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.

In recent years, trochoidal milling has become greatly widespread since it provides high productivity and long tool life, especially when cutting difficult-to-machine materials. Numerous studies have dealt with the analytical description and the experimental investigation of the evolution of cutting force, temperature, tool wear, chatter stability, etc. However, most of these analyses are focusing on the conventional circular and cycloid-shape trajectories. Only a few studies address the potential of improving the efficiency by modifying the tool path shape, leaving significant reserves untapped. If the mathematical formulation of the trajectory cannot be described easily, then a sophisticated optimisation algorithm is needed to determine the appropriate tool path shape. To overcome these difficulties at trochoidal milling of straight slots, the present work proposes a new tool path generation algorithm that can maximize the average material removal rate (MRR) while controlling the tool load. In this approach, the tool path is modelled by a B-spline curve whose control points are optimised using a differential evolution algorithm. The developed tool path generation strategy was compared through simulations and experiments with two widely used solutions viz the traditional cycloid-shaped tool path and a modern approach used in computer-aided manufacturing (CAM) systems. The results reveal that the developed method is able to control the tool load and the smoothness of the tool path, and most importantly, also exceeds the existing methods in terms of productivity.

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.

In CNC milling, the feed rate scheduling is a frequently used method to increase machining quality and efficiency. Among the benefits of feed rate scheduling, this paper focuses on controlling the tool load and optimizing the machining time. Although the advantages of feed rate scheduling are undeniable, some areas remain still to be addressed. In order to control the tool load, geometric methods are often used, which are based on keeping a specific parameter, such as chip thickness or material removal rate (MRR) constant. However, a high level of tool load control can only be provided if cutting force models or experimental-based techniques are used. Besides traditional methods, this paper presents an artificial neural network (ANN)-based feed rate scheduling method to keep the tool load constant, using data gained by preliminary cutting experiments. A case study demonstrates that a significantly higher level of tool load control can be achieved with this method as compared to the geometric models. Besides controlling the tool load, the present feed rate scheduling method also addresses the consideration of acceleration limits which is of great importance for practical uses. The application of feed rate scheduling in trochoidal milling is also discussed in detail in this paper. This area has not received enough attention, as due to the limited fluctuation of cutter engagement, the tool load was considered as well-controlled. However, experiments have shown that in the case of trochoidal milling, the introduction of feed rate scheduling can still further increase the machining efficiency. Using the developed ANN-based feed rate scheduling method, significant progress could be made as compared to conventional technologies in controlling the cutting force and optimizing the machining time. In the present case study, a reduction of 50% in machining time was achievable by adjusting the feed rate without increasing the peak value of cutting force.

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.