Thesis (Ph.D.)--McMaster University, 2003. / Advisor: M. Elbestawi. Includes bibliographical references ( leaves 221-243). Also available via World Wide Web.

Digitized by Kansas State University Libraries

Call number: LD2668 .T4 1960 A54

Call number: LD2668 .T4 1985 T78 / Master of Science

LD2668 .T4 1962 S74

Milling and co-milling are well known techniques that have potential to enhance the solubility and/or dissolution rate of poorly soluble drugs. There are broadly two aims for this project. The first was to develop an understanding of how individual and combination of techniques may be used to explore the impact of milling on particle characteristics (including phase changes, fractures and change in particle size) as a function of milling time/speed, for a range of single powder materials. Anhydrous (lactose, sucrose), monohydrate (lactose) and dihydrate (trehalose) excipients and a poorly soluble drug (ibuprofen), were chosen as model substrates. Each material was micronized by ball-milling (for various time durations and milling speeds) and then characterized by a range of techniques, specifically, SEM, DSC, TGA, THz and dielectric spectroscopy. The second aim of the project was to investigate the impact of milling and co-milling on the solubility and dissolution rate of ibuprofen after co-milling with a variety of excipients (polymer and surfactants). The principle findings of this programme of work can be summarized as follows: i) ball milling of lactose monohydrate produces nano-structured systems with a mixture of damaged crystals and amorphous phase, that can be characterised by dielectric relaxation spectroscopy (DRS), ii) THz spectroscopy provides estimates for residual crystallinity in lactose monohydrate that were much lower than the estimates from the thermal techniques. Such estimates of residual crystallinity are considered to be more reliable given the fact that the spectroscopic measurement characterizes the material in its native state, whereas thermal techniques require a heating process, which tend to induce de-vitrification and mutarotation of lactose. In case of anhydrous materials, while there was agreement between thermal and THz techniques at long milling times, it was shown that the THz technique was susceptible to moisture absorption and crystallization at short milling times, iii) In the molecular dynamics of milled sugars studied by DRS, the structural relaxation is not visible in the vicinity of glass transition, however the secondary relaxation (β) process is equally capable and provided molecular dynamics in term of activation energy changes. The activation energies of beta process of both lactose and sucrose are least affected by milling time, but the higher activation energies for sucrose as compared with lactose show that sucrose has lower propensity to re-crystallize than lactose during post milling storage, iv) Ibuprofen can be assayed by UV-method in the presence of interfering (in absorption) substance by applying multivariate method involving the calculation of concentration factors and v) Co-milling with soluplus has increased the in the solubility of ibuprofen by ~20% and dissolution rate ~50% in 30 min, while these values are ~5% and 30%, respectively in case of co-milling with HPMC.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

The ultimate goal in the manufacturing of a part is to achieve an economic production plan with precision and accuracy in the first attempt at machining. A physics-based comprehensive modeling of the machining processes is a fundamental requirement in identifying optimal cutting conditions which result in high productivity rates without violating accuracy throughout the part production process. This thesis presents generalized virtual simulation and optimization strategies to predict and optimize performance of milling processes up to 3-axis. Computationally efficient mathematical models are introduced to predict milling process state variables such as chip load, force, torque, and cutting edge engagement at discrete cutter locations. Process states are expressed explicitly as a function of helical cutting edge - part engagement, cutting coefficient and feedrate. Cutters with arbitrary geometries are modeled parametrically, and the intersection of helical cutting edges with workpiece features are evaluated either analytically or numerically depending on geometric complexity. The dynamics of generalized milling operations are modeled and the stability of the process is predicted using both time and frequency domain based models. These algorithms enable rapid simulation of milling operations in a virtual environment as the part features vary. In an effort to reduce machining time, a constraint-based optimization scheme is proposed to maximize the material removal rate by optimally selecting the depth of cut, width of cut, spindle speed and feedrate. A variety of user defined constraints such as maximum tool deflection, torque/power demand, and chatter stability are taken into consideration. Two alternative optimization strategies are presented: pre-process optimization provides allowable depth and width of cut during part programming at the computer aided manufacturing stage using chatter constraint, whereas the post-process optimization tunes only feedrate and spindle speed of an existing part program to maximize productivity without violating physical constraints of the process. Optimized feedrates are filtered by considering machine tool axes limitations and the algorithms are tested in machining various industrial parts. The thesis contributed to the development of a novel 3-axis Virtual Milling System that has been deployed to the manufacturing industry.

No description available.

The ultimate goal in the manufacturing of a part is to achieve an economic production plan with precision and accuracy in the first attempt at machining. A physics-based comprehensive modeling of the machining processes is a fundamental requirement in identifying optimal cutting conditions which result in high productivity rates without violating accuracy throughout the part production process. This thesis presents generalized virtual simulation and optimization strategies to predict and optimize performance of milling processes up to 3-axis. Computationally efficient mathematical models are introduced to predict milling process state variables such as chip load, force, torque, and cutting edge engagement at discrete cutter locations. Process states are expressed explicitly as a function of helical cutting edge - part engagement, cutting coefficient and feedrate. Cutters with arbitrary geometries are modeled parametrically, and the intersection of helical cutting edges with workpiece features are evaluated either analytically or numerically depending on geometric complexity. The dynamics of generalized milling operations are modeled and the stability of the process is predicted using both time and frequency domain based models. These algorithms enable rapid simulation of milling operations in a virtual environment as the part features vary. In an effort to reduce machining time, a constraint-based optimization scheme is proposed to maximize the material removal rate by optimally selecting the depth of cut, width of cut, spindle speed and feedrate. A variety of user defined constraints such as maximum tool deflection, torque/power demand, and chatter stability are taken into consideration. Two alternative optimization strategies are presented: pre-process optimization provides allowable depth and width of cut during part programming at the computer aided manufacturing stage using chatter constraint, whereas the post-process optimization tunes only feedrate and spindle speed of an existing part program to maximize productivity without violating physical constraints of the process. Optimized feedrates are filtered by considering machine tool axes limitations and the algorithms are tested in machining various industrial parts. The thesis contributed to the development of a novel 3-axis Virtual Milling System that has been deployed to the manufacturing industry.