The plasma jet cutting process has a high potential for the machining of aluminium and its alloys. Aluminium is well known as a highly thermally conductive and sensitive material, and because of that there exist uncertainties in defining process parameters values that lead to the best possible cut quality characteristics. Due to that, comprehensive analysis of process responses as well as defining optimal cutting conditions is necessary. In this study, the effects of three main process parameters—cutting speed, arc current, and cutting height—on the cut quality responses: top kerf width, bevel angle, surface roughness Ra, Rz, and material removal rate were analyzed. Experimentations were conducted on aluminium EN AW-5083. In order to model relations between input parameters and process responses and to conduct their optimization, a novel hybrid approach of response surface methodology (RSM) combined with desirability analysis was presented. Prediction accuracy of developed responses regression models was proved by comparison between experimental and predicted data. Significance of process parameters and their interactions was checked by analysis of variance (ANOVA). Desirability analysis was found as an effective way to conduct multi-response optimization and to define optimal cutting area. Due to its simplicity, the novel presented approach was demonstrated as a useful tool to predict and optimize cut quality responses in plasma jet cutting process of aluminium alloy.

Additive manufacturing is a technology of making a three-dimesional solid object of any shape from a digital model. Today on the global market exist various additive manufacturing processes. All of these processes build parts by applying...

In this paper Artificial Neural Network (ANN) model was developed for prediction of kerf width in plasma jet metal cutting process. Process parameters whose influence was analyzed are cutting height, cutting speed and arc current. An L18...

The development of corrosion mechanisms is a major issue that industrial experts are faced with while dealing with all phases of metal exploitation. Damage caused by corrosion affects the increase in both direct and indirect costs of manufacture. With adequate surface protection technology, it is possible to lessen the intensity of corrosion mechanisms’ development. In order for the selected surface protection technology to provide its full protective action, it is necessary to be familiar with its process parameters. This paper deals with a surface protection system that creates a barrier between the aggressive environment and the base material. The tested barrier was created on a base material surface by electrostatic powder spraying. In this experiment, three types of base material (carbon steel, aluminum and galvanized steel) were coated by applying different combinations of operating parameters. Operating parameters, i.e., the voltage and strength of the electric current, were taken as numerical input variables in the experiment. Each experiment design was focused on measuring the thickness of the protective layer. Analysis of the obtained results and statistical data processing confirmed that appropriate parameters (input variables) provided the target thickness of the protective coating on each of the tested materials. Optimization of parameters contributes to the efficiency of production, shortens the processing time and ensures that coating thickness stays within the limits defined either by the manufacturer or the end user.

This paper deals with the austempering of ductile iron (ADI) and clarifies the influential austempering parameters during the production of ADI. During the austempering process, the heat treatment parameters can be varied, thus influencing the final microstructure and, of course, the mechanical properties of ADI. To appropriately conduct experiments and obtain good results, an experimental plan was developed using the Design Expert 13 software. Along with the heat treatment parameters, the influence of the copper content on the ADI toughness, tensile strength, and elongation was determined. The obtained results from this experiment were used to develop unique mathematical models which describe the influences of heat treatment and copper content on the observed mechanical properties of ADI samples. These mathematical models can be applied to predict the analysed mechanical properties of ADI in the dependence of heat treatment parameters and copper content in base ductile iron. For the multi response optimisation of toughness, tensile strength, and elongation, a hybrid grey-fuzzy technique was presented as a significant contribution to the enhancement of the analysed mechanical properties. Consequently, the copper content and heat treatment parameter levels that resulted in the maximal mechanical properties’ functions were defined.

This study investigates the influence of layer height, infill density, and the number of perimeters on the FDM 3D printing performance of PLA, a biodegradable and renewable biopolymer. The primary objective is to identify parameter settings that simultaneously maximize impact strength and production efficiency, quantified through filament usage and printing time. In addition, 3D surface profilometry was employed as a non-destructive characterization method to evaluate surface roughness, assess its dependence on process parameters, and establish correlations with destructive impact strength testing. Experimental work was conducted using a Taguchi L9 orthogonal array, and regression-based mathematical models were developed to quantify the effects of individual parameters on the analysed responses. Finally, Grey Relational Analysis (GRA) was applied to perform multi-objective optimization and determine parameter combinations that jointly enhance mechanical durability, surface quality, and production efficiency. The results provide a clear set of manufacturing parameter settings that satisfy both destructive and non-destructive performance criteria while ensuring resource-efficient production.