scholarly journals Comparison of Machining Performances Using Multiple Regression Analysis and Group Method Data Handling Technique in Wire EDM of Stavax Material

2014 ◽  
Vol 5 ◽  
pp. 2215-2223 ◽  
Author(s):  
G. Ugrasen ◽  
H.V. Ravindra ◽  
G.V. Naveen Prakash ◽  
R. Keshavamurthy
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Group Method ◽  
Data Handling ◽  
Wire Edm ◽  
Handling Technique ◽  
Group Method Data Handling

Comparison of Machining Responses Using Multiple Regression Analysis and Group Method Data Handling Technique of EN-19 Material in WEDM

2014 ◽  
Vol 592-594 ◽  
pp. 97-101
Author(s):  
G. Ugrasen ◽  
H.V. Ravindra ◽  
G.V. Naveen Prakash ◽  
R. Keshavamurthy
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Machining Process ◽  
Group Method ◽  
Main Stream ◽  
Electrode Wear ◽  
Data Handling ◽  
Handling Technique ◽  
Group Method Data Handling

Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This study outlines the development of model and its application to estimation and comparison of machining responses using Multiple Regression Analysis (MRA) and Group Method Data Handling Technique (GMDH). Experimentation was performed as per Taguchi’s L’16 orthogonal array for EN-19 material. Each experiment has been performed under different cutting conditions of pulse-on, pulse-off, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Four responses namely accuracy, surface roughness, volumetric material removal rate and electrode wear have been considered for each experiment. Estimation and comparison of responses was carried out using MRA and GMDH.

Comparison of Machining Performances in Wire EDM for HCHCr Material Using Group Method Data Handling Technique and Artificial Neural Network

2015 ◽  
Author(s):  
Ugrasen Gonchikar ◽  
Ravindra Holalu Venkatadas ◽  
Naveen Prakash Goravi Vijaya Dev ◽  
Keshavamurthy Ramaiah ◽  
Giridhara Gudekota
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Group Method ◽  
Data Handling ◽  
Training Set ◽  
Work Material ◽  
Handling Technique ◽  
Artificial Neural ◽  
Group Method Data Handling ◽  
Testing Set

Wire Electrical Discharge Machining (WEDM) is a specialized thermo electrical machining process capable of accurately machining parts with varying hardness or complex shapes. Present study outlines the comparison of machining performances in the wire electric discharge machining using group method data handling technique and artificial neural network. HCHCr material was selected as a work material. This work material was machined using different process parameters based on Taguchi’s L27 standard orthogonal array. Parameters such as pulse-on time, pulse-off time, current and bed speed were varied. The response variables measured for the analysis are surface roughness, volumetric material removal rate and dimensional error. Machining performances were compared using sophisticated mathematical models viz., Group Method of Data Handling (GMDH) technique and Artificial Neural Network (ANN). GMDH is ideal for complex, unstructured systems where the investigator is only interested in obtaining a high-order input-output relationship. Also, the method is heuristic in nature and is not based on a solid foundation as regression analysis. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into two sets viz., the training set and testing set. The training set is used to make the GMDH learn the process and the testing set will check the performance of GMDH. Different models were obtained by varying the percentage of data in the training set and the best model were selected from these, viz., 50%, 62.5% & 75%. The best model was selected from the said percentages of data. Number of variables selected at each layer is usually taken as a fixed number or a constantly increasing number. It is usually given as fractional increase in number of independent variables present in the previous level. Three different criterion functions, viz., Root Mean Square (Regularity) criterion, Unbiased criterion and Combined criterion were considered for estimation. The choice of the criterion for node selection is another important parameter for proper modeling. The Artificial Neural Network is used to study and predict the machining responses. Input data are fed into the neural network and corresponding weights and bias are extracted. Then weights and bias are integrated in the program which is used to calculate and predict the machining responses. Estimation of machining performances was obtained by using ANN for various cutting conditions. ANN estimates were obtained for various percentages of total data in the training set viz., 50%, 60% & 70%. The best model was selected from the said percentages of data. Estimation and comparison of machining performances were carried out using GMDH and ANN. Estimates from GMDH and ANN were compared and it was observed that ANN with 70% of data in training set gives better results than GMDH.

