Academic literature on the topic 'Automated Precision Drilling Machine'
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Journal articles on the topic "Automated Precision Drilling Machine"
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Bharat, Navnath Mahajan Onkar Shrikant Kshirsagar Ganesh Ramchandra Thite Prof. V. P. Jagtap. "Design, Analysis and Manufacturing of Automated Precision Drilling Machine (PCDM 1.1)." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 239–42. https://doi.org/10.5281/zenodo.12644575.
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The design, analysis, and manufacturing of an automated precision drilling machine represent a comprehensive approach to advancing manufacturing capabilities. This innovative system integrates precision engineering principles with automation technology to enhance drilling processes. Through meticulous design, the machine ensures unparalleled accuracy and efficiency in drilling operations. Advanced analytical techniques are employed to optimize performance and reliability, ensuring precise control over drilling parameters such as speed, depth, and alignment. Manufacturing processes are carefully orchestrated to meet stringent quality standards, utilizing state-of-the-art materials and machining techniques. The resulting automated precision drilling machine represents a cutting-edge solution for industries requiring high levels of accuracy and productivity in drilling tasks. Its streamlined design, precise control mechanisms, and robust construction contribute to improved productivity, reduced downtime, and enhanced product quality, making it a valuable asset in modern manufacturing environments.
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Mehar, P. G., V. D. Dhopte, and C. D. Meshram. "“Drilling and Tapping Machine: A Review”." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44364.
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This paper presents the fabrication analysis of the drilling and tapping process within an semi automated machine system, aimed at improving precision, efficiency, and reliability in machining operations. The analysis focuses on evaluating the critical aspects of material selection, tooling, machining techniques, and assembly methods involved in the fabrication of the drilling and tapping components. It examines the effects of material properties, cutting forces, and machining accuracy. Additionally, the paper addresses the optimization of the fabrication process by evaluating boundary conditions such as spindle speed, feed rates, and tool geometry to minimize errors, enhance surface finish, and extend tool life. The paper also outlines the key parts involved in the fabrication of the drilling and tapping system, including the spindle assembly, feed mechanism, tool holders, motor, and structural frame. Key Words :- Drilling, Tapping, Material, Measurement , Construction
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Nasir Khan, Mohammad, Mohd Shadman Ansari, Md Irfan Ansari, Saifuddin, and Mohd Mukhtar Alam. "Fabrication and Automation of Drilling Machine by Using Arduino." IOP Conference Series: Materials Science and Engineering 1224, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1757-899x/1224/1/012007.
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Abstract For precision workpiece manufacturing, the system should have good dimensional accuracy and surface finish. In applications such as drilling, punching, marking, boring, tapping, etc., the workpiece is first positioned, and then the tool executes its action while the moving axis remains stationary. In the traditional method of such applications, manufacturers use very expensive CNC machines to program the cycle and perform the same work. Large manufacturers can afford such expensive machines, but for the small machinery manufacturing industry, we must consider low-cost solutions that can provide high-quality output. In this study, we tried to propose a low-cost design that can be used to achieve functions similar to CNC. By applying this machine in industry, multiple generations can be obtained in a short time. It is very difficult to estimate the drilling depth when manually drilling with a traditional drilling machine, and the work will usually fail due to over-drilling. In many cases, it is difficult to measure the depth after the drilling is completed; especially the depth of the fine hole cannot be measured. Therefore, an automatic drilling machine that performs the drilling function according to the generated drilling depth and transmitted to the control circuit is indispensable; therefore, undertaking the study, it exposed the technology of the dedicated drilling machine. The automatic drilling machine designed here is very useful for the mechanical workshop. The machine is built with power feed technology and is designed to drill the job to a certain specified depth.
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Lokesh, G. "Fabrication and Automation of Drilling Machine by Arduino Control." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 815–24. http://dx.doi.org/10.22214/ijraset.2024.63657.
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Abstract: For precision workpiece manufacturing, the system should have good dimensional accuracy and surface finish. In applications such as drilling, punching, marking, boring, tapping, etc., the workpiece is first positioned, and then the tool executes its action while the moving axis remains stationary. In the traditional method of such applications, manufacturers use very expensive CNC machines to program the cycle and perform the same work. Large manufacturers can afford such expensive machines, but for the small machinery manufacturing industry, we must consider low-cost solutions that can provide highquality output. In this study, we tried to propose a low-cost design that can be used to achieve functions similar to CNC. By applying this machine in industry, multiple generations can be obtained in a short time. It is very difficult to estimate the drilling depth when manually drilling with a traditional drilling machine, and the work will usually fail due to over-drilling. In many cases, it is difficult to measure the depth after the drilling is completed; especially the depth of the fine hole cannot be measured. Therefore, an automatic drilling machine that performs the drilling function according to the generated drilling depth and transmitted to the control circuit is indispensable; therefore, undertaking the study, it exposed the technology of the dedicated drilling machine. The automatic drilling machine designed here is very useful for the mechanical workshop. The machine is built with power feed technology and is designed to drill the job to a certain specified depth.
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Li, Haoyuan, Yongchao Wang, Wei Chen, et al. "Optical coherence tomography guided automatic robotic craniotomy surgery platform." Biomedical Optics Express 16, no. 2 (2025): 778. https://doi.org/10.1364/boe.549260.
