Abstract:
The objective of this work was an experimental investigation and optimization of
parameters of coffee husk-sawdust chipboard manufacturing. The performance measures
considered were: - modulus of rupture, tensile modulus, swelling thickness and water
absorption. The process parameters included in this study were pressing pressure,
temperature, time, coffee husk to sawdust ratios and unsaturated polyester percentage.
The results revealed that flexural strength of the chipboards with respect to the coffee husk
to sawdust ratios increases as the weight fraction of wood sawdust increases, the maximum
modulus of rupture obtained was 24.82 MPa at the 1:1 coffee to sawdust weight ratios.
The tensile strength of the chipboard increases moderately as weight fraction of the
sawdust increases. The results showed that maximum tensile modulus 62.34Mpa and
7.15Mpa tensile strength at the 1:1 coffee husk to sawdust weight ratios respectively. The
capability of the chipboards to absorb water and dimensional instability increases
significantly as coffee husk ratio increases. Higher swelling thickness was revealed for
boards from the coffee husk. In multi-objective optimization of process parameters of
chipboard manufacturing, experiments were conducted according to standard L18 design
of experiment orthogonal array designed. The Taguchi with Grey relational method has
been employed for optimization. Analysis was performed using Minitab 17 soft-ware and
the results showed that the optimal combination for the parameters were: pressing pressure
(4Mpa), temperature 160℃ , time (8min.), 1:1 coffee husk to sawdust ratio and unsaturated
polyester (60%). The ANOVA results showed that pressing temperature (33.11%), coffee
husk to sawdust (18.68%) and unsaturated polyester loading percentage (17.92%) are the
most influencing parameters that affects the multi-response of chipboard manufacturing
followed by pressing time (13.22%) and pressing pressure (2.75%). A confirmation test
result revealed the improvement in the grey grade for optimal process parameters by
employing the Taguchi-Grey relational analysis method.
