An Analysis of Interfacial Adhesion Between TPU/MWCNT Composites and ABS Substrate by Over Injection Molding

2016 ◽  
Author(s):  
Catalin Fetecau ◽  
Felicia Stan ◽  
Nicoleta Violeta Cristea ◽  
Laurentiu Ionut Sandu
Keyword(s):  
Carbon Nanotubes ◽  
Injection Molding ◽  
Flow Behavior ◽  
Thermoplastic Polyurethane ◽  
Acrylonitrile Butadiene Styrene ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Multi Walled Carbon Nanotubes ◽  
Walled Carbon Nanotubes

In this work, the advantages of Thermoplastic Polyurethane (TPU) filled with multi-walled carbon nanotubes (MWCNTs) were combined with those of the over injection molding process in order to obtain two-component (2k) structures with very different but high mechanical and electrical properties. TPU/MWCNT composites with different MWCNTs wt.% were over-molded onto Acrylonitrile Butadiene Styrene (ABS) substrates, under different processing conditions, and the adhesion was assessed by T-peel tests at room temperature. Since adhesion is also related to flow behavior, the rheological properties were studied with a capillary rheometer at shear rates similar to those of the injection molding process (102∼104s−1). Experimental results indicated that the most effective way to control the adhesion between the ABS substrate and the over-molded TPU/MWCNT composite is to increase the melt temperature. The addition of carbon nanotubes improves adhesion in the vicinity of 0.5 wt.% MWCNTs.

Bioactive poly(etheretherketone) composite containing calcium polyphosphate and multi-walled carbon nanotubes for bone repair: Mechanical property and in vitro biocompatibility

2018 ◽  
Vol 33 (5) ◽  
pp. 543-557 ◽  
Author(s):  
Jianfei Cao ◽  
Yue Lu ◽  
Hechun Chen ◽  
Lifang Zhang ◽  
Chengdong Xiong
Keyword(s):  
Mechanical Property ◽  
Carbon Nanotubes ◽  
Bone Repair ◽  
Mechanical Performance ◽  
Injection Molding Process ◽  
Molding Process ◽  
Calcium Polyphosphate ◽  
Multi Walled Carbon Nanotubes ◽  
Walled Carbon Nanotubes

Poly(etheretherketone) exhibits good biocompatibility, excellent mechanical properties, and bone-like stiffness. However, the natural bio-inertness of pure poly(etheretherketone) hinders its applications in biomedical field, especially when direct bone-implant osteo-integration is desired. For developing an alternative biomaterial for load-bearing orthopedic application, combination of bioactive fillers with poly(etheretherketone) matrix is a feasible approach. In this study, a bioactive multi-walled carbon nanotubes/calcium polyphosphate/poly(etheretherketone) composite was prepared through a compounding and injection-molding process for the first time. Bioactive calcium polyphosphate was added to polymer matrix to enhance the bioactivity of the composite, and incorporation of multi-walled carbon nanotubes to composite was aimed to improve both the mechanical property and biocompatibility. Furthermore, the microstructures, surface hydrophilicity, and mechanical property of multi-walled carbon nanotubes/calcium polyphosphate/poly(etheretherketone) composite, as well as the cellular responses of MC3T3-E1 osteoblast cells to this material were investigated. The mechanical testing revealed that mechanical performance of the resulting ternary composite was significantly enhanced by adding the multi-walled carbon nanotubes and the mechanical values obtained were close to or higher than those of human cortical bone. More importantly, cell culture tests showed that initial cell adhesion, cell viability, and osteogenic differentiation of MC3T3-E1 cells were significantly promoted on the multi-walled carbon nanotubes/calcium polyphosphate/poly(etheretherketone) composite. Accordingly, the multi-walled carbon nanotubes/calcium polyphosphate/poly(etheretherketone) composite may be used as a promising bone repair material in dental and orthopedic applications.

Rheological Behavior of Carbon Nanotubes / Copper Feedstocks for Metal Injection Molding

2011 ◽  
Vol 403-408 ◽  
pp. 5335-5340 ◽  
Author(s):  
Faiz Ahmad ◽  
Ali Samer Muhsan ◽  
M. Rafi Raza
Keyword(s):  
Carbon Nanotubes ◽  
Injection Molding ◽  
Flow Behavior ◽  
Metal Injection Molding ◽  
Increasing Trend ◽  
Multi Walled Carbon Nanotubes ◽  
Blade Mixer ◽  
Near Net Shape ◽  
Copper Composite ◽  
Walled Carbon Nanotubes

Metal injection molding (MIM) technology is known for its ability of producing near net shape components. This study presents the results of flow behavior of multi-walled carbon nanotubes (MWCNTs) reinforced copper composites mixes. The solid loadings in the copper mixes were investigated in the ranges of 55-61 V% using a binder. Copper mixes and copper/MWCNTs were compounded using a Z-blade mixer for homogenous dispersion of solids in the binder. Results identified a mix containing 59 V% copper suitable for substitution of MWCNTs. The flow properties were measured using a capillary rheometer in the shear rate range expected to occur during metal injection molding. An increasing trend in viscosity of the copper mixes with powder loading was noted. Copper/MWCNTs composite mixes showed viscosity more than 1000 Pa.s perhaps due to addition of MWCNTs and increasing trend in viscosity of copper/MWECNTs was recorded. The results of flow data showed that all copper composite mix containing up to 10 Vol.% MWCNTs were successfully injection molding and test samples were produced.

