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Portable machine tool precisely cuts chamfer on valve seat. With tool, delicate machining operation done without removing part to machine shop. Taken to part and used wherever pressurized air and electric power available. Plug and bushing nest in bore chamfered. They guide steady cutter rod as it cuts 15 degrees chamfer on top edge of bore.
Tool with stepped shoulders alines tubes for machining in preparation for welding. Alinement with machine tool axis accurate to within 5 mils (0.13mm) and completed much faster than visual setup by machinist.
Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.
One of the identified goals of EM3 is to implement virtual manufacturing by the time the year 2000 has ended. To realize this goal of a true virtual manufacturing enterprise the initial development of a machinability database and the infrastructure must be completed. This will consist of the containment of the existing EM-NET problems and developing machine, tooling, and common materials databases. To integrate the virtual manufacturing enterprise with normal day to day operations the development of a parallel virtual manufacturing machinability database, virtual manufacturing database, virtual manufacturing paradigm, implementation/integration procedure, and testable verification models must be constructed. Common and virtual machinability databases will include the four distinct areas of machine tools, available tooling, common machine tool loads, and a materials database. The machine tools database will include the machine envelope, special machine attachments, tooling capacity, location within NASA-JSC or with a contractor, and availability/scheduling. The tooling database will include available standard tooling, custom in-house tooling, tool properties, and availability. The common materials database will include materials thickness ranges, strengths, types, and their availability. The virtual manufacturing databases will consist of virtual machines and virtual tooling directly related to the common and machinability databases. The items to be completed are the design and construction of the machinability databases, virtual manufacturing paradigm for NASA-JSC, implementation timeline, VNC model of one bridge mill and troubleshoot existing software and hardware problems with EN4NET. The final step of this virtual manufacturing project will be to integrate other production sites into the databases bringing JSC's EM3 into a position of becoming a clearing house for NASA's digital manufacturing needs creating a true virtual manufacturing enterprise.
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For this study, we used Python and machine-learning tools to perform a comprehensive parametric and sensitivity analysis on a steam generator (SG) model. (The Python model was based on a previously completed MATLAB framework for the Holtec SMR-160 SG.) We investigated the influence of various input parameters (e.g., heat transfer coefficient [HTC], Nusselt number, and heat exchanger effectiveness) on the system’s output. With machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN), which was developed at Idaho National Laboratory, we were then able to perform an automated analysis of the SG inputs’ effect on the HTC. The analysis results give valuable insights into the performance and optimization of SG systems. We found the inlet mass flow rate (MFR) to have the greatest impact on the HTC, followed closely by the inlet temperature, and then pressure. Shifting of the input parameters causes the location of the maximum HTC along the SG length to change incrementally. The cold leg (CL) MFR was also found to impact the HTC magnitude as well as the location of the maximum HTC. At between 0.4–0.9 of the total SG length, the input parameters experience maximum impact on the HTC, leading us to suggest that sensors be efficiently placed on the SG so as to closely and effectively monitor thermal-hydraulic properties during reactor operation. We also found that the sensitivity data calculated manually agrees with the RAVEN – based data, confirming the same range of maximum sensitivity. However, the RAVEN-based analysis showed that cold leg pressure and hot leg temperature have a greater impact on the heat transfer coefficient than the mass flow rate, implying that a manual sensitivity study taking only two samples is not accurate.
The advancement of clean production using nanostructured materials in subtractive manufacturing processes has focused on replacing the use of liquid lubrication with solid lubricants coated to conventional cutting tools machining low carbon steel (~ 0.2 wt. % carbon). However, little is known about the wear mechanisms that dominate such tools when coated with functional graded nanostructured coatings. The present study follows an international standard for characterizing such wear (ISO 8688) but uses advanced measurement techniques such as x-ray fluorescence technology to measure the diffusion of chemical species from tool to workpiece and vice versa, that contributes to tool wear with the view to enhance the principles of cleaner production. The results show that when commercially-available tools coated with the appropriate functionally graded industrial coating, diffusion does not take place and creates an effective chemical barrier during machining. The experimental methods used in the study include machining workpieces using a CNC milling machine, tachometer for measuring spindle speed, dynamometer for measuring cutting forces, infra-red camera for measuring cutting temperatures, chemical species diffusion wear using x-ray florescence detector, and an optical microscope for measuring tool wear. Coupled with the physical measurement of wear, it is concluded that mechanical wear dominates the milling of a low carbon steel and that thermal properties of the coatings do not correlate with such wear. It is also concluded that flank wear as a function of volume of workpiece removed and machining duration (machining index) is a sound method for assessing the transition of the stages of wear during clean machining operations. The present study not only has implication for the design of better cutting tools, but also advances dry machining processes that eliminate the use of liquid lubricants making subtractive production processes cleaner. The main conclusions drawn from this study show that coated tools have a longer life under dry conditions compared to uncoated tools and that there is no correlation between coating properties and tool wear. In conclusion, diffusion of chemical species into the cut chips did not occur because of the lack of thermally induced chemical wear of the cutting tool and that wear is caused by the gradual erosion of the flank face due to the chip abrading the cutting tool.
Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.
Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.