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A transparent craniotomy window is required for optical brain imaging; however, traditional surgical preparation requires well-trained surgeons, is time-consuming, and suffers from low success rates. To address this issue, we present an automatic craniotomy platform combining optical coherence tomography (OCT) with an automated drilling machine. The OCT provides 3D skull data to guide a homemade closed-loop high-precision drill for controlled craniotomies, achieving a 100% success rate in creating small, large, and thinned windows. A synthetic transparent window was installed after skull excision. This system enables high-quality OCT angiography, velocimetry, and ultrasound imaging, offering an efficient tool for brain research.
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Shtuts, Andrii. "CONTROL ALGORITHM OF AUTOMATED STAMPING BY ROLLING OF THE CONTROL SYSTEM OF THE ELECTROMECHANICAL DRIVE OF THE VERTICAL DRILLING MACHINE." Vibrations in engineering and technology, no. 2(113) (August 30, 2024): 66–74. https://doi.org/10.37128/2306-8744-2024-2-7.
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The control algorithm of automated stamping by rolling of the control system of the electromechanical drive of the vertical drilling machine is relevant in modern conditions due to the rapid development of production automation technologies. Electromechanical drives allow for high precision and productivity in processing materials by pressure, and control systems make the rolling stamping process more efficient and less expensive by optimizing speed and movement angles. SHO, in turn, allows you to create complex geometric shapes of high-quality parts. Research in the field of development of control algorithms is important for improving the processes of manufacturing parts, increasing productivity and reducing production costs. The urgency of the work is to find optimal solutions for automated vertical drilling machines, which will improve the quality of production and the competitiveness of enterprises on the market. This scientific article examines the control algorithm of the automated SHO control system of the electromechanical drive of a vertical drilling machine. An overview of the principles of the machine and the main stages of the SHO process is given. The proposed algorithm is based on the use of a control system that ensures accuracy and stability of the process. An analysis of the efficiency of the algorithm in practice was carried out, the advantages and disadvantages of using this control system were determined. The results of research indicate an increase in the productivity and quality of processing parts due to the implementation of this algorithm. Automated SHO systems can be widely used in mechanical engineering for the manufacture of complex shaped parts. The reliability of these systems is a key factor that determines their efficiency and cost-effectiveness. The development of devices for automatically changing the equipment of the stamping and rolling complex arises with the aim of increasing the efficiency and automation of production processes in industry. Roll stamping is an important technology for the production of metal parts, but the process of changing equipment (such as tools or dies) can be time and resource intensive. The obtained results can be useful for productions using automated rolling stamping systems. They will make it possible to increase the efficiency of the production process, reduce material costs, and also ensure stable and uninterrupted production.
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Pan, Shenghu, and Xinglong Ni. "Design of Automatic Drilling Fluid Configuration System Based on PLC." Academic Journal of Science and Technology 13, no. 2 (2024): 173–81. http://dx.doi.org/10.54097/g5a90m96.
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With the rapid advancements in the drilling industry, drilling fluid serves as a crucial circulating medium, significantly influencing both efficiency and safety through precise dispensing. This paper introduces an automatic configuration system for drilling fluids based on a Programmable Logic Controller (PLC), addressing health risks, dosage instability, and high costs associated with traditional manual preparation methods. The system employs a modular design comprising components such as human-machine interaction, powder and liquid material addition, mixing and stirring, storage of drilling fluids, heating, and cleaning. By integrating a C#-developed human-machine interface with PLC control circuits, full process automation is achieved—including formula importation, system reset functions, raw material addition procedures, transfer to storage of drilling fluids and cleaning operations. Experimental results demonstrate that the system maintains an average error below 0.1g in powder feeding experiments—indicating high precision and consistency in formulating drilling fluids. This study not only enhances automation in preparing drilling fluids but also significantly supports technological advancement within the industry.
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Kofi Yeboah Adjei, Godwin Ekunke Odor, Sharafadeen Ashafe Nurein, et al. "Optimizing well placement and reducing costs using AI-driven automation in drilling operations." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 1029–38. https://doi.org/10.30574/wjarr.2025.25.2.0436.
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AI is increasingly being used in drilling operations, redefining efficiency, cost-effectiveness, and safety within the oil and gas industry. Traditional drilling operations are usually plagued by inefficiencies, high NPT, and suboptimal well placement due to over-reliance on manual decisions and conventional geological interpretation. AI-driven automation uses machine learning, IoT devices, real-time data analytics, and predictive maintenance to provide improved drilling precision, better placement of wells, and reduced operational risks. Industry leaders have shown that the gains in efficiency are huge; Chevron recorded a 30% increase in drilling speed, with a corresponding 25% reduction in operational costs, resulting from AI-driven automated drilling. Shell reported 130% gains in drilling efficiency due to AI-enhanced optimization models. BP and ExxonMobil implemented AI predictive maintenance, realizing a 20% reduction in maintenance costs, with a resulting 15% increase in equipment uptime. Saudi Aramco optimized well placement, leading to a 35% increase in production and reduced drilling time. This review critically assesses such AI applications in drilling automation with regard to operational efficiency, cost reduction, and sustainability. While a game-changing technology, several barriers to widespread diffusion exist: integration of data, which is highly complex; costs of implementation, which are relatively high; and skilled people are required. The ability to remove these barriers through technological development and strategic collaboration by the industry will be key in maximizing the full benefits of AI in drilling automation.