Rheological Properties of Thermoplastic Polyurethane/Multi-Walled Carbon Nanotube Nanocomposites

2016 ◽  
Vol 699 ◽  
pp. 18-24
Author(s):  
Ionut Laurentiu Sandu ◽  
Razvan Rosculet ◽  
Catalin Fetecau
Keyword(s):  
Carbon Nanotubes ◽  
Rheological Behavior ◽  
Flow Behavior ◽  
Thermoplastic Polyurethane ◽  
Polymer Processing ◽  
Effect Of Temperature ◽  
Capillary Rheometry ◽  
Multi Walled Carbon Nanotubes ◽  
Processing Techniques ◽  
Walled Carbon Nanotubes

Carbon nanotubes offer the possibility of substantial improvements in the properties of polymer-based composites. However, adding carbon nanotubes increases the viscosity and makes the composites more difficult to process. Consequently, understanding the rheological behavior of nanocomposites is important from both the theoretical and industrial points of view. In the present work, rheological behavior of thermoplastic polyurethane filled with various amounts (1, 3 and 5 wt.%) of multi-walled carbon nanotubes was investigated by capillary rheometry. In this regard, the melt flow behavior of the nanocomposite was measured using a capillary rheometer with a die length-diameter ratio of 30:1, 20:1 and 10:1. In order to investigate the effect of temperature on viscosity, the tests were carried out in the temperature range of 180 to 210°C. The shear rate examined between 100 and 5000 s-1, cover the shear experienced during most polymer processing techniques. The Bagley and Weissenberg-Rabinowitsch correction was performed to determine the real viscosity of the nanocomposites; moreover, the Cross viscosity model coefficients were determined.

scholarly journals Influence of Multiwalled Carbon Nanotubes on the Rheological Behavior and Physical Properties of Kenaf Fiber-Reinforced Polypropylene Composites

Polymers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2083
Author(s):  
Farah ‘Atiqah Abdul Azam ◽  
Zakaria Razak ◽  
Mohd Khairul Fadzly Md Radzi ◽  
Norhamidi Muhamad ◽  
Che Hassan Che Haron ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Injection Molding ◽  
Multiwalled Carbon Nanotubes ◽  
Flow Behavior ◽  
Load Factor ◽  
Injection Molding Process ◽  
Green Composite ◽  
Molding Process ◽  
Multiwalled Carbon ◽  
Kenaf Fiber

The incorporation of kenaf fiber fillers into a polymer matrix has been pronounced in the past few decades. In this study, the effect of multiwalled carbon nanotubes (MWCNTs) with a short kenaf fiber (20 mesh) with polypropylene (PP) added was investigated. The melt blending process was performed using an internal mixer to produce polymer composites with different filler contents, while the suitability of this melt composite for the injection molding process was evaluated. Thermogravimetric analysis (TGA) was carried out to investigate the thermal stability of the raw materials. Rheological analyses were conducted by varying the temperature, load factor, and filler content. The results demonstrate a non-Newtonian pseudoplastic behavior in all samples with changed kenaf fillers (10 to 40 wt %) and MWCNT contents (1 to 4 wt %), which confirm the suitability of the feedstock for the injection molding process. The addition of MWCNTs had an immense effect on the viscosity and an enormous reduction in the feedstock flow behavior. The main contribution of this work is the comprehensive observation of the rheological characteristics of newly produced short PP/kenaf composites that were altered after MWCNT additions. This study also presented an adverse effect on the composites containing MWCNTs, indicating a hydrophilic property with improved water absorption stability and the low flammability effect of PP/kenaf/MWCNT composites. This PP/kenaf/MWCNT green composite produced through the injection molding technique has great potential to be used as car components in the automotive industry.

