Warpage and Shrinkage Optimization of Injection-Molded Plastic Spoon Parts for Biodegradable Polymers Using Taguchi, ANOVA and Artificial Neural Network Methods

In this study, it is attempted to give an insight into the injection processability of three self-prepared polymers from A to Z. This work presents material analysis, injection molding simulation, design of ex- periments alongside considering all interaction effects of controlling parameters carefully for green biodegradable polymeric systems, including polylactic acid （PLA）, polylactic acid-thermoplastic poly- urethane （PLA-TPU） and polylactic acid-thermoplastic starch （PLA-TPS）. The experiments were carried out using injection molding simulation software Autodesk Moldflov~~ in order to minimize warpage and volumetric shrinkage for each of the mentioned systems. The analysis was conducted by changing five significant processing parameters, including coolant temperature, packing time, packing pressure, mold temperature and melt temperature. Taguchi＇s [.27 （35） orthogonal array was selected as an efficient method for design of simulations in order to consider the interaction effects of the parameters and reduce spu- rious simulations. Meanwhile, artificial neural network （ANN） was also used for pattern recognition and optimization through modifying the processing conditions. The Taguchi coupled analysis of variance （ANOVA） and ANN analysis resulted in definition of optimum levels for each factor by two completely different methods. According to the results, melting temperature, coolant temperature and packing time had significant influence on the shrinkage and warpage. The ANN optimal level selection for minimiza- tion of shrinkage and/or warpage is in good agreement with ANOVA optimal level selection results. This investigation indicates that PLA-TPU compound exhibits the highest resistance to warpage and shrink- age defects compared to the other studied compounds.

Modeling, analysis, and screening of cyclic pressure pulsing with nitrogen in hydraulically fractured wells

Cyclic pressure pulsing with nitrogen is studied for hydraulically fractured wells in depleted reservoirs.A compositional simulation model is constructed to represent the hydraulic fractures through local-grid refinement.The process is analyzed from both operational and reservoir/hydraulic-fracture perspectives.Key sensitivity parameters for the operational component are chosen as the injection rate,lengths of injection and soaking periods and the economic rate limit to shut-in the well.For the reservoir/hydraulic fracturing components,reservoir permeability,hydraulic fracture permeability,effective thickness and half-length are used.These parameters are varied at five levels.A full-factorial experimental design is utilized to run 1250 cases.The study shows that within the ranges studied,the gas-injection process is applied successfully for a 20-year project period with net present values based on the incremental recoveries greater than zero.It is observed that the cycle rate limit,injection and soaking periods must be optimized to maximize the efficiency.The simulation results are used to develop a neural network based proxy model that can be used as a screening tool for the process.The proxy model is validated with blind-cases with a correlation coefficient of 0.96.