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Novrianto, Elfizar, Machrus Ali, and Hidayatul Nurohmah3. "Optimasi Perancangan Sistem Kontrol Mesin CNC Pengebor PCB berbasis Ant Colony Optimization." Nucleus Journal 2, no. 2 (2023): 82–94. http://dx.doi.org/10.32492/nucleus.v2i2.2202.
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A Print Circuit Board (PCB) is a micro (small) sized board that contains various electronic components that are used in an automatic circuit. PCB drilling is usually done manually with human power, which takes a lot of time when there are more and more holes in the PCB. And precision is required when the drill bit touches the PCB board which creates frictional forces and can cause drilling errors. This research uses data collection after carrying out several simulation methods using Matlab 13a. With optimal division methods including without control, Conventional PID, auto PID and PID - ACO. The aim of this research is to determine the advantages of the Ant Colony Optimization (ACO) method in controlling Computer Numerical Control (CNC) machines. The simulation results show that the best optimization method is produced by the PID - Ant Colony Optimization method which produces overshoot: 0.1199, undershoot: 0.0544, and settling time at 2.532 seconds which is the smallest value, while the design without control never reaches stable steady with the largest undershot. : 0.523. so PID - Ant Colony Optimization was chosen as the best method and is suitable for use in controlling PCB Drilling CNC Machines. By applying the PID - Ant Colony Optimization method to the CNC PCB Drilling Machine, it will be able to produce more precise drilling results
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Zeng, Min, Guang Hui Chen, Liang Hong Wei, and Shi Ming Cui. "Control System Design of a Novel Fan Shaft Sleeve Automatic Processing Machine." Advanced Materials Research 422 (December 2011): 70–74. http://dx.doi.org/10.4028/www.scientific.net/amr.422.70.
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The fan shaft sleeve is a very important part of the fan-related machine and has high demand for precision. At present, due to the lack of machine that exclusively for sleeve processing at home, the sleeve production largely depends on manual labor which yields very low productivity. This paper introduces an automatic machine for fan shaft sleeve processing which introduces the Omron PLC as the hardcore of the control system. The machine proposed also includes a cam splitter. Controlled by motors, clutches and retarders, the splitter provides 6 stations for feeding, drilling, taping, milling, chamfer and discharging. Driven by 3 phases AC motors and air tanks, the machine can mange the fan shaft sleeve processing automatically and achieves high productivity and quality.
Dissertations / Theses on the topic "Automated Precision Drilling Machine"
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Massaccesi, Luciano. "Machine Learning Software for Automated Satellite Telemetry Monitoring." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20502/.
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During the lifetime of a satellite malfunctions may occur. Unexpected behaviour are monitored using sensors all over the satellite. The telemetry values are then sent to Earth and analysed seeking for anomalies. These anomalies could be detected by humans, but this is considerably expensive. To lower the costs, machine learning techniques can be applied.
In this research many diferent machine learning techniques are tested and compared using satellite telemetry data provided by OHB System AG. The fact that the anomalies are collective, together with some data properties, is exploited to improve the performances of the machine learning algorithms. Since the data comes from a real spacecraft, it presents some defects. The data covers in fact a small time-lapse and does not present critical anomalies due to the spacecraft healthiness. Some steps are then taken to improve the evaluation of the algorithms.
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Lin, Hao-Wei, and 林浩瑋. "Development of Automated Precision Bone Drilling Machine for Medical Surgery." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/18371736108470954684.
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碩士<br>元智大學<br>機械工程研究所<br>88<br>This thesis presents the development of a precision bone drilling machine for medical surgery. The neurosurgeon needs to drill holes on the head bone in order to get to the nidus. For the time being, this drilling operation is control by the doctor's feeling of hands.
The purpose of this investigation is to use fuzzy controller to control the penetration of the drill bit when it drills through different tissues of human bones. The drill is driven by a DC motor, whose current is proportional to the cutting torque. The controller senses the current to detect different tissue of human bone. A step motor controls the feed rate of the drill, and it stops feeding when the bone, whose thickness is unknown, is drilled through. A mechanical robot holds the drill smoothly avoiding the thrashing from the surgeon. The system undergoes many drilling tests using various cutting conditions. There were no unexpected failure, and the overshoots of all drilling tests were less than 2mm.
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何友森. "The studies on the dynamic searching of PCB laser drilling machine and wafer precision alignment used by the image processing." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/75486808717634042062.
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碩士<br>國立彰化師範大學<br>機電工程學系<br>93<br>This paper reports on a visual image system of initial workpiece alignment for fast positioning and accuracy inspection of printed circuit boards. The system uses one fixed position camera to detect the positions of the hole at workpiece placed on measuring table. Theoretical analysis and experiment show how approach is reasonable and can be used for machine vision application.
In addition, the inspection and the automatic alignment for the wafer bonding process will be presented. The major work of the two wafer bonding process is the automatic alignment with two cross marks which catching by the CCD image system. First the automatic alignment system of machine vision will be described in this paper. Then, the cross mark of the wafer catching by the CCD image system will be achieved. Finally, the inspecting, analysis of the automatic bonding system on the wafer process is controlled by the personal computer by used the Microsoft Visual Basic 6.0 & Microsoft Visual C++ 6.0 programs. We can provide the method of the automatic alignment of the two wafer bonding process by the image process method. In addition, the movable table driven by the servo motor and z axis feeding system driven by the pneumatics cylinder are also controlled by personal computer.
