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HDPE 1

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SiC particulates 1

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aluminum 1

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carbide tool (K10) 1

cutting forces (tangential 1

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Measuring residents’ anxiety under urban redevelopment in China: An investigation of demographic variables

Jinbo SONG, Chen QIAN, Zhuo FENG, Liang MA

Frontiers of Engineering Management 2021, Volume 8, Issue 1,   Pages 48-59 doi: 10.1007/s42524-020-0131-3

Abstract: Residents’ concerns and feelings play pivotal roles in smoothly promoting urban redevelopment. Anxiety, as an intuitive feeling toward uncertainties, generally exists among residents who are confronted with redevelopment, and this feeling has gradually attracted scholars’ attention. However, relatively few studies have focused on the multidimensional view of this concept and its influencing factors. Drawing upon a large-scale questionnaire survey conducted in 13 pilot areas in China, this study refines and verifies five prominent dimensions of anxiety, namely, housing conditions, monetary compensation, public services, life adaptation, and public participation level, through factor analysis and one-sample -test. The finding contributes to achieving a complete understanding of anxiety, and the scales developed for measuring these dimensions lay the foundation for further empirical studies on anxiety. The individual and collective effects of age, job, and region variables on anxiety dimensions are demonstrated via independent-sample -test and analysis of variance, which clarifies the formation process of anxiety and highlights the importance of these contextual variables. Tailored strategies for policymaking and engineering management, including establishing reasonable compensation standards, providing equal public services, and delivering high-quality housing, are proposed to relieve residents’ anxiety. These strategies are expected to consider further the sensitive group, such as the elderly, farmers, and casual workers.

Keywords： urban redevelopment     anxiety dimension     influencing factors     ANOVA     policymaking    

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Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 1, doi: 10.1007/s11783-023-1606-3

Abstract:

● Reducting the sampling frequency can enhance the modelling process.

Keywords： HDPE     Pyrolysis     Kinetics     Thermogravimetric     ANOVA     Artificial neural network    

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Prediction of cutting force in turning of UD-GFRP using mathematical model and simulated annealing

Meenu GUPTA, Surinder Kumar GILL

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 4,   Pages 417-426 doi: 10.1007/s11465-012-0343-2

Abstract:

Glass fiber reinforced plastics (GFRPs) composite is considered to be an alternative to heavy exortic materials. According to the need for accurate machining of composites has increased enormously. During machining, the obtaining cutting force is an important aspect. The present investigation deals with the study and development of a cutting force prediction model for the machining of unidirectional glass fiber reinforced plastics (UD-GFRP) composite using regression modeling and optimization by simulated annealing. The process parameters considered include cutting speed, feed rate and depth of cut. The predicted values radial cutting force model is compared with the experimental values. The results of prediction are quite close with the experimental values. The influences of different parameters in machining of UD-GFRP composite have been analyzed.

Keywords： UD-GFRP     ANOVA     radial cutting force     PCD tool     Taguchi method     regression analysis     simulated annealing     multi    

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Optimization of WEDM process of pure titanium with multiple performance characteristics using Taguchi’s DOE approach and utility concept

Rupesh CHALISGAONKAR, Jatinder KUMAR

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 2,   Pages 201-214 doi: 10.1007/s11465-013-0256-8

Abstract: machining parameters for their effect on the CS and SR was determined by using analysis of variance (ANOVA

Keywords： wire electro-discharge machining (WEDM)     Taguchi method     analysis of variance (ANOVA)     utility concept     cutting    

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Application of grey-taguchi method for optimization of dry sliding wear properties of aluminum MMCs

Rajesh SIRIYALA, Gopala Krishna ALLURU, Rama Murthy Raju PENMETSA, Muthukannan DURAISELVAM

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 3,   Pages 279-287 doi: 10.1007/s11465-012-0329-0

Abstract:

