Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches

Ti-6Al-2Sn-4Zr-6Mo is one of the most important titanium alloys characterised by its high strength, fatigue, and toughness properties, making it a popular material for aerospace and biomedical applications. However, no studies have been reported on processing this alloy using laser powder bed fusion. In this paper, a deep learning neural network (DLNN) was introduced to rationalise and predict the densification and hardness due to Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo alloy. The process optimisation results showed that near-full densification is achieved in Ti-6Al-2Sn-4Zr-6Mo alloy samples fabricated using an energy density of 77–113 J/mm3. Furthermore, the hardness of the builds was found to increase with increasing the laser energy density. Porosity and the hardness measurements were found to be sensitive to the island size, especially at high-energy-density. Hot isostatic pressing (HIP) was able to eliminate the porosity, increase the hardness, and achieve the desirable α and β phases. The developed model was validated and used to produce process maps. The trained deep learning neural network model showed the highest accuracy with a mean percentage error of 3% and 0.2% for the porosity and hardness. The results showed that deep learning neural networks could be an efficient tool for predicting materials properties using small data

A novel vision-based multi-functional sensor for normality and position measurements in precise robotic manufacturing

Cobots play an essential role in the fourth industrial revolution and the automation of complex manufacturing processes. However, cobots still face challenges in achieving high precision, which obstructs their usage in precise applications such as the aerospace industry. Nonetheless, advances in perception systems unlock new cobot manufacturing capabilities. This paper presents a novel multi-functional sensor that combines visual and tactile feedback using a single optical sensor, featuring a moving gate mechanism. This work also marks the first integration of Vision-Based Tactile Sensing (VBTS) into a robotic machining end-effector. The sensor provides vision-based tactile perception capabilities for precise normality control and exteroceptive perception for robot localization and positioning. Its performance is experimentally demonstrated in a precise robotic deburring application, where the sensor achieves the high-precision requirements of the aerospace industry with a mean normality error of 0.13° and a mean positioning error of 0.2 mm. These results open a new paradigm for using vision-based sensing for precise robotic manufacturing, which surpasses conventional approaches in terms of precision, weight, size, and cost-effectiveness

Elastomer-based visuotactile sensor for normality of robotic manufacturing systems

Modern aircrafts require the assembly of thousands of components with high accuracy and reliability. The normality of drilled holes is a critical geometrical tolerance that is required to be achieved in order to realize an efficient assembly process. Failure to achieve the required tolerance leads to structures prone to fatigue problems and assembly errors. Elastomer-based tactile sensors have been used to support robots in acquiring useful physical interaction information with the environments. However, current tactile sensors have not yet been developed to support robotic machining in achieving the tight tolerances of aerospace structures. In this paper, a novel elastomer-based tactile sensor was developed for cobot machining. Three commercial silicon-based elastomer materials were characterised using mechanical testing in order to select a material with the best deformability. A Finite element model was developed to simulate the deformation of the tactile sensor upon interacting with surfaces with different normalities. Additive manufacturing was employed to fabricate the tactile sensor mould, which was chemically etched to improve the surface quality. The tactile sensor was obtained by directly casting and curing the optimum elastomer material onto the additively manufactured mould. A machine learning approach was used to train the simulated and experimental data obtained from the sensor. The capability of the developed vision tactile sensor was evaluated using real-world experiments with various inclination angles, and achieved a mean perpendicularity tolerance of 0.34°. The developed sensor opens a new perspective on low-cost precision cobot machining

Modelling and control of an unmanned excavator vehicle

This paper investigates the modelling and control of a full-scale excavator vehicle. A detailed analytical model for an unmanned excavator vehicle is developed. The model takes into account the kinematics and dynamics of the mobile platform (vehicle) and the excavation arm (links and hydraulic system). The model describes the dynamic relationship between the operator input commands (fuelling and joystick commands to excavation arm and steering lever) and the trajectories and forces of the excavator vehicle. The dynamic model of the excavation arm system is validated against measured data. The validation of the model is conducted in collaboration with QinetiQ Limited (the new science and technology company formed from the major part of DERA, the British Government's defence research and development organization). A unified model is important for design of control strategies, since in order to move the bucket of a mobile excavator, movements of the entire vehicle are required. A key requirement for automating the excavation task is automated trajectory tracking, and a proportional-integral-derivative (PID) controller for trajectory tracking is developed and tested. It is noted that even though the results presented in this paper are focused on a particular excavator, the research is generic and can be adapted to any tracked ground vehicle with an on-board closed-chain manipulator

A three-term backpropagation algorithm

Efficient learning by the backpropagation (BP) algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. This paper analyzes the convergence of the new three-term backpropagation algorithm. If the learning parameters of the three-term BP algorithm satisfy the conditions given in this paper, then it is guaranteed that the system is stable and will converge to a local minimum. It is proved that if at least one of the eigenvalues of matrix F (compose of the Hessian of the cost function and the system Jacobian of the error vector at each iteration) is negative, then the system becomes unstable. Also the paper shows that all the local minima of the three-term BP algorithm cost function are stable. The relationship between the learning parameters are established in this paper such that the stability conditions are met