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Weixiang, Peng, and 彭偉翔. "Study on multi-surfaces and multi-holes of cutting and drilling different angles using a Reconfigurable Precision Hybrid Five -axis Machine Tool." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/85934398635366707808.
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碩士<br>高苑科技大學<br>機械與自動化工程研究所<br>99<br>Parallel machine tool (PMT) has advantages of high rigidity, high precision, low inertia and simple mechanism. The machine centers integrate two mechanical models which are a parallel structure and a traditional CNC machine. A 3-DOF parallel platform hybrid mechanism can also be combined with a CNC X-Y table into a 5-axis working center.
The machining centers are trended to be the reconfigurable precision hybrid machine tools（RHMT） to conduct the complexity and precision of future products. It will be developed on multi-surfaces and multi-holes of cutting different angles. This study focuses on static state errors that consist of component parts machining, work-piece orientation, and mechanism combination. The positions of moving platform is measured by a laser displacement sensor to prove center departure of moving platform and to calibrate three heights of connect point. Inverse kinematics of this mechanism will be revised and precision of position of moving platform will be promoted in this study. Finally, it will be proved to accurate on five and nine surfaces with holes of cutting different angles by Coordinate Measuring Machine（CMM）.
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Hwang, Susan. "Similarity-principle-based machine learning method for clinical trials and beyond." Thesis, 2020. https://hdl.handle.net/2144/41983.
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The control of type-I error is a focal point for clinical trials. On the other hand, it is also critical to be able to detect a truly efficacious treatment in a clinical trial. With recent success in supervised learning (classification and regression problems), artificial intelligence (AI) and machine learning (ML) can play a vital role in identifying efficacious new treatments. However, the high performance of the AI methods, particularly the deep learning neural networks, requires a much larger dataset than those we commonly see in clinical trials. It is desirable to develop a new ML method that performs well with a small sample size (ranges from 20 to 200) and has advantages as compared with the classic statistical models and some of the most relevant ML methods.
In this dissertation, we propose a Similarity-Principle-Based Machine Learning (SBML) method based on the similarity principle assuming that identical or similar subjects should behave in a similar manner. SBML method introduces the attribute-scaling factors at the training stage so that the relative importance of different attributes can be objectively determined in the similarity measures. In addition, the gradient method is used in learning / training in order to update the attribute-scaling factors. The method is novel as far as we know.
We first evaluate SBML for continuous outcomes, especially when the sample size is small, and investigate the effects of various tuning parameters on the performance of SBML. Simulations show that SBML achieves better predictions in terms of mean squared errors or misclassification error rates for various situations under consideration than conventional statistical methods, such as full linear models, optimal or ridge regressions and mixed effect models, as well as ML methods including kernel and decision tree methods.
We also extend and show how SBML can be flexibly applied to binary outcomes. Through numerical and simulation studies, we confirm that SBML performs well compared to classical statistical methods, even when the sample size is small and in the presence of unmeasured predictors and/or noise variables.
Although SBML performs well with small sample sizes, it may not be computationally efficient for large sample sizes. Therefore, we propose Recursive SBML (RSBML), which can save computing time, with some tradeoffs for accuracy. In this sense, RSBML can also be viewed as a combination of unsupervised learning (dimension reduction) and supervised learning (prediction). Recursive learning resembles the natural human way of learning. It is an efficient way of learning from complicated large data. Based on the simulation results, RSBML performs much faster than SBML with reasonable accuracy for large sample sizes.
Books on the topic "Automated Precision Drilling Machine"
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Hoffman, Peter J., Eric S. Hopewell, Brian Janes, and Sharp Kent M. Jr. Precision Machining Technology. Delmar Cengage Learning, 2013.
Find full textBook chapters on the topic "Automated Precision Drilling Machine"
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Bhoi, Ashutosh, Rajendra Prasad Nayak, Sourav Kumar Bhoi, and Srinivas Sethi. "Automated Precision Irrigation System Using Machine Learning and IoT." In Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6081-5_24.
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Zhao, Ran, Adrien Drouot, Joseph Griffin, Richard Crossley, and Svetan Ratchev. "A Low-Cost Automated Fastener Painting Method Based on Machine Vision." In Precision Assembly in the Digital Age. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05931-6_9.
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Klene, G., A. Grauel, H. J. Convey, and A. J. Hartley. "Design of Multi-drilling Gear Machines by Knowledge Processing and Machine Simulation." In Intelligent Data Engineering and Automated Learning — IDEAL 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_16.
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Banu, Mohammed Razia Alangir, and A. S. Gousia Banu. "Advancing Precision Medicine: An Exploration of Hybrid Deep Learning Approaches for Automated Human Brain Tissue Segmentation and Tumour Localization in MRI Imaging." In Smart Healthcare and Machine Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3312-5_10.
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Patwal, Priyanshu, Rahul Chauhan, Chandradeep Bhatt, and Swati Devliyal. "Automated tomato disease detection and classification using image processing and machine learning for precision agriculture." In Challenges in Information, Communication and Computing Technology. CRC Press, 2024. http://dx.doi.org/10.1201/9781003559085-84.
