Nitride

Şahinoğlu

Rafighi

2021

Scientia Iranica

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“…This material is widely used in the machine manufacturing industry, especially in the crankshaft, axle shaft, gear wheel, bolt, and nut construction. Increasing the strength of the material by heat treatment provides usability advantages for the material [1]. In addition, the workability and mechanic properties of these materials are improved by the cryogenic process.…”

Section: Introductionmentioning

confidence: 99%

An investigation of cutting parameters effect on sound level, surface roughness, and power consumption during machining of hardened AISI 4140

Şahinoğlu

Ulaş

2020

Mechanics & Industry

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In recent years, the necessity for energy in the manufacturing industry has become an important problem because fossil fuel reserves are decreasing in order to produce energy. Therefore, the efficient use of energy has become an important research topic. In this study, energy efficiency is investigated in detail for sustainable life and manufacturing. AISI 4140 material with high hardness of 50 HRC hardness has been applied cryogenic process to improve mechanical and machinability properties. In this experiment study, the effects of feed rate (0.04, 0.08, 0.12 mm/rev), cutting speed (140, 160, 180 m/min), depth of cut (0.05, 0.10, 0.15 mm) and tool radius (0.4, 0.8) on energy consumption, surface roughness and sound intensity were investigated. Then, a new mathematical model with high accuracy was developed. Total power consumption was calculated by considering the instantaneous current value and machining time. As a result, it is found that good surface quality obtained when the feed rate is low, and the tool radius is high and the machining time is shortened, the energy consumption is reduced due to the increase in cutting speed, depth of cut and feed rate. Also, it is found that the tool radius has a limited effect on energy consumption, but low feed value increases energy consumption.

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“…The architecture of a neural network is often defined by trial and error and can have a major influence on the quality of the results. From the 44 papers about ANN, 17 propose an architecture with one hidden layer [33,41,46,[52][53][54][55][56][57][58][59][60][61][62][63][64][65], two propose an architecture with two hidden layers [66,67] and the rest does not give any information about the network architecture. From these papers, the most common architecture of the neural network to monitor the tool wear is presented in Figure 8.…”

Section: General Architecture Of the Neural Networkmentioning

confidence: 99%

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

et al. 2021

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In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.

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Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

Cited by 6 publications

References 28 publications

Machinability of Hardened AISI S1 Cold Work Tool Steel using Cubic Boron Nitride

Machinability of Hardened AISI S1 Cold Work Tool Steel using Cubic Boron Nitride

An investigation of cutting parameters effect on sound level, surface roughness, and power consumption during machining of hardened AISI 4140

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

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