A systematic approach to preliminary polymer process development: modeling and design

A general and complete methodology is presented to facilitate systematic modeling and design of polymer processes during the early development period. To capture and handle the subjective type of uncertainty, embedded in the preliminary process development, fuzzy theories are used as a basis to model and design the process in the presence of ambiguity and vagueness. Physical membership functions are developed for mapping the relation between process variables and the associated fuzzy uncertainties. Based on the qualitative results generated using our previously proposed “linguistic based preliminary design method,” the process modeling can be followed even in the absence of any process governing equations. The modeling is carried out by establishing an appropriate fuzzy reasoning system which provides a specific functional mapping that relates input process variables to one (or more than one) output performance parameter(s). A reduced yet feasible domain is generated by our qualitative design scheme to constrain the process variables. Now, any optimization routine can then be employed to search for a proper process design. We demonstrate the effectiveness of the proposed methodology by its application to a typical compression molding process.     

Mössbauer effect studies and X-ray diffraction analysis of cobalt ferrite prepared in powder form by thermal decomposition method

Cobalt ferrite (Co_xFe_3?xO₄) is prepared in powder form by thermal decomposition of iron and cobalt salts and is analysed by X-ray diffraction and Mössbauer spectroscopic techniques. The variation of Mössbauer parameters, lattice parameters and crystallite size of the products formed with variation in the composition of Fe and Co ratios are studied. The studies confirm the formation of nano-size cobalt ferrite particles with defect structure and it is found to be maximum for the Fe : Co = 60 : 40 ratio of the initial precursor oxides.     

NEURAL-NETWORK-AIDED DESIGN OF AUTOMOBILE EXHAUST CATALYSTS

A priori design of catalysts is not yet possible. Such task would demand unavailable scientific knowledge of the correlations among synthesis parameters and resulting solid state and surface structures, on the one hand, and among those atomic-level structural details and their catalytic functions, on the other hand. To avoid testing every possible combination, therefore, the applied chemist or chemical engineer must identify empirical correlations underlying the existing experimental data base.

The ability of artificial neural networks to identify complex correlations and to predict the result of experiments has recently generated considerable interest in various areas of science and engineering. In this paper, neural networks are used to identify composition-performance relationships in automobile exhaust catalysts.

This work employs an artificial neural network technique to do a sensitivity analysis of the conversions of pollutant gases as a function of the catalyst composition and the operating conditions. This approach converges on the optimum catalyst composition and operating condition in order to produce specified conversions of carbon monoxide, hydrocarbons and nitrogen oxides, to carbon dioxide, water and di-nitrogen respectively.     

Melt‐stable poly(1,4‐dioxan‐2‐one) (co)polymers by ring‐opening polymerization via continuous reactive extrusion

Bulk polymerization of ?‐caprolactone (CL), 1,4‐dioxan‐2‐one (PDX), and mixtures of PDX and CL was carried out by initiation with Al(O^secBu)₃ in a co‐rotating twin‐screw extruder through a fast single‐step process. Both homopolymerizations and copolymerization of PDX and CL proceed very rapidly and reach almost complete (co)‐ monomer(s) conversion as soon as 8 mol% of CL are added in the feed. Even though poly(1,4‐dioxan‐2‐one) (PPDX) is known to thermally degrade mainly through unzipping depolymerization promoted from the hydroxyl end‐groups and yielding PDX monomer, it turns out that the thermal stability of PPDX chains is substantially improved by the copolymerization of PDX with limited amounts of CL. Interestingly, DSC analysis of the so‐obtained P(PDX‐co‐CL) copolymers has demonstrated that a CL molar fraction as high as 11 mol% does not prevent the crystallization of the resulting copolymer, which retains a melting temperature close to 95°C. This last observation has been explained by the formation of a blocky‐like copolymer structure, in which short PPDX and PCL sequences are randomly distributed. POLYM. ENG. SCI., 45:622–629, 2005. © 2005 Society of Plastics Engineers.     