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Lange, K. "Nc — Radial Forging — a New Concept in Flexible Automated Manufacturing of Precision Forgings in Small Quantities." In Proceedings of the Twenty-Fifth International Machine Tool Design and Research Conference. Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-07529-4_6.
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Tuncel, Nina. "The Role of Artificial Intelligence (AI) in Radiation Treatment and Investment Perspectives." In The Latest Innovative Approaches in Radiation Therapy. Nobel Tip Kitabevleri, 2024. http://dx.doi.org/10.69860/nobel.9786053359425.8.
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In this section, AI’s impact on medicine, specifically radiation treatment processes, is highlighted. AI in radiotherapy has led to significant innovations, enhancing the precision and efficiency of cancer treatments. Advanced algorithms enable automated and more accurate tumor detection and delineation in imaging, optimizing radiation dose distribution while minimizing exposure to healthy tissues. AI-driven treatment planning reduces the time required for complex calculations and improves personalized treatment strategies. Machine learning models predict patient responses and potential side effects, allowing for proactive adjustments. Overall, AI is revolutionizing radiotherapy by improving treatment accuracy, reducing planning time, and enhancing patient outcomes.
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Zünd, Daniel, and Luís M. A. Bettencourt. "Street View Imaging for Automated Assessments of Urban Infrastructure and Services." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_4.
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AbstractMany forms of ambient data in cities are starting to become available that allows tracking of short-term urban operations, such as traffic management, trash collections, inspections, or non-emergency maintenance requests. However, arguably the greatest promise of urban analytics is to set up measurable objectives and track progress toward systemic development goals connected to human development and sustainability over the longer term. The challenge for such an approach is the connection between new technological capabilities, such as sensing and machine learning and local knowledge, and operations of residents and city governments. Here, we describe an emerging project for the long-term monitoring of sustainable development in fast-growing towns in the Galapagos Islands through the convergence of these methods. We demonstrate how collaborative mapping and the capture of 360-degree street views can produce a general basis for a broad set of quantitative analytics, when such actions are coupled to mapping and deep-learning characterizations of urban environments. We map and assess the precision of urban assets via automatic object classification and characterize their abundance and spatial heterogeneity. We also discuss how these methods, as they continue to improve, can provide the means to perform an ambient census of urban assets (buildings, vehicles, services) and environmental conditions.
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Wang, Yinzhen, Jiangping Liu, and Liyun Zhuo. "Research on Irregular-Shaped Workpiece Fixture Based on Electrorheological Fluid." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7887-4_108.
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Abstract Although the current computer numerical control (CNC) machine tools on the market are flexible, efficient, and automated, capable of enhancing precision, handling large quantities of components, and processing diverse ordinary parts, they often encounter challenges in positioning, clamping, and stress concentration when fabricating special-shaped structures. In response to these issues, multiple sets of specialized fixtures are usually designed and manufactured according to requirements, which increases the processing cost of irregular-shaped workpieces. Moreover, these specialized fixtures cannot be universally applied, leading to substantial wastage. Therefore, this project utilizes the inherent transformation characteristics of electrorheological (ER) fluid between liquid and solid to achieve rapid automatic clamping. The clamping is fully enveloping, ensuring stable clamping force and minimizing damage to irregular-shaped workpieces. Simultaneously, by combining multi-axis attitude adjustment arms with multiple sets of laser positioning, flexible and precise adjustment of the attitude of irregular-shaped workpieces is achieved. Ultimately, the combination of ER fluid enables the clamping and positioning of any irregular-shaped workpiece.
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Poursanidis, Miltiadis, Patrick Link, Jochen Schmid, and Uwe Teicher. "Incorporating Shape Knowledge into Regression Models." In Cognitive Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83097-6_7.
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Abstract Informed learning is an emerging field in Machine Learning that aims at compensating for insufficient data with prior knowledge. Shape knowledge covers many types of prior knowledge concerning the relationship of a function’s output with respect to input variables, for example, monotonicity, convexity, etc. This shape knowledge—when formalized into algebraic inequalities (shape constraints)—can then be incorporated into the training of regression models via a constrained optimization problem. The defined shape-constrained regression problem is, mathematically speaking, a semi-infinite program (SIP). Although off-the-shelf algorithms can be used at this point to solve the SIP, we recommend an adaptive feasible-point algorithm that guarantees optimality up to arbitrary precision and strict fulfillment of the shape constraints. We apply this semi-infinite approach for shape-constrained regression (SIASCOR) to three application examples from manufacturing and one artificial example. One application example has not been considered in a shape-constrained regression setting before, so we used a methodology (ISI) to capture the shape knowledge and define corresponding shape constraints. Finally, we compare the SIASCOR method with a purely data-driven automated machine learning method (AutoML) and another approach for shape-constrained regression (SIAMOR) that uses a different solution algorithm.
Conference papers on the topic "Automated Precision Drilling Machine"
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Refaat, Seif Eldin, and Haitham Omran. "Machine Learning Assisted Automated Laser Profile Tracking." In Advanced Solid State Lasers. Optica Publishing Group, 2024. https://doi.org/10.1364/assl.2024.jtu2a.45.
