Automation Approach Research Articles

Robotized Mobile Platform for Non-Destructive Inspection of Aircraft Structures

The robotization of the non-destructive inspection of aircraft is essential for improving the accuracy and duration of performed inspections, being an integral part of inspection and data management systems within the currently developed NDT 4.0 concept. In this paper, the authors presented the design and testing of a universal mobile platform with interchangeable sensing systems for the non-destructive inspection of aircraft structures with various angles of inclination. As a result of the performed studies, a low-cost approach of automation of existing measurement devices used for inspection was proposed. The constructed prototype of the mobile platform was equipped with eddy current testing probe and successfully passed both laboratory and environmental tests, demonstrating its performance in various conditions. The presented approach confirms the effectiveness of the automation of the inspection process using climbing robots and defining the directions of possible development of automation in non-destructive testing in aviation.

Implementation of Solar PV Protection System in Indonesia: A Review

Conventional energy sources will run out if used continuously and damage the environment. Therefore, it is necessary to develop other energy sources that are safe, environmentally friendly, and inexhaustible known as renewable energy sources to meet energy needs in Indonesia. This article presents a review that discusses the protection of solar panels and PLTS. Solar panels are vulnerable to lightning strikes and other electrical disturbances, so an effective protection system is needed. This study uses a qualitative method through a literature study, reviewing various protection methods such as conventional and electrostatic lightning rods, good grounding systems, and overcurrent detection devices such as Solar Charge Controller (SCC) and Maximum Power Point Tracking (MPPT). In addition, battery protection with Automatic Transfer Switch (ATS) and Low Voltage Disconnected (LVD) is also discussed. Using Arduino Uno, ESP 32, and PLC, automation approaches offer real-time monitoring and control solutions for solar power plant performance. Innovations in the lightning protection zone concept and minimal quantity measurement schemes enable more cost-effective design of protection systems without reducing the level of protection. The results show that these various protection methods can provide effective and efficient protection for solar power plants, enabling optimization of system function and safety. In conclusion, the protection systems reviewed in this study offer a sustainable solution to Indonesia's energy challenges by utilizing the maximum potential of solar energy. The potential for further research is presented in this article to develop more efficient and effective protection methods.

Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations

AbstractNon‐trivial relationships link individual materials properties to device‐level performance. Device optimization therefore calls for new automation approaches beyond the laboratory bench with tight integration of different research methods. This study demonstrates a Materials Acceleration Platform (MAP) in the field of battery research based on the problem‐agnostic Fast INtention‐Agnostic LEarning Server (FINALES) framework, which integrates simulations and physical experiments while leaving the active control of the hardware and software resources executing experiments or simulations with the partners running the respective units. This decentralization of control is a distinctive feature of MAPs using the FINALES framework. The connected capabilities entail the formulation and characterization of electrolytes, cell assembly and testing, early lifetime prediction, and ontology‐mapped data storage provided by institutions distributed across Europe. The infrastructure is used to optimize the ionic conductivity of electrolytes and the End Of Life (EOL) of lithium‐ion coin cells by varying the electrolyte formulation. Trends in ionic conductivity are rediscovered and the effect of the electrolyte formulation on the EOL is investigated. Further, the capability of this MAP to bridge diverse research modalities, scales, and institutions enabling system‐level investigations under asynchronous conditions while handling concurrent workflows on the material‐ and system‐level is shown, demonstrating true intention‐agnosticism.

Experience of implementation and approaches for further automation in unmanned aerial counting of pacific salmon in the Okhotsk district of Khabarovsk Region

Experience of implementation and approaches for further automation in unmanned aerial counting of pacific salmon in the Okhotsk district of Khabarovsk Region

Impact of Critical Success Factors on Productivity Gain during Automation Testing

Software testing, a linchpin of quality assurance in software development, encompasses both manual and automation approaches. While manual testing ensures meticulous scrutiny, automation accelerates efficiency, reducing time and costs in dynamic landscape of software development. This research investigates the impact of critical success factors on productivity gain in software testing, with a focus on automation, within CMMI Level 5 companies. Drawing from a dataset comprising thirty real-time projects spanning banking, retail, and industrial applications, study explores dynamics of software development under agile and waterfall models. Interviews with developers and testers reveal insights into key aspects influencing productivity, including skills, experiences, and training. Emphasizing the need for strategic automation in projects subject to changes and maintenance, the research analyzes over thirty projects, scrutinizing variables such as manually designed test cases, execution time, and subsequent automation of test cases. Strategic selection of test cases for automation emerges as a cost-efficient practice. Results highlight a correlation between manual and automated test cases, indicating productivity gains. Research introduces a productivity gain metric, showcasing a break-even point where automation significantly reduces testing time. Overall, the findings offer a comprehensive understanding of software testing within CMMI Level 5, guiding organizations toward efficient automation practices and improved productivity.

