Optimization of Process Parameters for Fabrication of Wool Fiber-Reinforced Polypropylene Composites with Respect to Mechanical Properties

The present study focused optimizing the process parameters of compression molding with respect to mechanical properties for fabrication of wool fiber-reinforced polypropylene composites. An experiment was designed using the Box-Behnken method with three levels and three variables using temperature, time, and pressure, as independent variables and tensile, flexural, and impact strengths as dependent variables. The process conditions were optimized using response surface methodology with the Box-Behnken experimental design. Regression equations were obtained to analyze tensile strength, flexural strength, and impact strength and the optimum process parameters were identified. The results show that the optimum conditions for compression molding are 176°C, 7 min, and 35 bar.

Download Full-text

Optimization of mechanical properties of silk fiber-reinforced polypropylene composite using Box–Behnken experimental design

Journal of Industrial Textiles ◽

10.1177/1528083716667257 ◽

2016 ◽

Vol 47 (5) ◽

pp. 602-621 ◽

Cited By ~ 4

Author(s):

Rajkumar Govindaraju ◽

Srinivasan Jagannathan

Keyword(s):

Mechanical Properties ◽

Experimental Design ◽

Process Parameters ◽

Polypropylene Fiber ◽

Compression Molding ◽

Silk Fiber ◽

Fiber Reinforced ◽

Polypropylene Composite ◽

Molding Process ◽

Polypropylene Composites

In this study, the compression molding process parameters for the development of silk fiber-reinforced polypropylene composites was optimized using Box–Behnken experimental Design with response surface methodology. The trimmed silk fibers from shuttleless loom silk selvedge waste were used as reinforcement in polypropylene fiber matrix. The process parameters of compression molding such as temperature (165–185℃), time (7–15 min) and pressure (35–45 bar) were optimized with respect to the mechanical properties of the silk fiber-reinforced polypropylene composite. The optimum parameters for better mechanical properties were found to be temperature, 180℃; time, 7 min, and pressure, 35 bar in compression molding. The optimised level of parameters has shown good response to the predicted model.

Download Full-text

Evaluation of mechanical properties and surface roughness of cotton–viscose hybrid composite

Polymers and Polymer Composites ◽

10.1177/0967391120909052 ◽

2020 ◽

pp. 096739112090905

Author(s):

Kuppuraj Arunkumar ◽

Angamuthu Murugarajan

Keyword(s):

Mechanical Properties ◽

Surface Roughness ◽

Composite Material ◽

Process Parameters ◽

Water Pressure ◽

Machining Process ◽

Optimum Process ◽

Process Conditions ◽

Machined Surface ◽

Reinforced Composite

Natural-fibre reinforced composite material is an emerging material that has great potential to be used in various industrial aspects and applications. The cotton-viscose-reinforced composite is prepared using a compression moulding process. In addition to it, analysis of its mechanical properties was also carried out, such as tensile strength, flexural strength, impact strength and hardness. An attempt was made to process the prepared composite material using abrasive water jet machining (AWJM) under different process parameters (water pressure, nozzle transfer speed and abrasive flow rate) levels to determine the better suitable process conditions to achieve the better surface finish and optimize the machining process. The significance of the optimization process was ensured using the results of the analysis of variance. Morphological analyses of the machined surface were performed using a scanning electron microscope. The surface roughness of 8.28 µm was found to be the optimized process parameter. Optimum process parameters in AWJM are used to improve the surface quality.

Download Full-text

Optimization of Process Parameters of Friction Stir Welding by Taguchi Method

Wasit Journal of Engineering Sciences ◽

10.31185/ejuow.vol7.iss1.111 ◽

2019 ◽

Vol 7 (1) ◽

pp. 17-23

Author(s):

Azzam Sabah Albunduqee ◽

Hussein R Al-Bugharbee

Keyword(s):

Mechanical Properties ◽

Tensile Strength ◽

Friction Stir Welding ◽

Taguchi Method ◽

Process Parameters ◽

Rotational Velocity ◽

Welding Parameters ◽

Optimum Process ◽

Friction Stir ◽

Optimization Of Process Parameters

Friction Stir Welding is one of the technologies of joining solid states, which still attracts the researchers’ interest. In welded joints the mechanical properties are affected by a number of mechanical properties of the joined materials and by the process parameters as well. In the present study, the effect of a number of friction stir welding parameters on the tensile strength of the welded joint have been investigated using the Taguchi method and the analysis of variance (ANOVA). The study considers different levels of friction stir welding variables; namely, different rotational speeds of (2000, 1600, 1250 rpm), different welding speeds (12.5, 16, 20 mm / min), and different welding tilt angles (0, 1, 2 degrees). The optimum process parameters and their contribution rate were selected based on the Taguchi method for test design and by using the Minitab 16 program. In this study, the best results (i.e, higher tensile strength) were obtained at a rotational velocity of 1600 rpm, linear velocity of 16 mm / min, and welding angle, 1o. The highest tensile strength was obtained under these conditions.

