Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Includes bibliographical references. / by Francis Abbott Waldman. / M.S.

Dissertation (Ph. D.)--University of Akron, Dept. of Polymer Engineering, 2009. / "May, 2009." Title from electronic dissertation title page (viewed 11/27/2009) Advisor, Avraam I. Isayev; Committee members, James L. White, Erol Sancaktar, Kevin Kreider, Minel J. Braun; Department Chair, Sadhan C. Jana; Dean of the College, Stephen Cheng; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

The thermomechanical history experienced by plastics melts and solids during processing leads to the development of microstructure, which directly influences the ultimate properties of the plastic articles. Therefore, besides the predictions of thermomechanical history, the prediction of microstructure represents an important aspect of the modeling of plastics processing. / A comprehensive model has been developed to describe the behaviour of thermoplastic polymer melts during the injection molding process. The model, which deals with the three stages of the process (filling, packing and cooling), employs a viscoelastic constitutive equation and incorporates crystallization kinetics. All the properties of the material employed are based on experimental data. The model predicts fill times, velocity, temperature and pressure distributions, the distribution of shear and normal stresses, and stress relaxation. The predictions of the model are compared to experimental data relating to pressure distributions and variation with time, fill times, and the distributions of crystallinity, orientation and tensile modulus.

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Integrated high-temperature (HT) ultrasonic sensors have been developed successfully by using piezoelectric bismuth titanate and lead zirconate titanate films HT ultrasonic transducers for real-time, non-destructive, and non-intrusive diagnosis of polymer injection molding (IM) processes. The HT ultrasonic sensors can be integrated onto the barrel and/or mold of IM machine, according to the customer's requirements. These sensors can be operated up to 400°C without cooling system and ultrasonic couplant, and can be miniaturized with sufficient signal strength and signal-to-noise ratio. / The chosen IM processes are grouped to large- and small-scale 1M processes. The large-scale ones include conventional IM, co-injection molding (COIM), and fluid (gas/water) assisted injection molding (GAIM/WAIM). A filling incompleteness of 1 volume-% for IM of polycarbonate (PC) part, the core (PC) material movement and layers dimensions for COIM, the fluid motion, thickness and deformation of the hollowed high-density polyethylene (HDPE) part for GAIM/WAIM were diagnosed during processing by ultrasonic sensors and techniques developed. / The small-scale ones include IM for microfluidic device (IMMF) and micromolding (MM). The optimization of holding pressure for producing a flat polymethyl methacrylate (PMMA) part (surface roughness < 5 mum) having micro structures for IMMF, estimation of temperature of polyacetal copolymer (POM) melt in the barrel and filler concentration of nylon 66 (PA66) mixed with polyhedral oligomeric silsesquioxanes (POSS) part in the mold for MM, and evaluation of thickness variation of molded alumina ceramic powder part for MM were demonstrated. The melting stages and quality of low-density polyethylene (LDPE) in the barrel has been successfully monitored using ultrasound. The important phenomena during melting processes, such as partially melting pellets, air bubbles, melting completeness, and effects of melting temperature and rotation speed have been diagnosed by ultrasonic signatures. / These diagnostic results verify that the developed integrated HT ultrasonic sensors and techniques are capable of monitoring various IM processes to fabricate parts and products having complex formation, tiny size and micro structures, and evaluating the part quality in order to provide timely information for process optimization.