Study on Paper-Based Fiberboard and Vacuum Treatment
According to the required fiber quality and the technical conditions of manufacturing fiberboard, the technology on paper-based fiberboard manufacturing and vacuum treatment was studied. After thorough investigation, the optimum technical conditions have been gained as follows: hot pressing temperature 160¿, hot pressing time 6 min, forming pressure 11MPa and the quantity of adhesive 10 percent. After vacuum treatment by the steam jet system in conditions as chamber below 700 Pascal, temperature in chamber 80¿, treatment time two hours, the contained proportion and emission quantity of the urea formaldehyde resin meet the requirements of GBT11718-1999. In addition to this, the mechanical properties of the paper-based fiberboard are greatly improved at the same time.
Canola straw as a bio-waste resource for medium density fiberboard (MDF) manufacture.
Canola straw as an agricultural residue has been investigated for MDF production. The variables were steaming time (2, 5 and 8 min), the resin content (9% and 11%), and press time (4 and 6 min). Common physical and mechanical properties of experimental boards including modulus of rupture (MOR), modulus of elasticity (MOE), internal bond strength (IB) and thickness swelling (TS) were measured. Fiber properties of canola straw including length, diameter and cell wall thickness were determined. The results showed that all the tested mechanical properties improved with the increase of steaming time level. The results were close to the minimum requirements of MDF specified in the ANSI A208.2 standard. Dimensional stability of the MDFs improved as adhesive content increased. The IB values are positively affected by the increase of press time. MDF properties made from canola straw possess acceptable qualities as compared to those made from other non-wood plants. Furthermore, the fiber dimensions of canola straw were also in the range of reported values in hardwoods.
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.