Advanced test automation techniques for DevOps: Bridging the gap between test-driven development and continuous deployment in agile environments

As software development continues to change, it becomes more important to incorporate modern test automation approaches in DevOps to support efficient deployment. This work investigates TDD and CD in Agile to critically appraise advanced automation technologies and practices to fill existing deficits. This research demonstrates that the brighter Test Automation Framework approaches could improve code quality and deployment time, along with the culture of embracing best practices. The results can help practitioners who want to make their DevOps practices even more efficient and focused on quickly delivering high-quality products.

Zero-shot autonomous robot manipulation via natural language

Smart manufacturing revolutionizes advanced automation by leveraging deep learning and robotics technology. Nevertheless, deep learning implementation in manufacturing faces several challenges in preparing datasets, training a model, and adapting to various applications. Furthermore, programming languages are required to develop deep learning models and communicate with robots, which is not straightforward to quickly and precisely reflect human intentions in manufacturing processes. To alleviate the issue, this study proposes an autonomous zero-shot robot manipulation framework via natural language. Specifically, the Segmentation Anything Model (SAM), a promptable image segmentation model, first segments objects in images captured by an RGB-D camera, which are passed to Generative Pre-trained Transformers (GPTs), a Large Language Model (LLM), to identify and localize a designated object based on human commands given in natural language. Afterwards, the Robot Operating System (ROS) manipulates a robot to a localized position. With no additional programming or specialized skills in object localization and robot manipulation, the framework demonstrates an autonomous robot automation approach through intuitive communication between humans and machines through natural language.

Toward Memory-Efficient Analog Design Using Precomputed Lookup Tables

Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that can address the design productivity challenge. Although modern computing systems have powerful memory capabilities, which make the LUT approach viable, reducing the memory footprint of the LUTs remains a challenge. A memory-efficient design technique using LUTs is proposed by using an incomplete grid in the MOSFET degrees-of-freedom (DoFs) space. An efficient indexing technique for the incomplete grid is also proposed, using a precomputed offset array in various scenarios, such as two-sided constraints and three-dimensional LUTs. The results show that the proposed technique can achieve up to a 67% reduction in memory footprint, in addition to improving LUT generation time and query performance.

2D Path Following Control of Motor Graders

Optimal ground conditions in mining are crucial for operational efficiency and safety. Motor graders play a crucial role in achieving precise grading of passages; however, unlike many other mining vehicles, they have not yet been commercially automated. The redundant kinematics of motor graders, including articulation, front axle steering, and blade operations, pose significant challenges for automation. This research explores path following with a 2D kinematic model of motor graders and uses simulation to evaluate and compare the performance of these controllers in terms of step response and operational effectiveness. To address kinematic redundancy, two methods are proposed: Rear Offset Control, which defines the articulation angle using a lateral offset between the front and rear axles, and Single Track Control, which utilizes steering redundancy to adapt existing automation approaches to articulated vehicles. Simulation tests were conducted using two controllers: a Feedback Linearized PD controller (FBL+PD) and a Feedback Linearized Model Predictive Controller (FBL+MPC). Both were tuned for optimal performance on a step input path, with performance assessed based on Root Mean Square Error (RMSE) and control effort. This work introduces two methodologies—Rear Offset Control and Single Track Control—for autonomous motor grader operation. These were implemented with FBL+PD and FBL+MPC controllers on a 2D kinematic model. Future research will focus on dynamic modeling, implementing a Non-Linear Model-Predictive Controller, refining tuning methodologies for Feedback Linearized systems, and integrating these approaches with live vehicles.

Improvement of bioanalytical parameters through automation: suitability of a hand-like robotic system.

Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot's movements but also for adapting the bioanalytical method to the automated system. This study investigates whether a more human-like automation strategy-using a robotic system (RS)-is more suitable for research laboratories than a professional automation approach utilizing a commercial automated LHS. We conducted a series of measurements for protein determination using a Bradford assay manually, with a fully automated LHS, and with our human-like RS. Although the hand-like RS approach requires more than twice the time of the LHS, it achieved the best standard deviation in this setup (RS = 0.5, manual = 0.71, LHS = 0.86). Due to the low limit of detection (LOD) and limit of quantification (LOQ), most protein samples could be quantified with the RS (samples below LOQ = 9.7%, LOD = 0.23; LOQ = 0.25) compared to manual (samples below LOQ = 28.8%, LOD = 0.24; LOQ = 0.26) and the LHS (samples below LOQ = 36.1%, LOD = 0.27; LOQ = 0.31). In another time-dependent enzymatic assay test, the RS achieved results comparable to the manual method and the LHS, although the required time could be a constraint for short incubation times. Our results demonstrate that a more hand-like automation system closely models the manual process, leading easier to accurate bioanalytical results. We conclude that such a system could be more suitable for typical research environments than a complex LHS.

Deep learning based assembly process action recognition and progress prediction facing human-centric intelligent manufacturing

With the breakthroughs in cutting-edge technologies and the growing demand for personalized services, the approach of full automation alone is no longer sufficient to meet rapidly changing production needs. In light of this, a new human-centric manufacturing (HCM) paradigm has been put forward. Industrial human action recognition, as one of the key means of realizing HCM, has received widespread attention and research in recent years. In this paper, an end-to-end deep learning-based action recognition and progress prediction model was built, which can not only recognize action classes in real-time, but also accurately predict action completion times, to improve the efficiency and safety of HCM systems. First, a non-contact data acquisition system based on Azure Kinect sensor, MediaPipe and You-Only-Look-Once V5 (Yolo V5) algorithm was constructed, achieving comprehensive perception of human and objects in manufacturing system. Second, an Auto-encoder model was established to learn and compress high-dimensional human joint and object data into low-dimensional space to achieve efficient representation of the operation process. Finally, the human action recognition and progress prediction model was built and evaluated in a reducer assembly line, with an accuracy rate for action recognition of 99 %, and a time error for progress prediction of less than 6.9 %. This research aims to provide guidance for improving the level of intelligence in manufacturing and the application of AI technology in the industrial field.

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

Weighing up the pros and cons of adopting a piecemeal versus a full automation approach to cell therapy manufacture

Weighing up the pros and cons of adopting a piecemeal versus a full automation approach to cell therapy manufacture

CHALLENGES AND SOLUTIONS FOR SCALING DATA FLOWS IN THE INDUSTRY 4.0 ECOSYSTEM IN INDUSTRY AND BUSINESS

The information technology integration processes into manufacturing and logistics are often described by the term "Industry 4.0". This term includes various approaches in digitalization, automation and interconnection in various industrial sectors. The integration of advanced technologies into manufacturing creates large flows of data that conventional industry information systems are often unable to process and respond to in a timely manner. That can create delays and errors that can lead to inefficiencies in supply chains, delays in production, and communication between industrial sectors in addition in problems of transparency and accountability of information systems in the "Industry 4.0" ecosystem, problems with security, data integrity, real-time process tracking, etc. Therefore, it is an important task to research new methods of managing data flows in information systems in the "Industry 4.0" ecosystem, which can improve interaction and accelerate the adoption of information technologies in industry and logistics.

Abstract 5566: Streamlining tissue sample preparation: A comparative study on the efficiency and reproducibility of the Omni-Stainer™ dewaxing and HIER protocol

Abstract The evolution of life sciences research has entailed a shift toward understanding cellular functions within tissue microenvironments, with an emphasis on cell- and tissue-specific relationships. A key challenge in these endeavors is the need for accurate data acquisition, which can be impeded by variability in the multistep manual assay preparations. In the quest for developing reliable and efficient tissue sample preparation workflows, two protocols using multiple dewaxing agents have been compared: the Omni-Stainer™ Dewaxing and HIER antigen retrieval protocol from Parhelia Biosciences, Inc. and the analogous one on Leica BOND Rx. The objective of this research was to assess the efficiency and reproducibility of these methods in the context of a four-color morphology immunostaining used for GeoMx DSP RNA and protein detection assays (Nanostring). We examined the efficiency of automated dewaxing using various non-toxic xylene alternatives, including C12-15 alkyl Benzoate, diethylene glycol, methyl octanoate or methyl decanoate, and compared them to the proprietary Leica Bond Dewaxing solution. Then, we compared the quality of post-HIER immunostaining using two different four-antibody panels on human jejunum samples. After several rounds of optimization, we found that the V4 of the open-source Omni-Stainer™ dewax-HIER protocol resulted in identical quality of morphology immunostaining compared to proprietary Leica Bond protocol. This study validates the Omni-Stainer™ Dewaxing and HIER protocol as a viable and efficient approach for automation of sample prep for GeoMx DSP studies. Citation Format: Nikolay Samusik, Yury Goltsev, Noemi Kedei. Streamlining tissue sample preparation: A comparative study on the efficiency and reproducibility of the Omni-Stainer™ dewaxing and HIER protocol [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5566.

