Artificial Intelligence Fuzzy Logic Modeling of Surface Roughness in Plasma Jet Cutting Process of Shipbuilding Aluminium Alloy 5083

Journal Title: Journal of Materials and Engineering - Year 2023, Vol 1, Issue 2

Abstract

In this paper the influence of different process parameters on surface roughness responses in plasma jet cutting process was investigated. Experimentations were conducted on shipbuilding aluminium 5083 sheet thickness 8 mm. Experimental work was performed according to Taguchi L27 orthogonal array by varying four parameters such as gas pressure, cutting speed, arc current and cutting height. Due to complexity of manufacturing process and aim to cover wide experimental space few constraints regarding cutting area were defined. Surface roughness parameters Ra and Rz were analysed as cut quality responses. In order to define mathematical model that will be able to describe effects of process parameters on surface roughness artificial intelligence (AI) fuzzy logic (FL) technique was applied. After functional relations between input parameters and surface roughness responses were defined prediction accuracy of developed fuzzy logic model was checked by comparison between experimental and predicted data. Mean absolute percentage error (MAPE) as well as coefficient of determination (R2) were used as validation measures. Finally, optimal process conditions that lead to minimal surface roughness were defined by creating response surface plots.

Authors and Affiliations

Ivan Peko, Bogdan Nedić, Dejan Marić, Dragan Džunić, Tomislav Šolić, Mario Dragičević, Boris Crnokić, Matej Kljajo

Keywords

Related Articles

Triboinformatic Modeling of Wear in Total Knee Replacement Implants Using Machine Learning Algorithms

Pin-on-disk (PoD) tests, the most prevalent studies, are being carried out in order to evaluate tribological behaviour of different bearing materials. However, the comparison of results obtained from the PoD tests is ver...

Effect of Cross-Sectional Profile of Circular Dimples on Hydrodynamic Lubrication Characteristics of Thrust Bearings

The effects of three types of dimples with different internal structures on the fluid lubrication characteristics of thrust bearings were investigated. The load-carrying capacity and the frictional torque were measured....

Tribological Behavior of Cast Aluminum Matrix Composites After Multiple Remelting

One of the limiting factors for expanding the applications of aluminum alloy castings in many high-tech industries is the insufficient level of tribological properties, especially under conditions of dry and abrasive wea...

The Mystery and Clarity of Leonardo da Vinci's Coefficient of Friction

The science of friction has been using the coefficient of friction as the main quantitative characteristic of the friction process for more than five centuries. The concept of the coefficient of friction as a characteris...

Stress-State and Sliding Between Colliding Plates in the Subduction Zone

Understanding of friction is important for tribological processes ranging from engineering contact systems to the nonmechanical inorganic tribosystems of Earth’s seismo-tectonic zones. A common but little-studied case is...

Download PDF file
  • EP ID EP727412
  • DOI 10.61552/JME.2023.02.005
  • Views 71
  • Downloads 0

How To Cite

Ivan Peko, Bogdan Nedić, Dejan Marić, Dragan Džunić, Tomislav Šolić, Mario Dragičević, Boris Crnokić, Matej Kljajo (2023). Artificial Intelligence Fuzzy Logic Modeling of Surface Roughness in Plasma Jet Cutting Process of Shipbuilding Aluminium Alloy 5083. Journal of Materials and Engineering, 1(2), -. https://europub.co.uk/articles/-A-727412