When using a unique tool with different controlled path strategies in the absence of a punch and die, the local plastic deformation of a sheet is called Single Point Incremental Forming (SPIF). The lack of available knowledge regarding SPIF parameters and their effects on components has made the industry reluctant to embrace this technology. To make SPIF a significant industrial application and to convince the industry to use this technology, it is important to study mechanical properties and effective parameters prior to and after the forming process. Moreover, in order to produce a SPIF component with sufficient quality without defects, optimal process parameters should be selected. In this context, this paper offers insight into the effects of the forming tool diameter, coolant type, tool speed, and feed rates on the hardness of AA1100 aluminium alloy sheet material. Based on the research parameters, different regression equations were generated to calculate hardness. As opposed to the experimental approach, regression equations enable researchers to estimate hardness values relatively quickly and in a practicable way. The Relative Importance (RI) of SPIF parameters for expected hardness, determined with the partitioning weight method of an Artificial Neural Network (ANN), is also presented in the study. The analysis of the test results showed that hardness noticeably increased when tool speed increased. An increase in feed rate also led to an increase in hardness. In addition, the effects of various greases and coolant oil were studied using the same feed rates; when coolant oil was used, hardness increased, and when grease was applied, hardness decreased.
Many applications are available for the syntactic and semantic verification of NC milling tool paths in simulation environments. However, these solutions – similar to the conventional tool path generation methods – are generally based on geometric considerations, and for that reason they cannot address varying cutting conditions. This paper introduces a new application of a simulation algorithm that is capable of producing all the necessary geometric information about the machining process in question for the purpose of further technological analysis. For performing such an analysis, an image space-based NC simulation algorithm is recommended, since in the case of complex tool paths it is impossible to provide an analytical description of the process of material removal. The information obtained from the simulation can be used not only for simple analyses, but also for optimisation purposes with a view to increasing machining efficiency.
Recently, micro-milling has been one of the most important technologies to produce miniature components, because optional geometrical structures can be machined with a high material removal rate. In terms of conventional dimensions, dynamic milling definitely signals the direction of development in modern technologies: dynamic milling results in higher productivity, better thermal circumstances, and increased tool life. The current paper gives a summary of the possible applications of dynamic milling tool paths in the case of micromachining. The major problems of this technology are the issue of minimum chip thickness and relatively large tool deformation. Different milling strategies, i.e. up milling and down milling, will be compared in detail. A systematic series of experiments were performed in order to generate data for the investigation. A special measuring system was established to perform related data collection. The experiments were carried out on a 5-axis micromachining centre using a tool steel workpiece with a hardness of 50 HRC. Based on the results of the experiments, the force components and the vibrations were also analysed at different radial depths of cut and different feed per tooth values, where productivity was also an important factor. It was found that dynamic milling can be applied in micro sizes, too. It is concluded that in the case of small contact angles, setting as high a feed per tooth value as 23.52 μm is also justified. During the investigation, optimal cutting parameters were also determined within the applied parameter range, these are ae = 34.80%, fz = 8.28 μm, and the use of the down milling strategy.
It is a well-established fact that when equidistant tool paths are used for 2.5D milling operations tool load varies based on the functions of path curvature. This phenomenon makes the optimal selection of cutting parameters more difficult, and the local load peaks are also detrimental to machining stability and tool life. Numerous publications address possible solutions to eliminate the problems caused by varied cutting parameters. The best solution for this is to keep the cutter engagement at a constant value. Nowadays, several methods are available to generate tool paths which are able to maintain constant cutter engagement, but the widespread use of such solutions is significantly hindered, because in this scenario complex calculations are required. This article offers a solution to this problem by presenting a new non-equidistant offsetting method for ensuring a constant cutter engagement angle. The algorithm developed for this purpose is based on simple geometrical equations and allows for its widespread use just like pixel-based methods. Concerning this new solution, computation needs and uniform tool load are verified by simulation and through experiments. The experiments have shown favourable results. Keywords Tool path generation. Cutter engagement. High-speed milling. Cutting force. Non-equidistant offsetting Nomenclature a e Effective radial immersion [mm] a p Axial depth of cut [mm] c(t) Parametric representation of workpiece contour [{mm, mm}] f z Feed per tooth [mm] h ex Maximum chip thickness [mm] i, j Step index [−] n Spindle speed [1/min] p(t) Parametric representation of tool path [{mm, mm}] r tool Tool radius [mm] s Stepover [mm] t Free parameter of curve equations [−] v Feed vector [{mm, mm}] v c Cutting speed [m/min] v f Feed rate [mm/min] w Trochoidal step [mm] z Number of teeth [−] C Entry point of the cutting edge [{mm, mm}] D tool Tool diameter [mm] MRR Material removal rate [mm 3 /min] P Tool path point [{mm, mm}] Q Exit point of cutting edge [{mm, mm}] V(x, y) Vector field [{mm, mm}, {mm, mm}] a, β Angle parameters [ ∘ ] Δs Stepsize[−] θ Cutter engagement angle [ ∘ ]
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