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We investigate three convolutional neural networks machine-learning models —YOLO, Faster R-CNN, and DETR— in precision detection and tracking of laser profiles. Automated tracking is achieved by coupling trained models to nano-positioning system. We achieved detection mean-average-precision (mAP) of 99% with 20um spatial tracking resolution.
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Wahed, Mutaz Abdel, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mohammad Al-Batah, Ahmad Fuad Bader, and Salma Abdel Wahed. "Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux." In 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS). IEEE, 2024. https://doi.org/10.1109/netapps63333.2024.10823509.
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Ranganathan, Chitra Sabapathy, Amjath Ali Jakul Basha, A. Vijayalakshmi, N. Mohankumar, S. Sujatha, and C. Sasikala. "Green Leaf Disease Detection Using Raspberry Pi and Random Forest Machine Learning Algorithm: An Automated Approach for Precision Agriculture." In 2024 10th International Conference on Smart Computing and Communication (ICSCC). IEEE, 2024. http://dx.doi.org/10.1109/icscc62041.2024.10690665.
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Robinson, Timothy S., and Olav Revheim. "Automated Detection of Rig Events from Real-Time Surface Data Using Spectral Analysis and Machine Learning." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212481-ms.
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Abstract The authors present a method for automated, high-fidelity detection of rig events characterized by complex temporal signals, such as downlinking, or wave-induced heave affecting floating rigs. These can adversely impact other systems utilizing relevant data streams, for example downlinking via mud pulse telemetry can interfere with detection of pressure changes that might indicate hole cleaning problems. Identifying these events using classification techniques applied to time-domain data is difficult, hence spectral (frequency domain) techniques, combined with Machine Learning (ML), were applied to solving this problem. Surface measurements from a variety of wells, fields, regions, service companies and operators were used to develop and validate the detection methods. Data was preprocessed using time-frequency analysis, and then input to discriminative classifiers to identify rig events of interest. For downlinking state detection, high recall and precision scores (both &gt;93%) were achieved on independent holdout well data, and thus false positive rates were low. Successful detection was demonstrated on wells separate from the training data, hence the method is expected to generalize to new well operations. The detection method enhances situational awareness, and can actively support other software in improved automated decision-making by providing operational context in real-time, such as suppression of false warnings from monitoring pressure or modelled ECD for detecting signs of poor hole cleaning. These techniques are not limited to downlinking or heave detection, and can be applied more generally to scenarios with complex periodic signals.
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Bigoney, Burt, Scott Smith, and Michael Bruns. "A High-Performance Milling Machine for Aerospace Applications." In 2023 AeroTech. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-1002.
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<div class="section abstract"><div class="htmlview paragraph">As the aerospace industry moves toward determinate assembly and ever-tighter manufacturing tolerances, there is a need for automated, high-precision milling, trimming and drilling equipment that is specialized for aerospace applications. Precision countersinking is a common requirement for aircraft parts, but this is not a process that typical general-purpose milling machines are able to accommodate without the use of specialty tools such as depth-stop tool holders. To meet this need, Electroimpact has designed a 5-axis milling machine with high-speed clamping capability for countersink depth control. A custom trunnion and head with a quill and an additional clamp axis provide clamping functionality similar in speed and precision to a riveting machine, while maintaining the accuracy and features of a conventional machining center. An additional focus on design for pre-compensation accuracy has allowed the system to achieve post-compensation path and positioning tolerances that are competitive with premium milling machines. This combination of capabilities makes the system well suited for a variety of cutting and drilling processes for aircraft manufacture. This paper will describe the background and design process that led to the development of this system, and will provide details on its capabilities, specifications, and possible applications.</div></div>
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Whatley, Michael, Arima Ayanambakkam, Derek Patterson, Blakley Farrow, Le Feng, and Parker Hewitt. "Machine and Process Automation are Improving Personnel Safety and Drilling Performance." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210368-ms.
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Abstract The industries’ number one priority is ensuring everyone goes back home in the same condition they came to work. With that guiding principal in mind, how could a drilling contractor work to substantially reduce our crews health, safety, and environment (HSE) exposure? Engineer solutions that take them off the most dangerous place on a drilling rig. Traditional drilling processes require personnel to manually handle tubulars while making connections, building stands, or racking back pipe. This requires personnel to perform strenuous tasks in dangerous areas. These areas often require personnel to work near moving machinery. Manual operations often lead to high degrees of variability in performance based on rig crew experience and familiarity of the operations. Development of a fully automated rig required overcoming many challenges to process tubulars from the ground to well center. New machines were designed to meet the automation requirements. The ground handling system was designed to clean, dope, and measure pipe. The robotic pipe handler was designed to handle retrieving tubulars from the ground handling system, deliver to well center and spin in the connection. The automated floor wrench is responsible for making up connections. The rack and pinion hoisting system was designed to handle the drill string with precision and eliminate the need to slip and cut drill line. To achieve the desired automated tripping and drilling process automation, a rig operating system with sequencing functionality, tubular management, and zone management was required. The rig operating system integrates the different machines and manages the necessary protections at the machine layer. The sequencing layer is responsible for coordinating the process automation at the appropriate set points for rotary drilling, slide drilling, tripping, and casing connections. The robotic rig has currently completed 9 wells and has continuously improved since the start of drilling in August. The drilling connection times have improved by 28% and the rig became the third fastest across the fleet. Additionally, the rig has improved its casing running performance by approximately 43% and is now within a few joints of other top performing rigs in the fleet. Fully automated control of these processes guarantees consistent and repeatable performance across the wells drilled. This is all being done without the need to have personnel handling tubulars in the mast or on the rig floor. The success of this first of its kind robotic rig can be attributed to partnerships with the operator, the drilling operations team, the rig engineering team and the controls and automation team. The teams collaborate to identify areas for improvement to continuously drive better, safer, and faster drilling.