Through a pin-on-disc type wear setup, the dry sliding wear behavior of SiC-reinforced aluminum composites produced using the molten metal mixing method was investigated in this paper. Dry sliding wear tests were carried on SiC-reinforced metal matrix composites (MMCs) and its matrix alloy sliding against a steel counter face. Different contact stresses, reinforcement percentages, sliding distances, and sliding velocities were selected as the control variables, and the responses were selected as the wear volume loss (WVL) and coefficient of friction (COF) to evaluate the dry sliding performance. An L25 orthogonal array was employed for the experimental design. Initially, the optimization of the dry sliding performance of the SiC-reinforced MMCs was performed using grey relational analysis (GRA). Based on the GRA, the optimum level parameters for overall grey relational grade in terms of WVL and COF were identified. Analysis of variance was performed to determine the effect of individual factors on the overall grey relational grade. The results indicated that the sliding velocity was the most effective factor among the control parameters on dry sliding wear, followed by the reinforcement percentage, sliding distance, and contact stress. Finally, the wear surface morphology and wear mechanism of the composites were investigated through scanning electron microscopy.

Keywords： aluminum     ANOVA (analysis of variance)     grey relational analysis     metal matrix composites     SiC particulates    

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3,   Pages 674-685 doi: 10.1007/s11709-018-0505-3

Abstract: M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length ( ), angle of oblique load ( ), sand density ( ), number of batter piles ( ), and number of vertical piles ( ) as input and oblique load ( ) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load ( ) and number of batter pile ( ) affect the oblique load capacity for both smooth and rough pile groups.

Keywords： batter piles     oblique load test     neural network     M5 model tree     random forest regression     ANOVA    

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Prediction of cutting forces in machining of unidirectional glass fiber reinforced plastics composite

Surinder Kumar GILL, Meenu GUPTA, P. S. SATSANGI

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 2,   Pages 187-200 doi: 10.1007/s11465-013-0262-x

Abstract:

Machining of plastic materials has become increasingly important in any engineering industry subsequently the prediction of cutting forces. Forces quality has greater influence on components, which are coming in contact with each other. So it becomes necessary to measure and study machined forces and its behavior. In this research work, experimental investigations are conducted to determine the effects of cutting conditions and tool geometry on the cutting forces in the turning of the unidirectional glass fiber reinforced plastics (UD-GFRP) composites. In this experimental study, carbide tool (K10) having different tool nose radius and tool rake angle is used. Experiments are conducted based on the established Taguchi’s technique L18 orthogonal array on a lathe machine. It is found that the depth of cut is the cutting parameter, which has greater influence on cutting forces. The effect of the tool nose radius and tool rake angles on the cutting forces are also considerably significant. Based on statistical analysis, multiple regression model for cutting forces is derived with satisfactory coefficient (R2). This model proved to be highly preferment for predicting cutting forces.

Keywords： reinforced plastics (UD-GFRP) composites     machining     cutting forces (tangential     feed and radial force)     ANOVA    

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Title Author Date Type Operation

Measuring residents’ anxiety under urban redevelopment in China: An investigation of demographic variables

Jinbo SONG, Chen QIAN, Zhuo FENG, Liang MA

Journal Article

Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

Journal Article

Prediction of cutting force in turning of UD-GFRP using mathematical model and simulated annealing

Meenu GUPTA, Surinder Kumar GILL

Journal Article

Optimization of WEDM process of pure titanium with multiple performance characteristics using Taguchi’s DOE approach and utility concept

Rupesh CHALISGAONKAR, Jatinder KUMAR

Journal Article

Application of grey-taguchi method for optimization of dry sliding wear properties of aluminum MMCs

Rajesh SIRIYALA, Gopala Krishna ALLURU, Rama Murthy Raju PENMETSA, Muthukannan DURAISELVAM

Journal Article

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Journal Article

Prediction of cutting forces in machining of unidirectional glass fiber reinforced plastics composite

Surinder Kumar GILL, Meenu GUPTA, P. S. SATSANGI

Journal Article