SCDC Sciences & Culture Development Center CEIT-2016 16-18 December 2016 – Hammamet, Tunisia Fuzzy logic application for the design of boxes of muli-spindle drilling Manuscript ID_44 Ayoub Fajraoui, Noureddine Ben Yahia Higher National Engineering School of Tunis (ENSIT), University of Tunis (UT), Mehdi Kamel, Preparatory Institute for Engineering Studies El Manar (IPEIMa), University of Tunis EL Manar (UTM)

Outline Introduction Problem to Solve or to Discuss Materials and Methods Fuzzy reasoning steps Principle of the comparison between the two methods nd fuzzy system structure And I will finish with Evaluation of the optimal method for the design of a gear train by the fuzzy logic Conclusion & Discussion

Abstract This Paper proposes the application of fuzzy logic to the design of drilling housing multi-spindle using MATLAB, Using a conceptual analysis and using design rules for choosing the optimal solution, moreover, the proposed method will help us to select the optimal design solution, We want to see if the fuzzy logic is useful in our example of topological design mechanism and if it can correct some things that did not give us full satisfaction . We know , through a simple example , the fuzzy algorithm used for comparing two solutions. Once the notion of comparison is defined , it will be easy to make a ranking for the final stage. Several demonstrative simulations are carried out and show the effectiveness and the robustness of the proposed control approach under the input variations.

Introduction The evolution of technical progress and the scientific discoveries allowed the development and the progression of the production systems. Moreover, establishment of systems CAD, as well as the developments in the algorithms and programming techniques allowed the automation and the structuring of production methods, therefore an improvement of productivity. Also, the fuzzy logic used in much research task: as for the optimization of the machine tools of machining…etc. In this prospect, we will present, in this work, a new model based on a fuzzy logic approach allowing the automatic choice of the optimal method. However, the fuzzy logic is regarded as a refuge for the engineers seeing their capacities to solve the problems of classification, training, association, recognition form...etc, therefore the efficacy and the faithfulness of given results

Problem to Solve or to Discuss Multi-spindle drilling machines are used for mass production, a great time saver where many pieces of jobs having many holes are to be drilled. multi-spindle head machines are used in mechanical industry in order to increase the productivity of machining systems. The context of my researches in Aided system desing (mechanical Engineering)

Problem to Solve or to Discuss The model that we are creating is a multi-spindle drilling machine, we outline methods for automatic determination of toothed helical gear trains and the selection criteria for the optimal choice of gear trains. The second step is to choose the best design method from the fuzzy logic. We now replaced the traditional multi-criteria ranking method. The choice of these qualities (solutions) depends heavily on the type of mechanism design. *We have applied two methods. As first design to use an expert system for the design and then optimize the design that is why we used Kappa PC and Catia for CAD. *and the qualifications of the expert who is supposed to give the notes to each the basic mechanism.

Materials and Methods Generally, any fuzzy system breaks down into four main blocks: 1- A knowledge base that includes the different rules as well as the membership functions for inputs and outputs. 2- A decision block or inference engine that generates a conclusion from fuzzy entries and different active rules. 3- Fuzzification which transforms precise input quantities into fuzzy quantities thanks to the membership functions. 4- A defuzzification that transforms the fuzzy results into precise outputs. So we used Matlab toolbox for the application

Materials and Methods Method N°1 Method N°2 𝑍2= 𝑍1∗𝐷2 𝐷1 Assume Z1 No Increase, Z1   Yes OK Assume Z1 𝑍2= 𝑍1∗𝐷2 𝐷1 Application of the formula 𝑍1 ≥ 2∗𝑎∗ 1 𝑍2 ∗𝑃𝑑 1+ 1 𝑍2 ∗ 1 𝑍2 +2 ∗ sin 2 ∅−1 So we have the two method that we talked about , I will make a brief description : The first method : Design office , Customer specifications , d times m

Materials and Methods Before determining the suitable machine tool for the proposed machining entity, it is necessary to evaluate and calculate the number of teeth and the interferences between the gears. Then, from the two methods, we develop a mad system FL1 whose inputs are the design time (DT) and the number of iterations to obtain the final solution (IFR), as well as the output is the choice of the final design solution (Solution 1 or 2) 1* Indeed, two methods have been developed which consist in dehulling the gear train from the ratio of reduction or number of teeth.

Materials and Methods Principle of the comparison between the two methods and fuzzy system structure So , hte comparaison between the two methods will be like this

Summary of rating criteria Results Note that the initial information is qualitative in nature since it is relatively vague assessments. They are expressed in a form similar to that used by naturally an expert to give his point of view. Is used in this simple case, four quality classes each defined by a membership function with a triangular profile Advanced method of ranking Summary of rating criteria The fuzzy variable values, properly defined by membership functions are interlinked by rules, to draw conclusions. This is called fuzzy deductions. In this context, there are two kinds of inference rules: Inference with one rule or Inference with several rules In the fuzzy controller inference involved operators AND OR. The AND operator applies to variables inside of a rule, while the OR operator links the different rules.

Results In this work, we used the method of the center of gravity in the defuzzification phase of our fuzzy system, it allows to produce better results of optimal solutions. The obtained fuzzy surfaces for Methods and criteria are given in These figures which show their variation. We now combine the criterion by criterion comparisons into one. The reasoning is like this: If there is superiority and inferiority by C1 as C2 then there is equivalence.

Results

Results

Results This study can be improved in the case where the rules are classified If..Then by applying certain criteria of resistances, algorithms allowing the determination of the different geometrical, dimensional and functional characteristics of the different pairs of gears. .

Conclusion The use of fuzzy logic facilitates the creation of basic mechanism by making computer knowledge translation expert easier.In this paper, we present our novel approach for the design of gear train. Our main contribution consists on the use of the fuzzy logic system to evaluate the optimal solution according to expert notation. Besides , This procedure will help us to choose the output which are discrete variables to be an input to the Expert system. Results show that our new approach to gain more time in the design steps. But is still limited to fields where expert knowledge is available and the number of input variables is small.

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