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Nanlawala, Michael, and Ali Manesh. "Robotic Deburring of Precision Gears." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/ptg-14420.
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Abstract Machining processes such as milling, drilling, turning, hobbing or gear teeth cutting create burrs on the edges of metal parts when the cutting tool pushes material over an edge, instead of cutting cleanly through the material. The size, shape and characteristics of the resulting burrs depend upon a number of process factors such as: tool material and its hardness, tool sharpness, tool geometry, cutting forces, ductility of the material being machined, speed and feed of the cutting tool, and depth of the cut. Except for the turning operation, a subsequent deburring operation is often required to remove the “loose” burrs and also to produce a chamfer to “break” or smooth the edges of the machined part. Gears, in general are deburred manually or by simple mechanical equipment such as Redin™ Deburring Machine. Because of the complexity and/or specific chamfering requirements of aerospace gears, most of these gears have to be deburred manually. In general, manual deburring is a very labor intensive process. Poor quality resulting from inconsistent manual operation, health, safety and environmental related issues, and high turnover of operators incur indirect cost as well. The “Redin Deburring Machines”, however, lack the dexterity and the programmability, which are essential to meet the specific chamfering needs of usually complex shaped aerospace gears. Automating the deburring process can therefore result in significant cost reduction, improved productivity, and improved quality of deburred edges. Mainly because of these reasons, there has been industry wide demand to replace manual deburring by more efficient, reliable and safer automated deburring system.
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Ifenaike, A. O., and O. B. Oluwadare. "Advancing Drilling Safety: Automated Anomaly Detection in Well Control Using Machine Learning Techniques." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/221626-ms.
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Abstract The rise of anomalies like kicks, blowouts, lost circulation, and gas migration in drilling operations poses significant challenges to safety, environmental sustainability, and economic stability. Implementing frameworks for proactive monitoring and accurate anomaly detection is crucial to maintaining wellbore integrity, ensuring personnel safety, and minimizing environmental impact. This need is particularly acute in complex drilling environments, marked by intricate subsurface conditions and high costs, where unchecked anomalies can lead to severe consequences. Accordingly, this research emphasizes the importance of swiftly identifying and classifying such events, enabling timely interventions to prevent catastrophic outcomes and operational disruptions. This study introduces a multi-layered predictive model that effectively identifies and classifies well control anomalies, addressing the challenge of high false positive rates associated with existing research literature. This study utilizes a comprehensive dataset of historical well control incidents, including indicator parameters such as mud return rates, drilling fluid properties and wellbore pressure. The intelligent model is highly interpretable and outperforms existing counterparts in blind tests with a precision score of 0.918 and a low false positive rate of 2.38%, marking a significant advancement in intelligent anomaly prediction for drilling safety. This research improves traditional well control methods, which depend on equipment monitoring and slower responses, by employing real-time data analysis and machine learning. This shift provides drilling engineers with an advanced tool, enhancing safety and efficiency, and paving the way for more predictive and agile operations.
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Nye, Rebecca, Camilo Mejia, and Evgeniya Dontsova. "Real Time Cloud-Based Automation for Formation Evaluation Optimization, Risk Mitigation and Decarbonization." In SPE Offshore Europe Conference & Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205402-ms.
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Abstract Recent developments in artificial intelligence (AI) have enabled upstream exploration and production companies to make better, faster and accurate decisions at any stage of well construction, while reducing operational expenditure and risk, increasing logistic efficiencies. The achieved optimization through digitization at the wellsite will significantly reduce the carbon emissions per well drilled when fully embraced by the industry. In addition, an industry pushed to drill in more challenging environments, they must embrace safer and more practical methods. An increase in prediction techniques, to generate synthetic formation evaluation wellbore logs, has unlocked the ability to implement a combination of predictive and prescriptive analytics with petrophysical and geochemical workflows in real time. The foundation of the real time automation is based on advanced machine learning (ML) techniques that are deployed via cloud connectivity. Three levels of logging precision are defined in the automated workflow based on the data inputs and machine learning models. The first level is the forecasting ahead of the bit that implements advanced machine learning using historical data, aiding proactive operational decisions. The second level has improved precision by incorporating real time drilling measurements and providing a credible contingency to for wellbore logging program. The last level incorporates petrophysical workflows and geochemical measurements to achieve the highest precision for logging prediction in the industry. Supervised and unsupervised machine learning models are presented to demonstrate the path for automation. Precision above 95% in the real time automated workflows was achieved with a combination of physics and advanced machine learning models. The automation of the workflow has assisted with optimization of logging programs utilizing technology with costly lost in hole charges and high rate of tool failures in offshore operations. The optimization has reduced the requirement for logistics associated with logging and eliminated the need for radioactive sources and lithium batteries. Highest precision in logging prediction has been achieved through an automated workflow for real time operations. In addition, the workflow can also be deployed with robotics technology to automate sample collection, leading to increased efficiencies.
