4.3.3. Fuzzification

First, we proposed the parameters in our tests on the CMMs, which are within the Metrology Laboratory of ENP-Oran (see Table 1). These are very important for simulation calculations.


Table 1. Parameters of the test.

So this step allowed us to give the different linguistic variables that will be used during the gear control by the fuzzy logic [7–15].

#### 4.3.4. Rule base (inference)

Indeed, we exploit Table 2 to build the inference of fuzzy logic in C; however, we do not have to complete all the boxes. The rules are developed by an expert and his knowledge of the problem [12, 13, 15].


N.B: After completing the logic matrix of Table 2, it was concluded that the factors that

Over the last 20 years, remarkable progress has been made in three-dimensional measurement technology with regard to the mechanical elements of the machine, control equipment, and

Fuzzy Logic Applications in Metrology Processes http://dx.doi.org/10.5772/intechopen.79381 49

(2)

Matrix ¼

influenced on the logic estimator are:

Table 2. Matrix of fuzzy logic.

5. Probing and data processing

software.

#### 4.4. Construction of fuzzy logic matrix

The matrix is in the form of Table 2 or a matrix that we can build according to the previous parameters, Δki, Δαr, and Δzi, while the purpose of this table is to know which elements are most influenced during the implementation of fuzzy logic to spur gears.

Table 2. Matrix of fuzzy logic.

4.3.2. Output

4.3.3. Fuzzification

gear control by the fuzzy logic [7–15].

4.4. Construction of fuzzy logic matrix

4.3.4. Rule base (inference)

Table 1. Parameters of the test.

problem [12, 13, 15].

calculations.

The outputs are based on the problem that was posed; anyway, we can find one or more outputs and so on. Finally, it is lucid that the outputs in our work are two: xm and ym.

48 Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications

First, we proposed the parameters in our tests on the CMMs, which are within the Metrology Laboratory of ENP-Oran (see Table 1). These are very important for simulation

t (s) — 250 θ (radian) 0.2 30 xm (mm) — 26 ym (mm) — 26 i 100 200 Δki — 200 Δzi — 200 α<sup>i</sup> — 200

So this step allowed us to give the different linguistic variables that will be used during the

Indeed, we exploit Table 2 to build the inference of fuzzy logic in C; however, we do not have to complete all the boxes. The rules are developed by an expert and his knowledge of the

IF conditions:…THEN action

The matrix is in the form of Table 2 or a matrix that we can build according to the previous parameters, Δki, Δαr, and Δzi, while the purpose of this table is to know which elements are

most influenced during the implementation of fuzzy logic to spur gears.

IFconditions…THEN action (1)

Minimum value Maximum value

N.B: After completing the logic matrix of Table 2, it was concluded that the factors that influenced on the logic estimator are:

$$Matrix = \begin{bmatrix} 0 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 & 0 \end{bmatrix} \tag{2}$$
