Reducing of process model uncertainty towards smart machining systems

Neven Trajchevski,Piotr Cichosz,Mite Tomov,Mikolaj Kuzinovski

doi:10.17814/mechanik.2018.10.150

Abstract

This paper presents an approach of empirical modeling of cutting process physical phenomena with measurement uncertainty parameters accompanied to the model exponents/ /coefficients. The approach is presented trough an example of creating a power mathematical model for average cutting temperature in turning with details about the uncertainty contributions from different experimental plans. The approach is proposed to be implemented as usual practice during empirical modeling, in order the resulting models to fit with the needs of the smart machining systems and the needs of interoperability between researchers.

Highlights

W pracy przedstawiono propozycję modelowania empirycznego zjawisk fizycznych w skrawaniu z uwzględnieniem parametrów niepewności pomiarowej oraz modelowych współczynników
Even if we compare them with other authors’ results and by applying the best measurement practices, we can agree that the value of the relative expanded uncertainty is less than 10%, in our experiments for the cutting force measurement it was 8%, and for the average cutting temperature 2%
The experiment was performed under the following conditions: the workpiece material carbon steel, EN C55; cutting tool holder Kennametal IK.KSZNR-064 25×25; cutting insert Hertel SNGN 120704 mixed ceramics MC2(Al2O3 + TiC); cutting tool geometry κr = 85o, κr1 = 5o, γ0 = −6o, α0 = 6o, λS = −6o, where the mathematical model is showed calculated response surface compared to the confidence by (1), and where Θ is the average cutting temperature, v interval that can be presented within the design of experiment methodology (DOE) methodis the cutting speed, f is the feed rate, ap is the depth of cut and rε is the cutting tool nose radius, ci are the mathematical model coefficients, while the cutting ology or while analyzing the measurement uncertainty of single measurement

Summary

NEVEN TRAJCHEVSKI MIKOLAJ KUZINOVSKI MITE TOMOV PIOTR CICHOSZ *

Zmniejszanie niepewności modelu procesu w inteligentnych systemach obróbki skrawaniem. The approach is proposed to be implemented as usual practice during empirical modeling, in order the resulting models to fit with the needs of the smart machining systems and the needs of interoperability between researchers. Systems and process monitoring and control modules brought to us the possibilities of faster machining with increased accuracy and precision This is the result of implementing a number of new hardware and software tools from various manufacturers. If we compare it to the advances in other areas in the industries, we can face significant drawback as a result of the lack of standards in the area of interoperability of the vast number of modules, controllers, and software tools This means that the advances achieved within some elements or by some manufacturers cannot be used widely due to the closed hardware and software components, as well as the copyrights. As defined in [2], SMS is a machine that knows its capabilities to come up with the most efficient way of producing a correct

Empirical modeling and uncertainty towards SMS

Experimental research

As a result of this work we can highlight that presenting

Findings

Relative expanded uncertainty