Process Monitoring System Research Articles

Selective laser melting of Ti-6Al-4V: Comparing μCT with in-situ process monitoring data

Additive Manufacturing (AM) is increasingly used for the fabrication of metallic components used in the medical device, aerospace and automotive industries. With the wider adoption of AM in these sectors, there is an increased demand for the in-situ process monitoring of the build process. This study investigates the performance of a photodiode based, co-axial in-situ process monitoring (PM) system, during the Selective laser melting (SLM) of Ti6Al4V alloy parts. The PM system measures the optical and thermal emissions created by the meltpool, as well as the intensity and stability of the laser during the SLM process. The process monitoring software then creates a 2D or 3D representation of the part, based on the signal intensity recorded.The Ti6Al4V alloy parts were manufactured containing internal cavities, with diameters/width’s in the range of 200–600μm, while varying the input energy between 32.9 and 131.6J/mm3. A close correlation was established between the laser monitoring photodiode signal intensity and the laser energy input. Along with this, an increase in signal intensity recorded, by the meltpool monitoring photodiode, was observed when the first capping layer above a cavity, was processed by the laser. Further to this, it was shown that only the first layer was influence by the overhang, with the signal generated by the layers directly above this remaining unaffected. In addition to providing data on the laser energy during the build process, the PM system also provided valuable information regarding the intensity of the meltpool. A comparison was made between the dimensional measurements obtained using PM software, with those obtained through CT scanning of the parts, post build. It was found that for the 600μm cavities that the measurements were, at best, within 1.7% of each other. This closeness of such measurements however decreased very significantly as the size of the cavities decreased, with a variation for example, of up to 32% been obtained, for 400μm cavities.

Improving and monitoring the magnetic pulse welding process between dissimilar metals

Conventional fusion welding of dissimilar metals is often limited due to the different thermo-physical properties of the joining partners. In consequence, brittle intermetallic phases (IMC) can occur. Utilizing a pressure welding process like magnetic pulse welding (MPW) reduces the risk of IMCs significantly. Furthermore, this welding process has an outstanding short process duration in the range of a few microseconds, which makes it predestined for mass production. At the same time, this advantage challenges the process observation, inline-quality assurance hardware, and the design of the tool coils. The paper presents two strategies for reducing the energy input during MPW to increase the tool coil lifetime. The first approach, the introduction of a reactive nickel interlayer between steel and aluminum, leads to a significant welding energy reduction. Compared to aluminum samples joined by laser welding, the load-bearing capability of the resulting hybrid MPW driveshaft samples is higher in static torsion tests and similar in cyclic tests. The second approach is based on a novel process monitoring system that helps to analyze the characteristic light emission. The capability of the process monitoring system is presented on the example of a MPW-joined multimaterial part made of stainless steel, aluminum, and copper.

Development of a low‐cost food color monitoring system

AbstractThe colorimetric analysis of food has gained much popularity in the last few decades. The present study reports the development of a low‐cost food color quality testing and process monitoring system. The proposed device consists of a hardware and a software (Color Magic) combination, which may allow the hardware to be used either for quality testing or process monitoring applications. In the quality‐testing mode, the software captures the image of the food products through the imaging system. Subsequently, the software processes the acquired image and computes color parameters in RGB (red, green, blue), CIELAB, and HSI (hue, saturation, intensity) color spaces. The software also synthesizes and displays the perceived color information in the display panel. The datalog of the sample color information can be sent to the user. Further, a separate software (Color Process), which is installed in a central server, was developed to implement a wireless star network topology for multi‐node process monitoring. The “Color Process” software allows users to acquire color information from multiple hardware. The software monitors the Hue from all the devices. It alerts the user via email if the Hue is beyond the range in any of the nodes. Finally, the device was tested for quality testing and process monitoring applications using colored placards and apple slices. The implementation of the wireless sensor network (WSN) in designing the multi‐node process monitoring makes the proposed device a unique system. This further would allow the device to be used for process monitoring at multiple remote locations.

Research and Analysis of Sports Training Real-Time Monitoring System Based on Mobile Artificial Intelligence Terminal

With the rapid development of artificial intelligence, related technologies and applications come into being, and industries based on artificial intelligence are booming, among which image recognition and target tracking technologies are widely used in various fields, especially in the fields of security monitoring and augmented reality. In this paper, combined with the characteristics of athletes, based on mobile artificial intelligence terminal technology, the C/S mode of athlete training process monitoring system is developed and designed, which uses GPS to obtain the real-time position information of athletes and provide real-time guidance for athletes. In order to reveal the changing rules of various indexes of athletes in training state, the author makes synchronous tracking analysis from the aspects of individual sports function characteristics of athletes, training plan arrangement of coaches, brain function state, routine physiological and biochemical indexes, nutrition regulation, and injury conditions.