Biobased flexible polyurethane foams manufactured from lactide-based polyester-ether polyols for automotive applications

In this study, biobased polyester-ether polyols derived from meso-lactide and dimer acids were evaluated for flexible polyurethane foams (PUF) applications. Initially, the catalyst concentration was optimized for the biobased PUF containing 30% of biobased polyol (70% petroleum-based polyol). Then, the same formulation was used for biobased PUF synthesis containing 10%–40% of biobased polyols. The performance of biobased PUF was compared with the performance of the control foam made with 100% petroleum-based polyol. The characteristic times (cream, top of the cup, string gel, rise, tack-free) of biobased PUF were determined. The biobased PUF were evaluated for the mechanical (tensile and compressive) and morphological properties. As the wet compression set is important for automotive applications, it was measured for all biobased PUF. The thermal degradation behavior of biobased PUF was also evaluated and compared with the control foam. The effect of different hydroxyl and acid values of polyols on the mechanical properties of biobased PUF is also discussed. The miscibility of all components of PUF formulations is crucial in order to produce a foam with uniform properties. Thus, the miscibility of biobased polyols with commercial petroleum-based polyol was studied.     

VBTC: GPU‐Friendly Variable Block Size Texture Encoding

Recent advances in computer graphics have relied on high‐quality textures in order to generate photorealistic real‐time images. Texture compression standards meet these growing demands for data, but current texture compression schemes use fixed‐rate methods where statically sized blocks of pixels are represented using the same numbers of bits irrespective of their data content. In order to account for the natural variation in detail, we present an alternative format that allows variable bit‐rate texture compression with minimal changes to texturing hardware. Our proposed scheme uses one additional level of indirection to allow the variation of the block size across the same texture. This single change is exploited to both vary the amount of bits allocated to certain parts of the texture and to duplicate redundant texture information across multiple pixels. To minimize hardware changes, the method picks combinations of block sizes and compression methods from existing fixed‐rate standards. With this approach, our method is able to demonstrate energy savings of up to 50%, as well as higher quality compressed textures over current state of the art techniques.     

Towards locally and globally shape-aware reverse 3D modeling

The process of re-creating CAD models from actual physical parts, formally known as digital shape reconstruction (DSR) is an integral part of product development, especially in re-design. While, the majority of current methods used in DSR are surface-based, our overarching goal is to obtain direct parameterization of 3D meshes, by avoiding the actual segmentation of the mesh into different surfaces. As a first step towards reverse modeling physical parts, we extract (1) locally prominent cross-sections (PCS) from triangular meshes, and (2) organize and cluster them into sweep components, which form the basic building blocks of the re-created CAD model. In this paper, we introduce two new algorithms derived from Locally Linear Embedding (LLE) (Roweis and Sauk, 2000 [3]) and Affinity Propagation (AP) (Frey and Dueck, 2007 [4]) for organizing and clustering PCS. The LLE algorithm analyzes the cross-sections (PCS) using their geometric properties to build a global manifold in an embedded space. The AP algorithm, then clusters the local cross sections by propagating affinities among them in the embedded space to form different sweep components. We demonstrate the robustness and efficiency of the algorithms through many examples including actual laser-scanned (point cloud) mechanical parts.     

Hydrolysis and Condensation of Hydrophilic Alkoxysilanes Under Acidic Conditions

A hydrophilic silane was obtained from the reaction of ethylene carbonate and 3-aminopropyldiethoxymethylsilane. This silane undergoes rearrangement to yield an AB₂-type hyperbranched polymer under anhydrous conditions but hydrolyzes and condenses to produce linear siloxanes under acid hydrolysis. The hydrolysis and condensation reactions as a function of time, HCl concentration and water content were studied by ²⁹Si NMR. The compositions of the silanol containing hydrolysis intermediates and the siloxanes condensation products were identified under different conditions. The instantaneous composition was found to depend on the specific combination of the acid and the water. Under certain conditions the intermediate silane-diols were stable and did not condense even under mild acidic conditions.