Ti6Al4V titanium alloy is considered a biocompatible material, suitable to be used for manufacturing medical devices, particularly cranioplasty plates. Several methods for processing titanium alloys are reported in the literature, each one presenting both advantages and drawbacks. A decision-making method based upon AHP (analytic hierarchy process) was used in this paper for choosing the most recommended manufacturing process among some alternatives. The result of AHP indicated that single-point incremental forming (SPIF) at room temperature could be considered the best approach when manufacturing medical devices. However, Ti6Al4V titanium alloy is known as a low-plasticity material when subjected to plastic deformation at room temperature, so special measures had to be taken. The experimental results of processing parts from Ti6Al4V titanium alloy by means of SPIF and technological aspects are considered.

Mobile robots are increasingly used in industrial applications. There are many constructive solutions for mobile robots using various variants of actuation and control. The proposed work presents a low-cost variant of a mobile robot equipped with Mecanum wheels, which uses brushed DC motors, controlled by the PWM method as the actuation solution. In the first part, a multicriteria analysis based on the AHP method was performed for the selection of the actuation solution. Then, using the software tools Simscape Multibody, Matlab, and Simulink, models were developed that allowed the simulation of the operation of the proposed robot, based both on its kinematics and dynamics. Using these models, both the Mecanum wheel drive version and the differential drive version were studied by means of simulation. The simulations mainly aimed at identifying the way the currents vary through the wheel drive motors, in order to find methods to reduce them. The values obtained by the simulation were later compared with those obtained experimentally, and the corresponding conclusions with regard to the accuracy of the models were drawn.

Robot manufacturing involves continuous path control, which is now available for both robotic controllers and CAM software packages. However, CAM solutions are focused on generating the code for the robotic structure to follow the toolpath, without taking into consideration the dynamics and energy consumption. In this study, robot incremental forming was considered as the manufacturing process, and a simulation model, based upon Matlab-Simulink Simscape Multibody technology, was developed. The proposed model was fed with the trajectory information generated by the CAM program, and using an inverse kinematics function, it was able to generate the commands to drive the robotic structure on the technological toolpaths. The model was also used to study the dynamic behavior of the robot; external experimental data from a 3D force sensor were fed to the model to include the influence of the technological forces which appeared during the incremental forming process. Thus, using the proposed model in conjunction with the external CAM software, the influence of the workpiece position upon the joint torques could be estimated, opening the way for future optimization. The shortcomings of the model, mainly involving inaccurate information with regard to the physical properties of the robotic structure, were addressed by subtracting the dry-run joint torques from those obtained from the technological process.

Manufacturing processes are usually complex ones, involving a significant number of parameters. Unconventional manufacturing processes, such as incremental forming is even more complex, and the establishment of some analytical relationships between parameters is difficult, largely due to the nonlinearities in the process. To overcome this drawback, artificial intelligence techniques were used to build empirical models from experimental data sets acquired from the manufacturing processes. The approach proposed in this work used an adaptive network-based fuzzy inference system to extract the value of technological force on Z-axis, which appears during incremental forming, considering a set of technological parameters (diameter of the tool, feed and incremental step) as inputs. Sets of experimental data were generated and processed by means of the proposed system, to make use of the learning ability of it to extract the empirical values of the technological force from rough data.

Asymmetric single point incremental forming (ASPIF) has been recognized as a solution with potential in manufacturing small batches or single sheet metal parts. The approach presented in this paper presents the development of a knowledge base regarding the values of the technological force within the ASPIF process and the influence of some technological parameters such as feed, speed of the punch, thickness of the part and step, upon them. The method is based on the use of the information provided by the CNC machine sensors. Relationships between the torques developed by the drive motors on each axes and the technological forces will be set in order to refine the raw information displayed on the machine to a usable form. Finally, using an adaptive neuro-fuzzy inference system the dependence between the value of the technological force and the other parameters has been extracted.