The JSNS2 data acquisition system

The JSNS2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) experiment aims to search for neutrino oscillations over a 24 m short baseline at J-PARC. The JSNS2 inner detector is filled with 17 tons of gadolinium(Gd)-loaded liquid scintillator (LS) with an additional 31 tons of unloaded LS in the intermediate γ-catcher and an optically separated outer veto volumes. A total of 120 10-inch photomultiplier tubes observe the scintillating optical photons and each analog waveform is stored with flash analog-to-digital converters. We present details of the data acquisition, processing, and data quality monitoring system. We also present two different trigger logics which are developed for the beam and self-trigger.

A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation

In this study we bridge traditional standalone data-driven and knowledge-driven process monitoring approaches by proposing a novel hybrid framework that exploits the advantages of both simultaneously. Namely, we design a process monitoring system based on a data-driven model that includes two different data types: i) “actual” data coming from sensor measurements, and ii) “virtual” data coming from a state estimator, based on a first-principles model of the system under investigation. We test the proposed approach on two simulated case studies: a continuous polycondensation process for the synthesis of poly-ethylene terephthalate, and a fed-batch fermentation process for the manufacturing of penicillin. The hybrid monitoring model shows superior fault detection and diagnosis performances with respect to conventional monitoring techniques, even when the first-principles model is relatively simple and process/model mismatch exists.

Monitoring Chemistry In Situ with a Smart Stirrer: A Magnetic Stirrer Bar with an Integrated Process Monitoring System.

Inspired by the miniaturization and efficiency of the sensors for telemetry, we have developed a device that provides the functionalities of laboratory magnetic stirring and integrated multisensor monitoring of various chemical reaction parameters. The device, called "Smart Stirrer", when immersed in a solution, can in situ monitor physical properties of the chemical reaction such as the temperature, conductivity, visible spectrum, opaqueness, stirring rate, and viscosity. This data is transmitted real-time over a wireless connection to an external system, such as a PC or smartphone. The flexible open-source software architecture allows effortless programming of the operation parameters of the Smart Stirrer in accordance with the end-user needs. The concept of the Smart Stirrer device with an integrated process monitoring system has been demonstrated in a series of experiments showing its capability for many hours of continuous telemetry with fine accuracy and a high data rate. Such a device can be used in conventional research laboratories, industrial production lines, flow reactors, and others where it can log the state of the process to ensure repeatability and operational consistency.

Design of a Multi-sensor Monitoring System for Additive Manufacturing Process

Additive manufacturing (AM) technology is becoming increasingly feasible in industrial applications. Although AM systems continue to improve, the lack of repeatability and stability of these technologies remains an obstacle to industrial breakthroughs. Thus, AM process monitoring equipment has to be set up to supervise product quality and detect defects. This study first reviews the various categories of defects, relevant AM process signatures, and optical monitoring methods of powder bed fusion processes proposed so far. Then, according to the detection requirements, an optical process monitoring system based on multiple sensors is proposed, which is equipped with a white light imaging channel, an infrared imaging channel, and a polarization imaging channel. The aberrations can be optimized using Zemax software by appropriately selecting the optimization function operands. Thus, the design requirements for the multi-sensor monitoring system for the AM process can be achieved.

Machine Learning for Process Monitoring Systems

AbstractMachine learning (ML) as part of artificial intelligence will be a key technology of the future in many areas of our life, so also in laser materials processing. This data‐driven approach will use digital twins – digital equivalents of real parts covering the whole process chain. Optical process monitoring systems observe process steps in real time yielding in a digital process twin as part of the twin. ML will enable us to extract information from data, assisting experts in answering questions from “Ship the part?” to “How I can produce a part with a lot size of one at a given quality level?”

Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing

Machine chatter is still an unresolved and challenging issue in the milling process, and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement. In this paper, two indicators of chatter detection are investigated. One is the real-time variance of milling force signals in the time domain, and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain. Then, a novel classification concept for vibration condition, called slight chatter, is proposed and integrated successfully into the designed multi-classification support vector machine (SVM) model. Finally, a mapping model between image and chatter indicators is established via a distance threshold on the image. The multi-SVM model is trained by the results of three signals as an input. Experiment data and detection accuracy of the SVM model are verified in actual machining. The identification accuracy of 96.66% has proved that the proposed solution is feasible and effective. The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring.

