Hybrid silane-crosslinking polymers: a potential alternative to polyurethane wood adhesives? A preliminary investigation by TMA
In the last years, the research on wood adhesives has focused on matching technical performance with low health and environmental impacts. This study presents the first development of a new adhesive technology based on hybrid silane-crosslinking polymers that can combine both silicone’s and polyurethane’s adhesive properties. The advantage of hybrid silane-crosslinking polymers is the absence of potential harmful chemicals, such as isocyanates and solvents. Two hybrid formulations were characterized by thermo-mechanical analysis (TMA) and glued wood product elasticity is discussed in this brief original.
Silane-terminated polyurethane applied to a moisture-curable pressure-sensitive adhesive using triethoxysilane
A silane-terminated polyurethane (SPU) modified by a silane end-capper anilinomethyltriethoxysilane (AMTES) was synthesized and acted as a pressure-sensitive adhesive (PSA) which could be moisture-cured at room temperature without releasing CO2. Tuning the NCO/OH ratio (R value) and polyols molecular weight altered the performances of the SPU, and satisfied different applications. Putting emphasis on the characteristic properties of PSA, the increase of R value enhanced the peel strength and holding power, but lowered the tack; increasing the polyols molecular weight reversed the trend of the peel strength, holding power and tack of PSA. The obtained PSA was thermostable at 250 °C and almost uncrystallized. Additionally, the glass transition temperature (Tg) and contact angle also changed with the R value and polyols molecular weight.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.