The aim of the present work was to investigate the residual stresses distribution within parts processed by single point incremental forming (SPIF) as a function of the working parameters and to determine its influence on the parts accuracy. The study was performed for a double frustum of pyramid made by 304 stainless steel. An experimental analysis was first conducted by using the strain-rosette method. Since the method allowed determining the residual stresses only locally, a numerical model was then elaborated to inspect the distribution of residual stresses throughout the whole part. It was found that a favorable state of residual stresses and implicitly a good accuracy of parts can be obtained when small values of tool diameter and tool vertical steps, respectively high values of feed rate and spindle speed, are used.

This paper presents an experimental study carried out in order to assess the effects of the cutting parameters (cutting speed, feed and depth of cut) and also of cooling conditions on the cutting force and the surface quality during high speed face milling of a magnesium alloy. With its high ratio of strength to weight, good machinability and overall physico – mechanical properties magnesium has attracted a large interest upon itself, especially in fields such as automotive, aeronautics, electronics or biomaterials. On the other hand, there has been lately an increased interest for cooling techniques that are more environmentally friendly, which is why in this study two cooling systems were used: dry machining and minimum quantity lubrication (MQL). The tests have been organised according to design of experiment technique. By using the response surface methodology (RSM), a central composite experimental matrix was designed. Statistical analysis (ANOVA) has allowed to create mathematical models of the cutting force and surface roughness and to analyse the effects of the effects of the input parameters on these outcomes. It was thus revealed that the depth of cut and the feed, and the interactions feed – depth of cut and feed direction – cooling, respectively, have the most significant influence on the main cutting force, whereas when it comes to the surface roughness, the most significant factors were the feed, the feed direction and the interaction between feed direction and cooling type.

Abstract. The aim of the current paper is to determine the influence of material thickness on the quality aspects of AWJ processing of the AL6061T651 alloy. The analysis is performed under different working conditions. The parameters taken into account to quantify the quality of processing are those defined in the ISO/WD/TC 44 N 1770 standard: width of the processed surface at the jet inlet (Li), width of the processed surface at the jet outlet (Lo), deviation from perpendicularity (u), inclination angle (Į) and roughness (Ra). The obtained results emphasise a significant influence of material thickness on both, the surface quality and process productivity.

The harmful effects on the environment caused by the overexploitation of fossil resources and their industrial processing have attracted the attention of researchers worldwide. Their interest is not only in the use of biodegradable materials based on renewable natural resources but also in the use of manufacturing and processing technologies that minimize as much as possible the negative footprint left on the natural ecosystem. In the present paper, the thermoplastic material processed using abrasive water jet cutting technology (AWJ) is Arboblend V2 Nature. Thus, both the processed material and the technology closely follow the interests of the researchers. The study aims to obtain surfaces with higher quality than other synthetic polymers by appropriate variation of process parameters (abrasive material flow, traverse speed and pressure). Two methods for determining the roughness were used, one in accordance with the standard and one adopted by the authors in order to render more accurately the surface roughness value. Other output parameters observed were: the amount of material removed, the width of the machined surface, the deviation from the perpendicularity and the inclination angle of the machined surface. The output parameters result confirms the possibility of using the parts obtained by AWJ in engineering applications and not only because the shape deviations fall within the limitations imposed at the industrial level.

The main context in which abrasive water cutting is used is the reduction of thermal deformation induced by thermal (plasma arc PAC, oxyfuel OFC, laser) of electrothermal (electroerosion EDM) cutting methods. Although it is not the cheapest or time-efficient technique it can be used on a wide variety of metallic and non-metallic materials. Among other benefits are the lack of burrs, high precision and improved surface finish, low setup time and stress-free cutting. This leads to no secondary processing required in many other applications. Depending on the material hardness the cutting thickness can reach up to 300 [mm]. The present study proposes an analysis of high-pressure abrasive water jet cutting of a 19 [mm] thick plate. The aluminium alloy used in this study was Al-6061-T651. This alloy is being used especially in the aeronautics industry due to is excellent welding properties. The experiments were conducted using multiple input and output factors. The design of experiments (DOE) takes into account input factors and offers models for responses. The study was organised according to response surface methodology, with an I-optimal design type and a quadratic design model. The input factors were: cutting pressure, standoff distance, programmed quality of the cut. The responses analysed were: entrance (Iw) and exit (Ow) width of cut, and taper angle (α). An ANOVA analysis was performed for each response. This interpretation implies the significance (p-value) that the input factors have on the variation of the responses. For Iw and Ow a reduced 2FI model was proposed, while for θ a linear model was suggested. The p-value obtained for each response is smaller than 0.0001, which classifies the models as significant. The ANOVA fit statistics determine the R-squared error between 0.964 and 0.995, meaning that the responses are well defined by the input value variations. This high confidence in the results leads to accurate mathematical models.