Green High‐Power Disk Lasers and Industrial Applications

AbstractIn the publicly‐funded ProLasKu project, the laser manufacturer Trumpf has developed a high‐power laser with a wavelength of 515 nm. Substantial progress in the industrial applications of green lasers has been achieved in a cooperation between the Technical University of Munich and the companies Siemens and Continental. In addition, a camera‐based system for process monitoring and quality assurance in real‐time based on image processing methods has been developed.

Automated measurements in process monitoring system in bearing production

The methodological foundations of the use of automated measuring systems in the monitoring system of the technological process of manufacturing parts of bearings are considered. In addition to the means used to control the accuracy of manufacturing of bearing rings, the use of vibration measuring instruments to assess the dynamic quality of each grinding machine, automated eddy-current quality control devices for the surface layer of parts, and an active dimensional control system, the functions of which are expanded by controlling the speed of the removed stock, are considered. All information is processed in a monitoring system and used to improve the quality of bearing parts.

Design of Product Monitoring System Using Internet of Things Technology for Smart Manufacturing

The application system in the industrial era 4.0 is currently growing rapidly. One of them is the implementation of smart manufacturing in the industrial sector. In smart manufacturing, production processes can be monitored automatically and real time, starting from material selection, then the production process to the planned product. In this study discussed the design of a monitoring system on smart manufacturing based on internet of things technology. Smart technology is implemented on material scans automatically based on color sensors, then from the material it will also be known that with these materials produce certain products and require any material. In addition, the system will also automatically detect through the initial material to find out which processes will be passed. The methods used include literature studies, system requirements analysis, system design and system testing. The results of this study are in the form of a hardware system design and display of production process monitoring system applications. In the system that is made also a notification is displayed if a bottle neck occurs in the production process.

Correlating in-situ process monitoring data with the reduction in load bearing capacity of selective laser melted Ti–6Al–4V porous biomaterials

Selective Laser Melting allows for the creation of intricate porous structures, that possess favourable biological properties. These structures are known as porous biomaterials. The focus of this paper is to evaluate the use of an in-line photodiode based process monitoring system, for the monitoring of the operational behaviour of the laser, and to correlate this with the resultant parts mechanical performance.In this study the production scale Renishaw 500M was used to produce porous structures, using Ti–6Al–4V feedstock powder. During the process, a co-axial process monitoring system was utilised to generate data relating to both the meltpool and the operational behaviour of the laser. An advanced scanning technique was used to produce the structures, whereby the laser parameters determine the strut dimensions. In this study, the laser input energy was reduced by 33%, 66% and 100%, at specific layers within the structures. Computer Tomography and Scanning Electron Microscopy was utilised to characterise the affected struts within the structures, while quasi-static compression testing was used to determine the structure's mechanical properties.It was demonstrated that as the level of input energy decreased and the number of affected layers increased, a corresponding decrease in the load bearing capacity of the structures occurred. With the structures experiencing a significant loss in strength also exhibiting a change in the failure mode during compression testing. Data generated during the processing of such structures was compared to the data generated during the processing of control structures, with the difference between the two been calculated on a layer-by-layer basis. A clear correlation was demonstrated between the total level of deviation between the two signal sets and a reduction in the load bearing capacity of the structures. This indicates that by comparing build data to a benchmark data set, valuable information relating to the structural integrity of the porous structures can be obtained.

A Development of Offshore plant Piping Process Monitoring System Based on 3D CAD Model

A Development of Offshore plant Piping Process Monitoring System Based on 3D CAD Model

Sensitivity analysis of drilling parameters in rock rotary drilling process based on orthogonal test method

Drilling exploration is the main method for geological studies of rocks and soil. Selecting reasonable drilling parameters is important for ensuring the safety and economic benefits of a project. According to the force limit equilibrium of rock during rotary drilling and using the energy equilibrium method, the relationship between the vertical drilling efficiency η and drilling parameters such as the speed of rotation, thrust force, and rotation torque were deduced in this study. Through improvements in the drilling process monitoring (DPM) system, it was possible to quickly, efficiently, and quantitatively obtain the drilling parameters during rock drilling. An in situ drilling test of granite using the improved DPM system showed the sensitivity of the drilling efficiency to each drilling parameter via an orthogonal test. The results indicated that the optimal drilling efficiency (η = 13.2%) was achieved under the following test conditions: A rotation speed of 128 r/min, thrust force of 788 kPa, and a rotation torque of 64 N·m. the influence of the thrust force on the drilling efficiency was extremely significant, the sensitivity of this parameter was high, and the weight value of the thrust force had a 57.87% influence among the others. Furthermore, the influence of the rotation torque on the drilling efficiency index was significant, the sensitivity to this parameter was neither high nor low, and its weight value was 36.82%. In contrast, the influence of the rotation speed on the index was insignificant, sensitivity to this parameter was low, and its weight value was 5.31%. The research methods and results presented herein can serve as references for the optimum design of drilling construction and the reasonable selection of drilling parameters.