With the rapid development of precision technologies, the demand of high-precision optical surfaces has drastically increased. These optical surfaces are mainly fabricated with computer controlled optical surfacing (CCOS). In a CCOS process, a target surface removal profile is achieved by scheduling the dwell time for a set of machine tools. The optimized dwell time should be positive and smooth to ensure convergence to the target while considering CNC dynamics. The total run time of each machine tool is also expected to be balanced to improve the overall processing efficiency. In the past few decades, dwell time optimization for a single machine tool has been extensively developed. While the methods are applicable to multi-tool scenarios, they fail to consider the overall contributions of multiple tools simultaneously. In this paper, we conduct a systematic study on the strategies for multi-tool dwell time optimization and propose an innovative method for simultaneously scheduling dwell time for multiple tools for the first time. First, the influential factors to the positiveness and smoothness of dwell time solutions for a single machine tool are analyzed. The compensation strategies that minimize the residual while considering the CNC dynamics limit are then proposed. Afterwards, these strategies are extended to the proposed multi-tool optimization that further balances the run time of machine tools. Finally, the superiority of each strategy is carefully studied via simulation and experiment. The experiment is performed by bonnet polishing a 60 mm × 60 mm mirror with three tools of different diameters ( i.e., 12 mm, 8 mm, and 5 mm). The figure error of the mirror is reduced from 45.42 nm to 11.18 nm root mean square in 13.28 min. Moreover, the measured polishing result well coincides with the estimation, which proves the effectiveness of the proposed method.
ORNL Manufacturing Demonstration Facility and Perisense worked on a low-cost solution for machining process monitoring to enable a digital factory. Enabling machine connectivity for digital factors is expensive for many small to medium manufacturers. The hypothesis for the research project is that a lowcost sensor suite, which can be retrofitted to a legacy machine tool or a new machine tool, can effectively provide real-time, in-situ sensing for manufacturing processes, which can enable improvements in process performance and efficiency. The Perisense sensor node was installed on the Haas TM1 machine with a current sensor and an accelerometer. Cutting tests were performed at different process parameters and sensor data was recorded. Results show a correlation of the current sensor to the spindle load. The accelerometer, mounted on the machine base, showed a correlation to the spindle speed as well as the spindle load. The sensor data can be used to monitor the machining process and provide insights on the machine spindle load utilization.
The bibliography contains citations concerning the design, testing, and application of servomotors. AC, DC, and brushless motor drives are discussed. Applications are examined, including use in hydraulic presses; teleprinters; machine tools; sewing machines; and servocontrol devices for instrumentation, robots, and aircraft control. Testing methods evaluate precision, vibration and vibration reduction, and stability of servomotors.
The bibliography contains citations concerning the design, testing, and application of servomotors. AC, DC, and brushless motor drives are discussed. Applications are examined, including use in hydraulic presses; teleprinters; machine tools; sewing machines; and servocontrol devices for instrumentation, robots, and aircraft control. Testing methods evaluate precision, vibration and vibration reduction, and stability of servomotors. (Contains 50-250 citations and includes a subject term index and title list.)
Here, this paper describes an iterative hybrid (additive + subtractive) manufacturing approach for a titanium alloy (Ti6Al4V) part using a laser hotwire directed energy deposition system (LHWDED) and a traditional four-axis milling machine tool. The term iterative hybrid manufacturing is used to described hybrid manufacturing where the additive and subtractive operations occur in multiple stages rather than sequentially. It is currently common to produce an entire part by sequential hybrid manufacturing by additively manufacturing (AM) an entire preform geometry that then requires post processing by another machine tool to create final part features. By contrast, a part produced by iterative hybrid manufacturing (IHM) does not produce the entire preform geometry in a single AM process. Instead, a portion of the entire preform geometry is manufactured by an AM process, then that portion is transferred to another machine tool which creates features in that portion, and then that machined portion is transferred back to the AM machine to complete another AM process. There is no limit to the number of iterations that an IHM process can have. IHM offers several advantages over sequential hybrid manufacturing such as the use of shorter and stiffer subtractive tooling, better access to part geometries that require subtractive processes, and the separation of the AM heat source from the subtractive machine tool. A titanium alloy demonstration part was successfully manufactured by IHM with three iterations using a shared pallet system between the AM machine tool and the subtractive machine tool.
The software provides machine learning analysis and visualization to detect patterns in epigenetic data, including conventional machine learning and statistical methods, and open-source packages like pyBigWig (https://github.com/deeptools/pyBigWig) for data processing. The software is written in python, it uses some python libraries.
We present MLstructureMining, a machine learning tool that identifies a structural model from an experimental pair distribution function. We show how the method can be used for structure analysis of both crystalline and nanocrystalline materials.
Freon-12 applied to tool cutting edge evaporates quickly, leaves no residue, and permits higher cutting rate than with conventional coolants. This technique increases cutting rate on Rene-41 threefold and improves finish of machined surface.
Single tool prepares a pipe end for precision welding by simultaneously performing internal machining, end facing, and bevel cutting to specification standards. The machining operation requires only one milling adjustment, can be performed quickly, and produces the high quality pipe-end configurations required to ensure precision-welded joints.