Approach to prediction of laser cutting quality by employing fuzzy expert system

Syn, Chong Zhian; Mokhtar, M.; Feng, Chin Jeng; Manurung, Yupiter Harangan Prasada

doi:10.1016/j.eswa.2010.12.111

Cited by 57 publications

(36 citation statements)

References 8 publications

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“…Modeling studies in laser cutting are the scientific ways to study the system behaviors and help us to get a better understanding of this complex process (Syn et al, 2011). A mathematical model of a system is the relationship between input and output parameters in terms of mathematical equations (Dubey & Yadava 2008).…”

Section: Kert Taper Angle Mathematical Modelmentioning

confidence: 99%

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Madić¹,

Radovanović²,

Gostimirović³

2015

10.5267/j.ijiec

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In this paper, an attempt has been made to develop a mathematical model in order to study the relationship between laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position, and kerf taper angle obtained in CO 2 laser cutting of AISI 304 stainless steel. To this aim, a single hidden layer artificial neural network (ANN) trained with gradient descent with momentum algorithm was used. To obtain an experimental database for the ANN training, laser cutting experiment was planned as per Taguchi's L 27 orthogonal array with three levels for each of the cutting parameters. Statistically assessed as adequate, ANN model was then used to investigate the effect of the laser cutting parameters on the kerf taper angle by generating 2D and 3D plots. It was observed that the kerf taper angle was highly sensitive to the selected laser cutting parameters, as well as their interactions. In addition to modeling, by applying the Monte Carlo method on the developed kerf taper angle ANN model, the near optimal laser cutting parameter settings, which minimize kerf taper angle, were determined.

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Section: Kert Taper Angle Mathematical Modelmentioning

confidence: 99%

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Madić¹,

Radovanović²,

Gostimirović³

2015

10.5267/j.ijiec

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“…Proper selection of type of membership functions decides the complexity and performance of fuzzy model. Triangular and trapezoidal membership functions are commonly used [2].…”

Section: Determination Of S/n Ratiomentioning

confidence: 99%

“…Few researchers concentrated on modeling and optimization of laser beam cutting through Artificial intelligence (AI) based techniques such as artificial neural network (ANN) and fuzzy logic (FL) [1]. Syn et al [2] developed a mamdani type fuzzy expert system to prediction of laser cutting quality. The three input parameters were used such as power, cutting speed, gas pressure to predict the output surface roughness and dross inclusion.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Investigation of Multiple Quality Characteristics of Laser Beam Machined Surface using Integrated Taguchi and Fuzzy Logic Method

Kumar¹,

Kumar

Sharma

2016

Journal for Manufacturing Science and Production

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In laser cutting, the capability of laser cutting mainly depends on optical and thermal properties of work material. The surface quality and metallurgical properties of the product is most important from the point of laser cutting quality. The present research work explores the modeling and optimization of laser beam cutting process parameters by using hybrid approach of Taguchi based fuzzy logic. The multi-response optimization of process parameters has been done to improve geometrical accuracy by minimizing the kerf width and kerf deviation. The four input parameters power, gas pressure, feed rate, pulse frequency and three output parameters kerf width (KW), kerf deviation (KD) and material removal rate (MRR) have been taken for the experimentation work. The S/N ratios taken for the KW and KD is of the smaller-the-better type and MRR is of the higher the better type. The predicting fuzzy logic model is implemented on Fuzzy Logic Toolbox of MATLAB using Mamdani technique. The fuzzy logic theory has been applied to compute the fuzzy multiresponse performance index (FMRPI). This performance index is further used for multi-objective optimization. The selected samples were analyzed using scanning electron microscope. The predicted optimum results have been validated by performing the confirmation tests. The confirmation tests showed the considerable reduction in kerf deviation and increase in material removal rate.

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“…The information of defined process is created as a rule-based [44]. Syn et al (2011) proposed model based on fuzzy theory that predict the surface quality of the products produced by the machine [45]. Zeaiter et al (2011) proposed real-time cavity pressure that estimate weight and dimensions of the product using force sensor data by using regression analysis model [46].…”

Section: Quality Improvement Based On Data Miningmentioning

confidence: 99%

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

Syafrudin

Fitriyani

et al. 2017

Sustainability

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Currently, the manufacturing industry is experiencing a data-driven revolution. There are multiple processes in the manufacturing industry and will eventually generate a large amount of data. Collecting, analyzing and storing a large amount of data are one of key elements of the smart manufacturing industry. To ensure that all processes within the manufacturing industry are functioning smoothly, the big data processing is needed. Thus, in this study an open source-based real-time data processing (OSRDP) architecture framework was proposed. OSRDP architecture framework consists of several open sources technologies, including Apache Kafka, Apache Storm and NoSQL MongoDB that are effective and cost efficient for real-time data processing. Several experiments and impact analysis for manufacturing sustainability are provided. The results showed that the proposed system is capable of processing a massive sensor data efficiently when the number of sensors data and devices increases. In addition, the data mining based on Random Forest is presented to predict the quality of products given the sensor data as the input. The Random Forest successfully classifies the defect and non-defect products, and generates high accuracy compared to other data mining algorithms. This study is expected to support the management in their decision-making for product quality inspection and support manufacturing sustainability.

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Approach to prediction of laser cutting quality by employing fuzzy expert system

Cited by 57 publications

References 8 publications

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Experimental Investigation of Multiple Quality Characteristics of Laser Beam Machined Surface using Integrated Taguchi and Fuzzy Logic Method

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

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