Design Optimization of Injection Molds with Conformal Cooling for Additive Manufacturing

poster abstractAbstract This is a framework for optimizing additive manufacturing of plastic injection molds. The proposed system consists of three modules, namely process and material modeling, multi-scale topology optimization, and experimental testing, calibration and validation. Advanced numerical simulation is implemented for a typical die with conformal cooling channels to predict cycle time, part quality and tooling life. A thermo-mechanical topology optimization algorithm is being developed to minimize the die weight and enhance its thermal performance. The technique is implemented for simple shapes for validation before it is applied to dies with conformal cooling in future work. A method for designing a die with porous material which can be produced in additive manufacturing is developed. Also a comprehensive set of systemic design rules are developed and to be integrated with CAD modeling to automate the process of obtaining viable plastic injection dies with conformal cooling channels. Finally, material modeling using simulation as well as design of experiments is underway for obtaining the material properties and their variations

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations

Fear of falling in obese women under 50 years of age: a cross-sectional study with exploration of the relationship with physical activity

An understanding of capacity for physical activity in obese populations should help guide interventions to promote physical activity. Fear of falling is a phenomenon reported in the elderly, which is associated with reduced mobility and lower physical activity levels. However, although falls are reportedly common in obese adults, fear of falling and its relationship with activity has not been investigated in younger obese populations. In a cross-sectional study, fear of falling was measured in 63 women aged 18 to 49 years, with mean BMI 42.1 kg/m (SD 10.3) using the Modified Falls Efficacy (MFES), the Consequences of Falling (COF) and the Modified Survey of Activities and Fear of Falling in the Elderly (MSAFFE) scales. The choice of scales was informed by prior qualitative interviews with obese younger women. Physical activity levels were measured at the same time using the International Physical Activity Questionnaire. The mean score for fear of falling scales, with 95% confidence intervals, were estimated. Chi-square tests and t-tests were used to explore differences in age, body mass index and fear of falling scores between fallers and non-fallers. For each fear of falling scale, binomial logistic regression was used to explore its relationship with physical activity. Mean scores suggested high levels of fear of falling: MFES [mean 7.7 (SD 2.7); median 8.5]; COF [mean 31.3 (SD 9.4)]; MSAFFE [mean 25.9 (SD 8.7); median 23]. Scores were significantly worse in fallers (  = 42) compared to non-fallers (  = 21). MFES and MSAFFE were independently associated with lower levels of physical activity [odds ratio = 0.65, 95% Cl 0.44 to 0.96 and odds ratio = 1.14, 95% CI 1.01 to 1.28 respectively], when adjusted for age, BMI and depression. This study confirms that fear of falling is present in obese women under 50 years of age. It suggests that it is associated with low levels of physical activity. These novel findings warrant further research to understand capacity for physical and incidental activity in obese adults in both genders and suggest innovative interventions to promote lifestyle changes and/or consideration of falls prevention in this population

A study on clinical, laborotory and management profile in patients with liver abscess.

Liver abscess is a commom surgical problem in our country. Our study aims to review the literature on the management of liver abscess,focusing on the choice of drainage.A case series of our experience with clinical pathological correlation is presented to highlight the indication and outcome of each modality of drainage. Chronic Alcohol intake is a definitive risk factor for development of liver abscess. Diabetes Mellitus is the prevalent comorbid factor, seen especially in the elderly. Other comorbid factors include hypertension, bronchial asthma etc. None of these seemed to have a significant correlation with the disease process. Pig tail drainage is preferred in patients with single abscess of size less than 5 cm situated in superficial segments. Laparotomy and drainage was done in patients with ruptured or impeding rupture abscess. Conservative management with antibiotics was also useful in very small single cavity abscess. The incidence of complications was predominantly seen in patients with ruptured liver abscess who underwent laparotomy, in form of sepsis followed by MODS and death. Residual abscess cavity was seen in small number of patients following pig tail drainage but it was not Significant

Analysis of BCB and SU 8 photonic waveguide in MZI architecture for point-of-care devices