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Acharya, S., K. Fabian, and K. Westeng. "Comparison of Supervised and Unsupervised Machine Learning for Well-Log Depth Alignment." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/223730-ms.
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Abstract Well logs, crucial for drilling and post-drilling analysis, provide continuous measurements of subsurface formations as a function of depth. Logging while drilling (LWD) and electrical wireline logs (EWL) are commonly used techniques for well-log acquisition. Both methods are prone to depth measurement errors due to various factors, which need to be aligned to a common depth-scale for subsequent analysis. This study compares two automated machine learning approaches for aligning repeated measurements of the same parameters from LWD and EWL logs of the same well. The first approach is based on supervised learning and the second on unsupervised learning. The supervised approach trains a 1D convolutional neural network (1D CNN) classification model on actual well-log data from the Norwegian North Sea, using LWD-EWL pairs of log slices. A specific depth discrepancy is introduced for each pair, and the logs are divided into various classes based on the depth error between them. The unsupervised method combines autoencoders and K-means clustering to identify potential lithological boundaries in EWL and LWD multiparameter log data. These predicted boundaries are validated by requesting a maximal Pearson correlation. The performance of the classification model is evaluated using metrics such as accuracy, precision, and recall. The optimal number of clusters for K-means clustering is identified using silhouette scores and the elbow method. Depth alignment is verified through visual inspection, correlation analysis, and Euclidean distances between logs. Supervised and unsupervised approaches significantly improve the alignment of various logs, such as bulk density, deep resistivity, sonic compressional, and neutron porosity. Both methods outperform maximization of cross-correlation for specific logs, such as deep resistivity and neutron porosity. These results highlight the potential of machine learning for efficient and accurate depth alignment of well logs, with promising implications for enhancing drilling and post-drilling analysis in the oil and gas industry.
Reports on the topic "Automated Precision Drilling Machine"
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Adam, Gaelen P., Melinda Davies, Jerusha George, et al. Machine Learning Tools To (Semi-) Automate Evidence Synthesis. Agency for Healthcare Research and Quality (AHRQ), 2025. https://doi.org/10.23970/ahrqepcwhitepapermachine.
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Introduction. Tools that leverage machine learning, a subset of artificial intelligence, are becoming increasingly important for conducting evidence synthesis as the volume and complexity of primary literature expands exponentially. In response, we have created a living rapid review and evidence map to understand existing research and identify available tools. Methods. We searched PubMed, Embase, and the ACM Digital Library from January 1, 2021, to April 3, 2024, for comparative studies, and identified older studies using the reference lists of existing evidence synthesis products (ESPs). We plan to update searches every 6 months. We included evaluations of machine learning or artificial intelligence tools to automate or semi-automate any stage of systematic review production. Two reviewers conducted title and abstract screening independently, with disagreements resolved through discussion or adjudication by a third reviewer. A single reviewer performed full-text screening and data extraction. We did not assess the quality of individual studies or the strength of evidence across studies. Extracted data included key characteristics of the tools (e.g., type of automation method, systematic review tasks automated), evaluation methods, and performance results (e.g., recall, measures of workload, accuracy, and the authors’ conclusions). The protocol was prospectively registered on the AHRQ website (https://effectivehealthcare.ahrq.gov/products/tools/protocol). Results. We included 56 studies, which evaluated the performance of tools primarily relative to standard human processes across various systematic review tasks. For search-related tools (7 studies), recall (the percent of relevant citations correctly identified) ranged from 0 to 97 percent (median 26%) compared to human-developed search strategies, while precision (the percent of identified citations that are relevant) ranged from 0 to 13.4 percent (median 4.3%). Tools designed to identify randomized controlled trials (RCTs) (6 studies) had recalls between 96 and 100 percent (median 98.5%), with precision ranging from 8 to 92 percent (median 44%), compared to either manual identification or PubMed’s “publication type” tags. Abstract screening tools (22 studies) had a median recall of 93 percent (range 1–100%) with human screening as the standard, while median burden reduction was 50 percent (range 1–93%), and median work saved over sampling to achieve 95 percent recall (WSS@95) was 54 percent (range 33–90%). Data extraction tools (9 studies) showed highly variable performance, with the percentage of data correctly extracted compared to manual extraction ranging from 0 to 99 percent (median 10%). Finally, tools used for risk of bias assessment (7 studies) showed modest agreement with human reviewers, with Cohen’s weighted kappa ranging from 0.11 to 0.48 (median 0.16). Discussion. Certain tools, particularly those for automatically identifying RCTs and prioritizing relevant abstracts in screening, show a high level of recall and precision, suggesting they are nearing widespread use with human oversight. However, other tools, such as those for searching and data extraction, show highly variable performance and are not yet reliable enough for semi-automation. This work revealed the importance of developing standardized evaluation frameworks for assessing the performance of machine learning and artificial intelligence tools in systematic review tasks. We did not assess the risk of bias or methodological quality of the included studies, which may affect the reliability and comparability of the reported performance outcomes. Additionally, the tools were evaluated in a variety of settings, tasks, and review questions, which introduces heterogeneity that makes direct comparisons across tools challenging. Lastly, the rapidly evolving nature of machine learning technologies means that our findings may quickly become outdated. Therefore, we have planned ongoing updates every 6 months.