Estimation and Comparison of Machining Performances Using Group Method Data Handling Technique and ANN in Wire EDM of Stavax Material

2020 ◽  
Author(s):  
Ugrasen Gonchikar ◽  
Holalu Venkatdas Ravindra ◽  
Rudreshi Addamani ◽  
Prathik Jain Sudhir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cutting Conditions ◽  
Group Method ◽  
Data Handling ◽  
Training Set ◽  
Handling Technique ◽  
Artificial Neural ◽  
Group Method Data Handling ◽  
Testing Set

Abstract Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This study outlines the development of model and its application to estimation of machining performances using Group Method Data Handling Technique (GMDH) and Artificial Neural Network (ANN). Experimentation was performed as per Taguchi’s L’16 orthogonal array for Stavax (modified AISI 420 steel) material. Each experiment has been performed under different cutting conditions of pulse-on, pulse-off, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Four responses namely accuracy, surface roughness, Volumetric Material Removal Rate (VMRR) and Electrode Wear (EW) have been considered for each experiment. Estimation and comparison of responses was carried out using GMDH and ANN. Group method data handling technique is ideal for complex, unstructured systems where the investigator is only interested in obtaining a high-order input-output relationship. Also, the method is heuristic in nature and is not based on a solid foundation as in regression analysis. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into two sets viz., the training set and testing set. The training set is used to make the GMDH learn the process and the testing set will check the performance of GMDH. Different models can be obtained by varying the percentage of data in the training set and the best model can be selected from these, viz., 50%, 62.5% & 75%. The best model is selected from the said percentages of data. Number of variables selected at each layer is usually taken as a fixed number or a constantly increasing number. It is usually given as fractional increase in number of independent variables present in the previous level. Three different criterion functions, viz., Root Mean Square (Regularity) criterion, Unbiased criterion and Combined criterion were considered for estimation. The choice of the criterion for node selection is another important parameter for proper modeling. The Artificial Neural Network is used to study and predict the machining responses. Input data are fed into the neural network and corresponding weights and bias are extracted. Then weights and bias are integrated in the program which is used to calculate and predict the machining responses. Estimation of machining performances was obtained by using ANN for various cutting conditions. ANN estimates were obtained for various percentages of total data in the training set viz., 50%, 60% & 70%. The best model is selected from the said percentages of data. Estimation and comparison of machining performances were carried out using GMDH and ANN. Estimates from GMDH and ANN were compared and it was observed that ANN with 70% of data in training set gives better results than GMDH.

Pengaruh Citra Merek, Kualitas Produk Dan Harga Terhadap Kepuasan Konsumen Pada Bisnis Franchise Minuman

2017 ◽  
Vol 24 (2) ◽  
pp. 144-153
Author(s):  
Yunita Fitri Wahyuningtyas
Keyword(s):  
Regression Analysis ◽  
Marketing Strategy ◽  
Product Quality ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Sampling Method ◽  
Sampling Technique ◽  
Sampling Process ◽  
And Function ◽  
Simultaneous Influence

This research is conducted upon the emergence of many companies producing the same product of the same kind and function. It leads to the urgency of proper and well planned marketing strategy. This research aims to investigate how far the influence of branding, product quality, and price toward consumer’s satisfaction in beverage franchise business. This research utilizes 5 likert scale questionnaire which is tested by using multiple regression analysis to reveal whether or not there is partial and simultaneous influence of branding, product quality, and price toward consumer’s satisfaction in beverage franchise business. Sampling method is accidental sampling technique, in which sample of particular population is taken based on the accessibility and availability of the sample during the sampling process. Sample used is 100 samples among consumers or customers of Mang Endy Milkshake. The result shows that branding, product quality, and price influence consumer’s satisfaction in beverage franchise business.