The proposed refractive index-based biosensor made of cost-effective polymer material in Mach-Zehnder configuration with novel horizontal slot waveguide structure offers high sensitivity. Due to the recent demand for low-cost point-of-care biosensor, silicon wafer-based polymer material has the potential for developing new hybrid waveguides for sensors. Hence we designed BenzoCycloButene (BCB), SU8 material as the core, and polymethyl methacrylate (PMMA) clad on a silicon wafer. The novelty of proposed BCB and SU8 horizontal slot waveguide structure of 2.5 μ m wide and 1.8μ m thick with a slot height of 400 ​nm, which handles the analyte effectively without any external sample holder. In Mach-Zehnder Interferometer architecture, reference arm 1 ​cm and sensing arm at a length of 1.1 ​cm with analyte refractive index of cancer cell (RI ​= ​1.401) and Influenza A type virus (RI ​= ​1.48) are used. Effective mode index of BCB core and SU8 are investigated through Finite-difference time domain (FDTD) analysis, and sensitivity results are calculated. Detection of disease are plotted in the transmission spectrum with reference to normal human serum (RI ​= ​1.35). This horizontal slot waveguide of BCB core achieves a sensitivity of 19280 nm/RIU and SU8 horizontal slot core waveguide provides a sensitivity of 16500 nm/RIU, which is higher in this polymer based refractive index biosensing

Mobile Service Robot Path Planning Using Deep Reinforcement Learning

A mobile service robot operates in a constantly changing environment with other robots and humans. The service environment is usually vast and unknown, and the robot is expected to operate continuously for a long period. The environment can be dynamic, leading to the generation of new routes or the permanent blocking of old routes. The traditional path planner that relies on static maps will not suffice for a dynamic environment. This work is focused on developing a reinforcement learning-based path planner for a dynamic environment. The proposed system uses the deep Q-Learning algorithm to learn the initial paths using a topological map of the environment. In an environmental change, the proposed $\pmb \beta$ -decay transfer learning algorithm trains the agent in the new environment. This algorithm uses experience vs. exploration vs. exploitation-based training depending on the similarity of the old and new environments. The system is implemented on the Robotic Operating System framework and tested using Turtlebot3 mobile robot in the Gazebo simulator. The experimental results show that the reinforcement learning system learns all the routes based on the initial topological map of different service environments with an accuracy of over 98%. A comparative analysis of the $\pmb \beta$ -decay transfer learning and non-transfer learning agents is performed based on various evaluation metrics. The transfer learning agent converges twice faster than the non-transfer learning agent

A scalable tree based path planning for a service robot

Path planning plays a vital role in a mobile robot navi‐ gation system. It essentially generates the shortest tra‐ versable path between two given points. There are many path planning algorithms that have been proposed by re‐ searchers all over the world; however, there is very little work focussing on path planning for a service environ‐ ment. The general assumption is that either the environ‐ ment is fully known or unknown. Both cases would not be suitable for a service environment. A fully known en‐ vironment will restrict further expansion in terms of the number of navigation points and an unknown environ‐ ment would give an inefficient path. Unlike other envi‐ ronments, service environments have certain factors to be considered, like user‐friendliness, repeatability, sca‐ lability, and portability, which are very essential for a service robot. In this paper, a simple, efficient, robust, and environment‐independent path planning algorithm for an indoor mobile service robot is presented. Initially, the robot is trained to navigate to all the possible desti‐ nations sequentially with a minimal user interface, which will ensure that the robot knows partial paths in the en‐ vironment. With the trained data, the path planning al‐ gorithm maps all the logical paths between all the des‐ tinations, which helps in autonomous navigation. The al‐ gorithm is implemented and tested using a 2D simulator Player/Stage. The proposed system is tested with two dif‐ ferent service environment layouts and proved to have features like scalability, trainability, accuracy, and repe‐ atability. The algorithm is compared with various classi‐ cal path planning algorithms and the results show that the proposed path planning algorithm is on par with the other algorithms in terms of accuracy and efficient path generation

Physicochemical properties of new cellulosic fiber extracted from Carica papaya bark

This article presents the characteristics of Carica papaya fibers (CPFs) extracted from the bark of the perennial papaya plant. Detailed chemical compositions of CPFs such as cellulose, lignin, ash, moisture, and wax contents were established and determined by using standard methods. Further, chemical groups, crystalline structure, surface roughness, and thermal stability of CPFs were examined using Fourier transform infrared analysis, X-ray diffraction, atomic force microscope, and thermogravimetric analysis, respectively. The physico-chemical properties of CPFs, crystallinity index (56.34%), cellulose content (38.71 wt. %), hemicellulose (11.8%), and density (943 kg/m3) were compared to those properties of other natural fibers. The results suggest that the biodegradable CPFs can be used as a potential reinforcemnet in the polymer matrix composite structure