Comparison of pull-out capacities of robotically and manually drilled and set fasteners

The performance of post-installed fasteners in concrete can be affected by the chosen drilling and setting techniques. In the context of the development of a drilling robot for construction site implementation the impact of an automated setting process on the pull-out loads of fastening systems was evaluated. This article investigates the effect of two different fixing systems, drop-in expansion and chemical anchors, on the pull-out behaviour of manual and robotic drilling and installation. While for chemical anchors, the results indicate that robotic installation leads to similar pull-out forces with smaller deviation, expansion anchors in robotically drilled holes show slightly lower pull-out loads than those in manually drilled holes. Furthermore, a more uniform pull-out behaviour was observed for chemical anchors when set by the robot. In conclusion it can be shown that robotically set fasteners achieve a comparable performance, presenting a feasible automation approach for the construction industry.

Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe

The diagnostic of poloidal magnetic field ( ) in field-reversed configuration (FRC), promising for achieving efficient plasma confinement due to its high β, is a huge challenge because is small and reverses around the core region. The laser-driven ion-beam trace probe (LITP) has been proven to diagnose the profile in FRCs recently, whereas the existing iterative reconstruction approach cannot handle the measurement errors well. In this work, the machine learning approach, a fast-growing and powerful technology in automation and control, is applied to reconstruction in FRCs based on LITP principles and it has a better performance than the previous approach. The machine learning approach achieves a more accurate reconstruction of profile when 20% detector errors are considered, 15% fluctuation is introduced and the size of the detector is remarkably reduced. Therefore, machine learning could be a powerful support for LITP diagnosis of the magnetic field in magnetic confinement fusion devices.

Deep learning with filtering for defect characterization in pulsed thermography based non-destructive testing

Pulsed thermography is widely used for non-destructive testing of various materials. The temperature profile obtained after pulse heating is used to characterize the underlying defects in an object. In this paper, the automation of the process of defect visualization and depth quantification in pulsed thermography through various deep learning algorithms is reported. Stainless steel plate with artificial defects is considered for analysis. The raw temperature data is smoothed using moving average, Savitzky-Golay and quadratic regression filters to reduce noise. Thermal signal reconstruction, the conventional method to eliminate noise, is also used for generating filtered datasets. Defect visualization refers to identifying and locating the defects in an image sample and Mask region convolutional neural network (Mask R-CNN) is considered for not just detecting the defects but also locating them on the image. The located defects are utilized for depth estimation using the following networks-multi-layer perceptron (MLP), long short-term memory (LSTM) and gated recurrent units (GRU). The input to the networks is the temperature contrast characteristics which symbolizes the difference in temperature over defective and non-defective areas measured over 250 time points and output of the networks is the estimated depth. The study shows that LSTM based approach provides the least percentage error of 5.5% and is a very suitable approach for automation of defect characterization in pulsed thermography.

Understanding plant growth using automation approaches for biomaterial synthesis

Understanding plant growth using automation approaches for biomaterial synthesis

Towards Automated Target Picking in Scalar Magnetic Unexploded Ordnance Surveys: An Unsupervised Machine Learning Approach for Defining Inversion Priors

With advancements in both the quality and collection speed of magnetic data captured by uncrewed aerial vehicle (UAV)-based systems, there is a growing need for robust and efficient systems to automatically interpret such data. Many existing conventional methods require manual inspection of the survey data to pick out candidate areas for further analysis. We automate this initial process by implementing unsupervised machine learning techniques to identify small, well-defined regions. When further analysis is conducted with magnetic inversion algorithms, then our approach also reduces the nonlinear computation and time costs by breaking one huge inversion problem into several smaller ones. We also demonstrate robustness to noise and sidestep the requirement for large quantities of labeled training data: two pitfalls of current automation approaches. We propose first to use hierarchical clustering on filtered magnetic gradient data and then to fit ellipses to the resulting clusters to identify subregions for further analysis. In synthetic data experiments and on real-world datasets, our model successfully captures all true targets while simultaneously proposing fewer computationally costly false positives. With this approach, we take an important step towards fully automating the detection of high-risk subregions, but we wish to emphasize the importance of prudent skepticism until it has been tested and proven on more diverse data.