Significance of Experience, Farm Size, Quantity of Propagules and Location f or Seaweed Farmers in the Divided Island of Sebatik

2018 ◽  
Vol 9 (9) ◽  
pp. 825-832
Author(s):  
James M. Alin ◽  
◽  
Datu Razali Datu Eranza ◽  
Arsiah Bahron ◽  
◽  
...  
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Production Function ◽  
Weather Conditions ◽  
Education Level ◽  
Farm Size ◽  
Total Production ◽  
Explanatory Factors ◽  
Douglas Production Function

Seaweed-Kappaphycus-Euchema Cottonii and Denticulum species was first cultivated at Sabah side of Sebatik in 2009. By November 2014, sixty one Sabahan seaweed farmers cultivated 122 ha or 3,050 long lines. Thirty Sabahan seaweed farmers in Kampung Pendekar (3.2 m.t dried) and 31 in Burst Point (12.5 m.t dried) produced 16 metric tonnes of dried seaweed contributed 31% to Tawau’s total production (51 m.t). The remaining 69% were from farmers in Cowie Bay that separates Sebatik from municipality of Tawau. Indonesian in Desa Setabu, Sebatik started in 2008. However, the number of Indonesian seaweed farmers, their cultivated areas and production (as well as quality) in Sebatik increased many times higher and faster than the Sabah side of Sebatik. In 2009 more than 1,401 households in Kabupaten Nunukan (including Sebatik) cultivated over 700 ha and have produced 55,098.95 and 116, 73 m.t dried seaweed in 2010 and 2011 respectively. There is a divergence in productions from farming the sea off the same island under similar weather conditions. Which of the eight explanatory factors were affecting production of seaweeds in Sebatik? Using Cobb Douglas production function, Multiple Regression analysis was conducted on 100 samples (50 Sabahan and 50 Indonesian). Results; Variable significant at α = 0.05% are Experience in farming whereas Farm size; Quantity of propagules and Location — Dummy are the variables significant at α 0.01%. Not significant are variables Fuel; Age; Number of family members involved in farming and Education level.

Adiksi Internet pada Remaja

2019 ◽  
Vol 7 (1) ◽  
pp. 72-78
Author(s):  
Ismalia Prambayu ◽  
Mulia Sari Dewi
Keyword(s):  
Regression Analysis ◽  
Social Skills ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Parenting Style ◽  
Internet Addiction ◽  
Psychological Factors ◽  
Social Skill ◽  
Analysis Method ◽  
Loneliness Scale

AbstractInternet addiction has become a worrying phenomenon for Indonesian teenagers. This research was conducted to determine whether the psychological factors will influence internet addiction in adolescents. This research uses quantitative with multiple regression analysis method. The winning sample is 200 adolescents. The instrument collects data using a scale internet addiction scale that compiled by Griffiths (2005) and developed by Lemmens (2009), Parenting Authority Questionnaire (PAQ) developed by Buri (1991), Social Skill Inventory (SSI) developed by Riggio (1986), and A Rasch-Type Loneliness Scale compiled by De Jong Gierveld (2006).  The results showed that there were significant differences in the parenting style, social skills, and loneliness on the tendency of internet addiction in adolescents.AbstrakAdiksi Internet menjadi salah satu fenomena yang mengkhawatirkan untuk remaja Indonesia. Penelitian ini dilakukan untuk mengetahui faktor psikologis apakah yang memberikan pengaruh terhadap kecenderungan adiksi internet pada remaja. Sampel pada penelitian ini berjumlah 200 remaja dengan menggunakan metode analisis kuantitatif. Penelitian ini menggunakan alat ukur sebagai berikut, alat ukur adiksi internet yang dikembangkan oleh Lemmens (2009), Parenting Authority Questionnaire (PAQ) yang dikembangkan oleh Buri (1991), Social Skill Inventory (SSI) yang dikembangkan oleh Riggio (1986), dan A Rasch-Type Loneliness Scale yang disusun oleh De Jong Gierveld (2006). Berdasarkan hasil pengujian ditemukan pengaruh signifikan gaya pengasuhan, keterampilan sosial, dan kesepian terhadap kecenderungan adiksi internet pada remaja.

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