Surface texture is the pattern of the surface which deviates from a nominal surface. The deviations may be repetitive or random and may result from roughness, waviness, lay, and flaws. The real surface of an object is the peripheral skin which separates it from the surrounding medium. This surface invariably assimilates structural deviations which are classified as form errors, waviness, and surface roughness.

Roughness consists of the finer irregularities of the surface texture, usually including those irregularities that result from the inherent action of the production process. Profiles of roughness and waviness are shown in Figure.

Surface Finish Parameters:

Surface finish could be specified in many different parameters. Due to the need for different parameters in a wide variety of machining operations, a large number of newly developed surface roughness parameters were developed.

Most popular parameters of surface finish specification are described as follows

Roughness average (Ra): This parameter is also known as the arithmetic mean roughness value, AA (arithmetic average) or CLA (center line average). Ra is universally recognized and the most used international parameter of roughness. Therefore Where Ra = the arithmetic average deviation from the mean line L = the sampling length y = the ordinate of the profile curve

Data Mining Process Algorithm

1. Defining the business issue in a precise statement,

2. Defining the data model and data requirements,

3. Sourcing data from all available repositories and preparing the data. They should be selected and filtered from redundant information,

4. Evaluating the data quality,

5. Choosing the mining function and techniques,

6. Interpreting the results and detecting new information,

7. Deploying the results and the new knowledge into your business.

Data Mining Technique

Data mining, one of the steps of Knowledge Discovery in Databases process, is an exploratory data analysis that discovers interesting knowledge, such as associations, patterns, structures and anomalies from large amount of data stored in databases or other information repositories. Descriptive data mining aims to summarize data and extract their characteristics. Predictive data mining, on the other hand, try to find models to forecast future behaviours.