Comparison between Single and Multi Gates for Minimization of Warpage Using Taguchi Method in Injection Molding Process for ABS Material

2013 ◽  
Vol 594-595 ◽  
pp. 842-851 ◽  
Author(s):  
S.M. Nasir ◽  
Khairul Azwan Ismail ◽  
Z. Shayfull ◽  
Norshah Afizi Shuaib
Keyword(s):  
Injection Molding ◽  
Acrylonitrile Butadiene Styrene ◽  
Mold Material ◽  
Injection Molding Process ◽  
Inlet Temperature ◽  
Molding Process ◽  
Melt Temperature ◽  
Thermoplastic Material ◽  
Packing Pressure ◽  
Packing Time

In this study, a mold is designed in single and dual type of gate in order to investigate the deflection of warpage for thick component in injection molding process. Autodesk Moldflow Insight software was used as a medium for experimental tested. Nessei NEX 1000 injection molding machine and P20 mold material details were entered in this study to get more accurate data on top of Acrylonitrile Butadiene Styrene (ABS) as a molded thermoplastic material. Taguchi orthogonal array, analysis of Signal to Noise (S/N) ratio and Analysis of Variance (ANOVA) were implemented to get the best combination of parameter and significant factor that affect the warpage problem for both types of gates. Coolant inlet temperature, melt temperature, packing pressure and packing time are the selected parameter that used in this study. A conformation test is conducted to verify the combination parameters optimized. From the result, multi gates used was founded that can decrease the deflection of warpage for thick product. From ANOVA, the most significant factor is melt temperature for single gate, and coolant inlet temperature for multi gate. Packing pressure and packing time were slightly influence on warpage problem for both studies.

scholarly journals Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann
Keyword(s):  
Injection Molding ◽  
Transfer Learning ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Melt Temperature ◽  
Processing Industry ◽  
Source Models ◽  
Plastics Processing ◽  
Artificial Neural

AbstractThe necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

Determination of Optimum Process Parameters in Gas-Assisted Injection Molding

2011 ◽  
Vol 143-144 ◽  
pp. 494-498
Author(s):  
Ke Ming Zi ◽  
Li Heng Chen
Keyword(s):  
Injection Molding ◽  
Delay Time ◽  
Optimization Design ◽  
Gas Pressure ◽  
Optimum Process ◽  
Injection Molding Process ◽  
Technological Parameters ◽  
Molding Process ◽  
Element Analysis ◽  
Melt Temperature

With finite element analysis software Moldflow, numerical simulation and studies about FM truck roof handle were conducted on gas-assisted injection molding process. The influences of melt pre-injection shot, gas pressure, delay time and melt temperature were observed by using multi-factor orthogonal experimental method. According to the analysis of the factors' impact on evaluation index, the optimized parameter combination is obtained. Therefore the optimization design of technological parameters is done. The results show that during the gas-assisted injection molding, optimum pre-injection shot is 94%,gas pressure is 15MPa,delay time is 0.5s,melt temperature is 240 oC. This study provided a more practical approach for the gas-assisted injection molding process optimization.

The Characterization and Flow Behavior of 316L Stainless Steel Feedstock for Micro Metal Injection Molding (μMIM)

2010 ◽  
Vol 44-47 ◽  
pp. 2872-2876
Author(s):  
Pei Li Haw ◽  
Norhamidi Muhamad ◽  
Hadi Murthadha
Keyword(s):  
Stainless Steel ◽  
Injection Molding ◽  
316L Stainless Steel ◽  
Flow Behavior ◽  
Palm Stearin ◽  
Metal Injection Molding ◽  
Injection Molding Process ◽  
Binder System ◽  
Molding Process ◽  
Rheological Characterization

The rheological behaviors of the Micro Metal Injection Molding feedstock are important for the stability of the feedstock during micro injection molding process and quality of the final micro-components. Homogeneous feedstocks are preferable for MIM process to ensure the dimensional consistency of molded components and prevent the defects of powder-binder separation or particle segregation. In this work, feedstocks with various formulations of 316L stainless steel and binder system were prepared by using Brabender Plastograph EC Plus mixer. The binder system comprises of palm stearin, polyethelene (PE) and stearic acid. In order to obtain the viscosity, activation energy, flow behavior and mold ability index, the rheological characterization of the feedstocks were investigated in numerous conditions by using Shimadzu 500-D capillary rheometer The study showed that all of the 316L stainless steel feedstocks are homogenous with pseudo-plastic behaviors.

Optimization of Injection Molding Process Parameters with Material Properties Based on GA and BP

2013 ◽  
Vol 345 ◽  
pp. 586-590 ◽  
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Axial Deformation ◽  
Molding Process ◽  
Melt Temperature ◽  
Bp Network ◽  
Injection Parameters ◽  
Moldflow Simulation

To obtain optimal injection process parameters, GA was used to optimize BP network structure based on Moldflow simulation results. The BP network was set up which considering the relationship between volume shrinkage of plastic parts and injection parameters, such as mold temperature, melt temperature, holding pressure and holding time etc. And the optimal process parameters are obtained, which is agreed with actual results. Using BP network to predict injection parameters impact on parts quality can effectively reduce the difficulty and workload of other modeling methods. This method can be extended to other quality prediction in the process of plastic parts.Keyword: Genetic algorithm (GA);Neural network algorithm (BP);Injection molding process optimization;The axial deformation

Sign in / Sign up

Export Citation Format

Share Document