Engineering and Technology Journal

Chemical machining is a well-known nontraditional machining process and is the controlled chemical dissolution of the machined work piece material by contact with a strong acidic or alkaline chemical reagent. It is also called as chemical etching. The present work is aimed at studying the effect of machining time, machining temperature, etching solution concentration on the surface finish of stainless steel 304 using mixed of acids (HCL+HNO3+HF+ H2SO4+H2O). Alloy samples are of (33×33×6) mm dimensions. Three machining temperatures (45, 50 and 55 ºC) for each of which three machining times (3, 6, and 9 min) were used as machining conditions. Surface roughness increases with the machining temperature and machining time. An assessment of CHM was achieved by empirical models for selecting the appropriate machining conditions of the required surface finish. The models were designed based on multiple regression method via Mtb 16 software.

A new multivariate multi site MV.MS.Reg model is developed in this
research depended on regression analysis mixed with Auto regressive multisite
Matalas model (AMMM)and used for water demand forecasting .This developed
model was applied to Kerkuk city as a case study for long term forecasting of
water demand for different types such as domestic demand,industrial,commercial
and public demand.This was done by dividing the city into four sites and
dividing the total water demand in each site into three types of
demand(domestic,industrial with commercial and public demand) .Each type of
water demand in each site was analyzed by multivariate regression base then the
cross correlation between this type of demand for the four sites were included in
the model using multi site Matalas model.Many explanatory variables were
concluded to be most effective factors affecting different types of demands such
as monthly temperature,monthly evaporation ,number of residential units
,number of industrial and commercial units and number of public units which
were forecasted successfully using Stochastic weather generation (SWG)
method.