In case of indoor drone applications three different areas are classified: inventory management, indoor intra-logistics and inspection & surveillance. For each drone movement there is an optimal trajectory where the time for reaching point B from point A can be minimized. This optimal trajectory cannot be executed in case a human is blocking the route. In order to avoid collisions with the drone, either the drone trajectory has to be modified in real-time (which might cost additional time and energy in case the drone delivers a heavy object) or the human operator has to be warned with a pre-defined understandable signal so he/she can modify his/her movement in time. In this paper, the implementation of an Augmented Reality solution (previously tested in an industrial relevant environment on a collaborative robot) using a micro drone is presented.

Today the topic of incremental sheet forming (ISF) is one of the most active areas of sheet metal forming research. ISF can be an essential alternative to conventional sheet forming for prototypes or non-mass products. Single point incremental forming (SPIF) is one of the most innovative and widely used fields in ISF with the potential to form sheet products. The formed components by SPIF lack geometric accuracy, which is one of the obstacles that prevents SPIF from being adopted as a sheet forming process in the industry. Pillow effect and wall displacement are influential contributors to manufacturing defects. Thus, optimal process parameters should be selected to produce a SPIF component with sufficient quality and without defects. In this context, this study presents an insight into the effects of the different materials and shapes of forming tools, tool head diameters, tool corner radiuses, and tool surface roughness (Ra and Rz). The studied factors include the pillow effect and wall diameter of SPIF components of AlMn1Mg1 aluminum alloy blank sheets. In order to produce a well-established study of process parameters, in the scope of this paper different modeling tools were used to predict the outcomes of the process. For that purpose, actual data collected from 108 experimentally formed parts under different process conditions of SPIF were used. Neuron by Neuron (NBN), Gradient Boosting Regression (GBR), CatBoost, and two different structures of Multilayer Perceptron were used and analyzed for studying the effect of parameters on the factors under scrutiny. Different validation metrics were adopted to determine the quality of each model and to predict the impact of the pillow effect and wall diameter. For the calculation of the pillow effect and wall diameter, two equations were developed based on the research parameters. As opposed to the experimental approach, analytical equations help researchers to estimate results values relatively speedily and in a feasible way. Different partitioning weight methods have been used to determine the relative importance (RI) and individual feature importance of SPIF parameters for the expected pillow effect and wall diameter. A close relationship has been identified to exist between the actual and predicted results. For the first time in the field of incremental forming study, through the construction of Catboost models, SHapley Additive exPlanations (SHAP) was used to ascertain the impact of individual parameters on pillow effect and wall diameter predictions. CatBoost was able to predict the wall diameter with R² values between the range of 0.9714 and 0.8947 in the case of the training and testing dataset, and between the range of 0.6062 and 0.6406 when predicting pillow effect. It was discovered that, depending on different validation metrics, the Levenberg–Marquardt training algorithm performed the most effectively in predicting the wall diameter and pillow effect with R² values in the range of 0.9645 and 0.9082 for wall diameter and in the range of 0.7506 and 0.7129 in the case of the pillow effect. NBN has no results worthy of mentioning, and GBR yields good prediction only of the wall diameter.

Incremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

The performance of human–robot collaboration can be improved in some assembly tasks when a robot emulates the effective coordination behaviours observed in human teams. However, this close collaboration could cause collisions, resulting in delays in the initial scheduling. Besides the commonly used acoustic or visual signals, vibrations from a mobile device can be used to communicate the intention of a collaborative robot (cobot). In this paper, the communication time of a virtual reality and depth camera-based system is presented in which vibration signals are used to alert the user of a probable collision with a UR5 cobot. Preliminary tests are carried out on human reaction time and network communication time measurements to achieve an initial picture of the collision predictor system’s performance. Experimental tests are also presented in an assembly task with a three-finger gripper that functions as a flexible assembly device.

Single-Point Incremental Forming (SPIF) is a flexible process for manufacturing sheet metal parts that is well adapted and profitable for prototypes or small batch production. Compared to traditional sheet forming technologies, this relatively slow process can be used in different applications in the automotive and aircraft industries; architecture engineering; and medical aid manufacture. In this article, the indirectly obtained axial forming force on the SPIF of variable wall angle geometry is studied using different process parameters. The estimation of the forces on AlMn1Mg1 sheets with an initial thickness of 0.22 mm is performed by continuous monitoring of servomotor currents. The deformation states of the formed parts were analysed using the ARGUS optical strain measurement system of GOM, while the roughness measurements were carried out by a Mitutoyo system. Some initial finite element analysis simulations and a crack monitoring method together with an interaction plot of forming speed, incremental depth, tool diameter, and lubrication were also reported.

Incremental Sheet Forming is a prosperous process to manufacture sheet metal parts that is well adapted for prototypes or small batch production. Compared to traditional sheet forming technologies this relatively slow process is only profitable for the above mentioned production types but it can be used in different applications in automotive and aircraft industries, in architecture engineering and in medical aids manufacturing. In this paper indirectly obtained axial forming force on Single Point Incremental Forming (SPIF) of variable wall angle geometry were studied under different process parameters. The estimation of the forces on AlMn1Mg1 sheets with 0.22 mm initial thickness is performed by continuous monitoring of servo motor currents. The deformation states of the formed parts were analysed using the ARGUS optical strain measurement system of GOM. Interaction plot of forming speed, incremental depth, tool diameter and lubrication were also reported.

In contrast to one-way assembly of products, simple disassembly and more complex remanufacturing present additional challenges and unknowns on several levels, often requiring human capabilities to be combined with machines—thereby becoming a rewarding deployment field for human–robot collaboration, supported by artificial intelligence, advanced planning and extended reality for improved human–machine interrelations. While the industry has realized little benefit of these—still evolving—areas, learning factories can contribute to closing gaps in skills and mindset of future engineers likely to actively shape the aforementioned fields at the time they begin to notably penetrate industrial production. The paper proposes an approach for building up a portfolio of learning factory resources supporting students in acquiring and independently refining knowledge and practice related to collaborative remanufacturing. The paper presents an incremental approach extending manufacturing knowledge to diagnostics and disassembly in collaborative environments, with an outlook on more comprehensive remanufacturing.