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Study of mixing and segregation of granular materials was performed in a Bohle bin blender using both computational modeling and experiments. A multicomponent mixture of ...pharmaceutical excipients and coated theophylline granules, an active pharmaceutical ingredient (API) was considered as the blend formulation. A DEM (Discrete Element Method) Model was developed to simulate the flow and mixing of the multicomponent blend to compare with the experimental data. DEM is a numerical modeling technique which incorporates all the material properties (such as Particle size, density, elastic modulus, yield strength, Poisson’s ratio, work function etc.)to simulate granular flow (such as mixing, conveying) of particles. In simulation, the degree (Relative standard deviation) of mixing in a Bohle bin blender was assessed as a function of critical processing parameters (loading pattern, rotational rate, and fill percentage). Numerical simulation results reveal radial mixing in a Bohle bin blender is faster than axial mixing due to symmetric geometry limitation. This study investigates a numerical model-based approach to study the effect of the critical process parameters on the mixing dynamics in Bohle bin blender for a moderately cohesive pharmaceutical formulation. The DEM model can be used to provide crucial insights to developed optimized mixing protocols to ascertain the best mixing conditions for different formulation. As for example, as we try to develop a mixing protocol for another formulation with different operational parameters such as loading pattern, rotational speed, and fill percentage, one can device an optimized mixing protocol of the formulation with the help of this DEM model.
Landing on small-bodies is a very challenging problem that requires high degrees of robustness and autonomy. Being able to perform simulations with great flexibility and accuracy is paramount for the ...development and design of landing systems. To this end, contact dynamics plays a fundamental role and is often handled by complex tools that require large amount of development and validation efforts and very specific expertise. In the last decade, the Visual Effects (VFX) industry has developed numerous suites that deal with contact dynamics frameworks. In this work, the possibility of leveraging on the work of the VFX industry by using Blender, one of these tools, as the source for the contact dynamics modelling is investigated.
This research focuses on the description of the methodology used for the landing simulations and the validation of the tool developed. A step-by-step guide through the simulation setup is given, discussing how the wrapping GNC simulator and Blender interact. Validation tests for the different parameters and dynamic models involved in the simulations are also presented. The results refer to the landing of a CubeSat in the crater region of an asteroid. In particular, the artificial crater that will be generated on Dimorphos by NASA’s DART impact in late September 2022, is considered in the simulations presented in this work. Safety maps are generated by post-processing these results, and are used to assess different landing strategies or site-selection criteria on the Dimorphos crater study case. Finally, the role of the developed tool in optimising the use of space resources and its contribution to landing design strategies is discussed.
Known limitations of tumbling blenders (weak diffusive axial mixing and segregation of free flowing granules) have provided the motivation to investigate the flow and mixing of granules inside a ...tetrapodal blender. This blender can be thought of as two V-shaped pairs of arms connected and twisted at their bottom ends. In this work, more than 100 experiments were carried out under a wide set of operating conditions and geometrical configurations. Compared to the conventional V-blender, this geometry is shown to provide shorter mixing times and better axial and radial mixing efficiency, especially when its upper or lower V-shaped part is twisted by 45° with respect to the rotation axis. Segregation of granules with different sizes and densities was investigated for varying rotational speeds (5–30RPM) and fill levels (35–65%V). It is observed that the segregation intensity is far less important in the tetrapodal blender than in the V-blender, and that it decreases significantly with an increase in rotational speed, the effect of the fill level being insignificant. It is also shown that kinetic sieving is the main governing mechanism for the segregation of granules. Finally, a criterion is proposed for the scale-up of the tetrapodal blender and the V-blender so that they may operate efficiently, without pronounced segregation.
•The performance of the tetrapodal blender is compared to that of the V-blender.•The mixing mechanisms are more efficient in the tetrapodal blender than the V-blender.•The tetrapodal blender is less prone to granular segregation than the V-blender.•A criterion is provided for the scale-up of tumbling blenders.
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•The performance of the tetrapodal blender is compared to that of the V-blender.•The mixing time in the tetrapodal blender is shorter than that in the V-blender.•The mixing mechanisms ...are more efficient in the tetrapodal blender than the V-blender.•The tetrapodal blender is less prone to granular segregation than the V-blender.
One aspect that must be addressed when designing tumbling blenders is poor axial mixing, which can lead to non-homogeneous mixtures, especially when the particle physical and flow properties are different. To overcome these limitations, we recently undertook an interest in a tetrapodal mixing device patented in 1964. It can be described as two V-shaped pairs of arms connected at their bottoms, one of which is twisted by 90°. In this work, particle mixing and segregation are investigated using the discrete element method in both the V-blender and this tetrapodal blender. Results of mixing time and uniformity are compared for different loading profiles, fill levels and rotational speeds. Compared to the V-blender, this geometry is shown to provide better axial and radial mixing efficiency. Good behavior was also observed for size segregating granules, yet more investigation would be needed for worse cases involving granules with large size ratios and different densities.