Download Full-text

Optimization of Process Parameters for Production of Pectinase using Bacillus Subtilis MF447840.1

Recent Patents on Biotechnology ◽

10.2174/1872208312666180917094428 ◽

2019 ◽

Vol 13 (1) ◽

pp. 69-73 ◽

Cited By ~ 2

Author(s):

Ram Balak Mahto ◽

Mukesh Yadav ◽

Soumya Sasmal ◽

Biswnath Bhunia

Keyword(s):

Bacillus Subtilis ◽

Process Parameters ◽

Volume Ratio ◽

Optimum Process ◽

Agitation Rate ◽

Fermentation Condition ◽

Optimization Of Process Parameters ◽

Food And Beverage ◽

Pectinase Production ◽

Sterile Product

Background: Pectinase enzyme has immense industrial prospects in the food and beverage industries. Objective: In our investigation, we find out the optimum process parameters suitable for better pectinase generation by Bacillus subtilis MF447840.1 using submerged fermentation. Method: 2% (OD600 nm = 0.2) of pure Bacillus subtilis MF447840.1 bacterial culture was inoculated in sterile product production media. The production media components used for this study were 1 g/l of pectin, 2 g/l of (NH4)2SO4, 1 g/l of NaCl, 0.25 g/l of K2HPO4, 0.25 g/l of KH2PO4 and 1 g/l of MgSO4 for pectinase generation. We reviewed all recent patents on pectinase production and utilization. The various process parameters were observed by changing one variable time method. Results: The optimum fermentation condition of different parameters was noticed to be 5% inoculums, 25% volume ratio, temperature (37°C), pH (7.4) and agitation rate (120 rpm) following 4 days incubation. Conclusion: Maximum pectinase generation was noticed as 345 ± 12.35 U following 4 days incubation.

Download Full-text

Crystallization and Mechanical Properties of Glass Fiber Reinforced Polypropylene Composites Molded by Rapid Heat Cycle Molding

Fibers and Polymers ◽

10.1007/s12221-020-1284-8 ◽

2020 ◽

Vol 21 (12) ◽

pp. 2915-2926

Author(s):

Aimin Zhang ◽

Guoqun Zhao ◽

Jialong Chai ◽

Junji Hou ◽

Chunxia Yang ◽

...

Keyword(s):

Mechanical Properties ◽

Glass Fiber ◽

Fiber Reinforced ◽

Heat Cycle ◽

Polypropylene Composites ◽

Glass Fiber Reinforced

Download Full-text

Prediction model for the mechanical properties of compacted poplar powder generated via hot-pressing

BioResources ◽

10.15376/biores.16.3.4947-4962 ◽

2021 ◽

Vol 16 (3) ◽

pp. 4947-4962

Author(s):

Jin Yan ◽

Jianan Liu ◽

Liqiang Zhang ◽

Zhili Tan ◽

Haoran Zhang ◽

...

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Network Model ◽

Process Parameters ◽

Neural Network Model ◽

Hot Pressing ◽

Process Conditions ◽

Wood Powder ◽

Single Factor ◽

The Relationship

The influence of the process parameters on the mechanical properties of compact wood powder generated via hot-pressing was analyzed through a single-factor experiment. The mechanical properties exhibited a nonlinear trend relative to the process conditions of hot-pressed compact wood powder. The relationship models between the process parameters and the mechanical properties for the compact wood powder were established by applying a multiple regression analysis and neural network methods combined with data from an orthogonal array design. A comparison between experimental and predicted results was made to investigate the accuracy of the established models by applying several data groups among the single-factor experiments. The results showed that the accuracy of the neural network model in terms of predicting the mechanical properties was greater compared with the multiple regression model. This demonstrates that the established neural network model had a better prediction performance, and it can accurately map the relationship between the process conditions and the mechanical properties of the compact wood powder.

Download Full-text