A comprehensive approach to karst identification and groutability evaluation – A case study of the Dehou reservoir, SW China

Yunnan Province in SW China has one of the most developed karst terrains in China and has been historically affected by sinkholes and other instability phenomena caused by unpredictable cavity formation and well-developed underground hydraulic conduits. During construction of a karst seepage control reservoir project (grouting of a 4.6-km long anti-seepage curtain) in Yunnan province, some typical detection methods (electromagnetic and water injection tests) failed to map the complex geo-structural karst zones clearly, resulting in considerable grout loss and excessive leakage during the grouting phase of this project. To deal with this limitation, a comprehensive approach, including a new drilling process monitoring system, borehole electrical resistivity tomography, borehole sonic logging, and water injection tests, is employed to identify the general characterization, geometry, and spatial distribution of karst zones along the anti-seepage curtain. Additionally, a deep belief network (DBN) model replaced the empirical relationships typically used to predict the grouting quantity for groutability evaluation in karstified areas. The DBN algorithm is trained by data collected using this comprehensive approach (rate of penetration, rock permeability, resistivity, and P-wave velocities) and two previous grouting quantities as input data. The lessons learned in this case study permit us to define an effective comprehensive approach for delineating the general characterization of karst zones between two boreholes and locations of potential conduits for grout flow through the borehole. The proposed DBN model can predict the grouting quantity for karst regions, and the accuracy of prediction is greatly affected by previous grouting quantity rather than the collected parameters.

Slag-foaming phenomenon originating from reaction of titanium-bearing blast furnace slag: effects of TiO2 content and basicity

ABSTRACTThe foaming behaviour originating from the reduction of iron oxide in molten CaO–SiO2–MgO–Al2O3–TiO2 slag was studied at 1500°C with the aid of a real-time foaming process monitoring system. The effect of TiO2 content and binary basicity on the slag foaming were investigated. It was found that the TiO2 content has a significant influence on the foaming degree, while the basicity of the slag has a smaller influence. The foam-generation time, foam duration, and foam-elimination time greatly increased with increasing TiO2 content, while they slightly increased with increasing basicity. Furthermore, the source of gas and the effect of physical properties of the slag on the foaming behaviour were also discussed. This study can provide a guideline for the utilisation of high-ratio titanium-bearing magnetite ore in the blast-furnace iron-making process, which is seriously affected by the slag-foaming problem.

Remote machine mode detection in cold forging using vibration signal

Detecting machine mode can allow smarter process monitoring systems and more accurate fault prediction without external information. A remote machine monitoring system was installed on a cold heading machine in the factory of an automotive fastener manufacturing company. The process monitoring system was non-intrusive and was designed to measure vibration. The end goal of the study was to predict tool wear, but part classification was required first, as the machine produced multiple parts which produced different vibration signals. The collected vibration data was processed using wavelet transform and passed through a convolutional neural network for part classification. This method achieved part classification accuracy as high as 86% when looking at data for a 1-month period. The results show that meaningful classification features are present in the data using the process monitoring system as designed.

Evaluation of the Process Performance in Wire EDM Based on an Online Process Monitoring System

The wire EDM process can be evaluated according to various criteria. In addition to the generated component quality, the cutting rate is a key performance index for the main cut. Process stability is another evaluation factor. It is defined by a resulting constant feed rate during machining and a low probability of occurrence of wire breakages. In several studies, the discharge distribution has been analyzed to predict a wire breakage. Due to the high discharge frequency, the analysis of the electrical signals has been conducted only for very short time periods. A quantitative evaluation of the continuous wire EDM process could therefore not be carried out. By implementing a real time process monitoring system, the detection of single discharges and the allocation to normal and abnormal discharges can be conducted. By using a FPGA (Field Programmable Gate Array), the characteristic discharge parameters can be analyzed in real time. The monitoring system stores only characteristic numbers and thus a continuous process can be recorded without generating a large data volume.