FUZZY ASSESSMENT OF MANUFACTURABILITY DESIGN FOR MACHINING

The article attempts to assess the manufacturability design taking into account the assessment due to the processing, assembly process and organization of production. The evaluation was conducted by the fuzzy inference methods. An assessment was presented for machining based on the proposed fuzzy inference database.


INTRODUCTION TO APPLICATION METHODS FOR FUZZY SETS
Knowledge representation in the fuzzy rule-based system is enhanced by the use of linguistic variables and the language of their values, which are defined by context-dependent fuzzy sets, which determine the importance of a gradual membership function (Zadeh, 1965). On the other hand, fuzzy set inference methods, such as generalized Modus Ponens, generalized Modus Tollens, etc., form the basis for approximate inference (Zadeh, 1975). Therefore, fuzzy logic provides a unique framework for inference computational systems based on rules. This idea suggests the presence of two clearly different concepts in the inference methods of fuzzy sets: knowledge and reasoning. This clear separation of knowledge and reasoning (knowledge base) and processing structure is a key aspect of knowledge-based systems, so from this point of view fuzzy set inference methods can be considered a kind of knowledge-based system. The methods of inference of fuzzy sets in which two input variables (x1 and x2) and a single output variable (s) are involved, for example, sets of terms are related as follows: {small, medium, large}, {short, medium, long} and {bad, medium, good}.
The following base rule consists of five linguistic rules:  R1W IF X1 is small and X2 is short, THEN Y is bad,  R2W IF X1 is small and X2 is medium, then Y is bad,  R3W IF X1 is medium and X2 is short, THEN Y is medium,  R4W IF X1 is large and X2 is medium, THEN Y is medium,  R5W IF X1 is large and X2 is long and Y is good.
The above method of inference can be represented by the decision table shown in Table 1. The Mamdani method (Fernández & Herrera, 2012) processing structure of fuzzy set inference consists of the following five elements - Fig. 1:   Fig. 1  Defuzzification interface that converts fuzzy sets received from the inference process into a clear value,  Output scaling that converts defragmented value from the output domain of fuzzy areas to output variables, creating a global result of the fuzzy set inference method.
Defuzzification (sharpening) is an action to provide the predicted value of a parameter. The center of gravity method consists in determining the value of * , which is the center of gravity of the area under the curve The project reference model is of the type: multiple entriesmultiple outputs (multiple input-multiple output MIMO).

General description of the variables
of the linguistic value tij with an assessment of belonging from 0 to 1.

Procedurelist of variables
The fuzzy method course of action results from project management schemes adopted according to PMIaccording to AIAG (Kuo, Huang & Zhang, 2001). Assessment of machining processability and subsequent assembly process assessment, correspond to the prototyping phase during product design and development (Lalaoui & El Afia, 2018), and the assessment of the production organization technology corresponds to the pilot series and preserver phase during validation and then serial production (Favi, Germani & Mandolini, 2016).
In terms of manufacturability processing variables being indicators evaluating product design for the future feasibility of the machining technology (Deka & Behdad, 2019) and compliance with selected requirements - Fig. 2:  V1 -Technological Capabilities of the Machine Park/Accuracy,  V2 -CAD/CAM Software Capability,  V3 -Machining Capabilities of Available Tools,  V4 -Material meeting the project requirements,  V5 -Energy Consumption,  V6 -Waste Environmental Aspects.

Fig. 2. Procedurelist of variables
In terms of manufacturability assembly (Matuszek & Seneta, 2017) variables being indicators evaluating product design for the feasibility of installation in accordance with the principles assemblability and shortest installation time : In terms of manufacturability organization of production variables being indicators evaluating product design in terms of organizational and technical capabilities, quality and maintenance (Matuszek, Seneta & Moczała, 2018): Sets of Vi variables can be modified and changed depending on the nature of the target process for which we design the product. This gives the fuzzy method a significant advantage in terms of flexibility. In the example presented, the set of variables Vi was prepared for medium-sized plant and small-lot production.

The manufacturability evaluation processing procedure
An example of the assessment of machinability of processing consists of three sub-stages of analysis for which linguistic variables are described in

Machining Manyfacturability Assessmentsub-step 1
The processability of the workpiece (sample housing) is determined, assuming that it depends on two factors, which are: -Technological Capabilities of the Machine Park/Accuracy, -CAD/CAM Software Capability. The course of fuzzification with the Mamdani rule, the basis of inference rules (tab. 6) was carried out for the selected workpiece according to expert assessments:
From rules 10, 11, 14 and 15 -MAX, so we activate the rules: 11. Technology 1 takes the value for Technology Capabilities 20 and Software Capability 55: Tab. 6. Base rules of inference manufacturability evaluation processing -Substage 1  The value of the sample assessment of Machining Manufacturability Sub-step 1 is 29.9.

CONCLUSIONS
Attempt to assess manufacturability assessment takes into account the structure due to the machining, assembly process and organization of production. The assessment was carried out according to the fuzzy set inference methods. The manufacturability evaluation procedure is proposed in the steps, which start their ratings linguistic variables divided into sub-steps. An example of the evaluation due to machining on the basis of the proposed base of fuzzy inference can be extended for the assembly process steps and organization of production.