To enable automatic disassembly of different product types with uncertain condition and degree of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements ...are needed. Machine learning algorithms can be employed due to their generalization capabilities of learning from various types and variants of products. However, in reality, datasets with a diversity of samples that can be used to train models are difficult to obtain in the initial period. This may cause bad performances when the system tries to adapt to new unseen input data in the future. In order to generate large datasets for different learning purposes, in our project, we present a Blender add-on named MotorFactory to generate customized mesh models of various motor instances. MotorFactory allows to create mesh models which, complemented with additional add-ons, can be further used to create synthetic RGB images, depth images, normal images, segmentation ground truth masks and 3D point cloud datasets with point-wise semantic labels. The created synthetic datasets may be used for various tasks including motor type classification, object detection for decentralized material transfer tasks, part segmentation for disassembly and handling tasks, or even reinforcement learning-based robotics control or view-planning.
DEM study on identification of mixing mechanisms in a pot blender Tsunazawa, Yuki; Soma, Nobukazu; Sakai, Mikio
Advanced powder technology : the international journal of the Society of Powder Technology, Japan,
January 2022, 2022-01-00, Volume:
33, Issue:
1
Journal Article
Peer reviewed
Open access
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•A pot blender is a typical drum mixer that can rotate and swing simultaneously.•The DEM is employed to clarify the mixing process in the pot blender.•The mixing mechanism of the pot ...blender is shown to be convective and shear mixing.•The particle density does not affect the mixing performance of the pot blender.•The mixing performance of the pot blender depends on the filling ratio of particles.
A pot blender with both blending and storage capabilities offers an advantage over a conventional rotating drum. However, the mixing mechanism of the pot blender is extremely complicated because the pot blender rotates and swings simultaneously. Owing to the lack of systematic investigations, the mixing mechanism of the pot blender has not been fully elucidated. In this study, we clarify the mixing mechanism of the pot blender by using the discrete element method. Simulation results reveal that the main mixing mechanism is convective mixing in the rotational direction and shear mixing in the axial direction. Moreover, the mixing performance is unaffected by particle density, whereas the velocity gradient in the axial direction, which mainly determines the axial mixing performance, is affected by the particle filling ratio. Considering the relationship between the variance of axial particle velocity and granular temperature, the filling ratio is shown to significantly influence the mixing efficiency in the pot blender. In addition, the dependency of shear and diffusive mixing on Lacey’s mixing index in the pot blender is newly clarified. Consequently, this study demonstrates essential insights into the mixing mechanism of the pot blender and the pot blender as an effective industrial mixer.
Surgical robots rely on robust and efficient computer vision algorithms to be able to intervene in real-time. The main problem, however, is that the training or testing of such algorithms, especially ...when using deep learning techniques, requires large endoscopic datasets which are challenging to obtain, since they require expensive hardware, ethical approvals, patient consent and access to hospitals. This paper presents VisionBlender, a solution to efficiently generate large and accurate endoscopic datasets for validating surgical vision algorithms. VisionBlender is a synthetic dataset generator that adds a user interface to Blender, allowing users to generate realistic video sequences with ground truth maps of depth, disparity, segmentation masks, surface normals, optical flow, object pose, and camera parameters. VisionBlender was built with special focus on robotic surgery, and examples of endoscopic data that can be generated using this tool are presented. Possible applications are also discussed, and here we present one of those applications where the generated data has been used to train and evaluate state-of-the-art 3D reconstruction algorithms. Being able to generate realistic endoscopic datasets efficiently, VisionBlender promises an exciting step forward in robotic surgery.
This study presents the design of a 2-pole high-speed BLDC motor for kitchen blenders and its control method with the aim to reduce the speed ripple that decreases the grinding performance in the ...high-speed region. In blender machines, the high pulsating load caused by the blending materials such as ice cubes acting on the grinding blade, create significant speed ripple in the constant power region. Because current and torque are limited to protect the drive, the actual motor torque cannot overcome the pulsated load if the speed reference is maintained. Therefore, the speed fluctuates, and the grinding performance is lowered. In the proposed variable speed reference control scheme, the speed command is continuously modified to overcome the maximum pulsating torque requirement which results in lower speed ripple. To achieve this, if the load torque suddenly increases, the current value is then set based on the maximum load and speed reference value is temporarily lowered. If then the load torque becomes lower than the actual torque, the previously set speed reference value is reused. The design result and grinding performance of the proposed control method are verified by simulations and experiments. Hard white beans are used for actual performance testing.
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