Monitoring Of Process Parameters Research Articles

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

Dielectric Spectroscopy to Improve the Production of rAAV Used in Gene Therapy

The insect cell-baculovirus expression vector system is an established method for large scale recombinant adeno-associated virus (rAAV) production, largely due to its scalability and high volumetric productivities. During rAAV production it is critical to monitor process parameters such as Spodoptera frugiperda (Sf9) cell concentration, infection timing, and cell harvest viabilities since they can have a significant influence on rAAV productivity and product quality. Herein we developed the use of dielectric spectroscopy as a process analytical technology (PAT) tool used to continuously monitor the production of rAAV in 2 L stirred tank bioreactors, achieving enhanced control over the production process. This study resulted in improved manufacturing robustness through continuous monitoring of cell culture parameters, eliminating sampling needs, increasing the accuracy of infection timing, and reliably estimating the time of harvest. To increase the accuracy of baculovirus infection timing, the cell growth/permittivity model was coupled to a feedback loop with real-time monitoring. This system was able to predict baculovirus infection timing up to 24 h in advance for greatly improved accuracy of infection and ensuring consistent high rAAV productivities. Furthermore, predictive models were developed based on the dielectric measurements of the culture. These multiple linear regression-based models resulted in correlation coefficients (Q2) of 0.89 for viable cell concentration, 0.97 for viability, and 0.92 for cell diameter. Finally, models were developed to predict rAAV titer providing the capability to distinguish in real time between high and low titer production batches.

Temperature Measurement on Tool Surfaces by Wear Resistant PVD Sensor Coatings

In manufacturing technology, sensors are important elements for monitoring process parameters and for recording data in the Industrie 4.0. Thin sensors manufactured by means of physical vapor deposition (PVD) offer a way to combine wear and corrosion protection on the one hand and the integration of a temperature sensor function on the other hand. For the analysis of the performance of PVD sensor coatings based on the thermoelectric effect, no standardized methods and procedures are state of the art. In this paper a measuring setup is presented, which allows reference measurements using a calibrated thermocouple and a thermographic camera besides the potential difference measurement of the sensor coating. Two measuring modes, which allow a continuous and a discontinuous measurement, are presented. The measuring methodology was evaluated using the PVD sensor coating Al2O3+Ni+NiCr+Al2O3. This multilayer sensor coating was deposited using an industrial coating unit and was tested with regard to the sensor properties. The deposition technology used for the sensor coating results in an interface without defects between the two sensor layers with a smooth transition. This provides a suitable electrical contact and a promising compound adhesion. The results show a suitable and detailed measurement of temperature and potential differences by means of calibrated measurement methods and the sensor coating. The measured results are reproducible and show a linear relationship between the potential difference and the surface temperature.

Anaerobic digestion performance and microbial community structures in biogas production from whiskey distillers organic by-products

By-product and residue management are significant economic and environmental considerations in whiskey production. Incorporation of sustainable production methods in the industry can reduce costs in the areas of energy consumption and waste disposal and lessen environmental impacts via reduced greenhouse gas emissions. Circular economy opportunities for waste valorisation are possible also, such as anaerobic digestion of process effluents with high organic loads to produce high quality biogas for use in generation of thermal energy, electricity and transport biofuel. In this study lab-scale anaerobic digestion of whiskey distillery waste streams was trialled at mesophilic and thermophilic temperatures. Biogas production was assessed while monitoring process parameters and the microbial communities at different stages of reactor operation were described. Syntrophic acetate-oxidation appeared to be a significant process at mesophilic temperatures but not at thermophilic operation. Both hydrogenotrophic methanogenesis and acetoclastic methanogenesis appear to be important methanation processes under both temperature regimes.

In-Line Predictive Monitoring Framework

Process monitoring, which is used to ensure the product quality in semiconductor manufacturing, develops a control chart and alerts engineers whenever a control limit is exceeded. However, a late alarm could generate defects or scraps, and thus, a prealarm is urgent to be developed. This study proposes an in-line predictive monitoring (ILPM) framework that uses process parameter monitoring (PPM) in the first phase and equipment parameter monitoring (EPM) in the second phase. PPM includes off-line training and in-line prediction, where we collect the first half of time-series data and predict the second half for in-line quality control. To maintain robustness and accuracy, the concept drift is used to update the ILPM model in real time; specifically, the EPM detects the loss of prediction by cumulative sum (CUSUM) control chart or detects the change of equipment parameters by Bayesian approach for developing retraining mechanism. An empirical study of a semiconductor manufacturer indicates that the proposed ILPM framework improved both the quality control and production capacity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Although some in-line monitoring approaches have been proposed for specific conditions, little research has been done to develop a prealarm method; in particular, this study proposes predictive monitoring by using data from multiple sensors (or status variable identifications, SVIDs) to predict one sensor in one process step of semiconductor manufacturer. The prealarm benefits the early troubleshooting and equipment capacity. This study also applies concept drift and equipment parameter monitoring (EPM) to identify the prediction model misalignment or equipment misalignment. In practice, root cause identification of the prediction error is critical and benefits the model retraining and equipment maintenance.

A comprehensive CHO SWATH-MS spectral library for robust quantitative profiling of 10,000 proteins

Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) is a data-independent acquisition (DIA) strategy that requires a specific spectral library to generate unbiased and consistent quantitative data matrices of all peptides. SWATH-MS is a promising approach for in-depth proteomic profiling of Chinese hamster Ovary (CHO) cell lines, improving mechanistic understanding of process optimization, and real-time monitoring of process parameters in biologics R&D and manufacturing. However, no spectral library for CHO cells is publicly available. Here we present a comprehensive CHO global spectral library to measure the abundance of more than 10,000 proteins consisting of 199,102 identified peptides from a CHO-K1 cell proteome. The robustness, accuracy and consistency of the spectral library were validated for high confidence in protein identification and reproducible quantification in different CHO-derived cell lines, instrumental setups and downstream processing samples. The availability of a comprehensive SWATH CHO global spectral library will facilitate detailed characterization of upstream and downstream processes, as well as quality by design (QbD) in biomanufacturing. The data have been deposited to ProteomeXchange (PXD016047).

A review of dispersion control charts for multivariate individual observations

A multivariate control chart is designed to monitor process parameters of multiple correlated quality characteristics. Often data on multivariate processes are collected as individual observations, i.e., as vectors one at a time. Various control charts have been proposed in the literature to monitor the covariance matrix of a process when individual observations are collected. In this study, we review the literature on control charts based on individual observations from multivariate continuous processes, where we find 30 relevant articles from the period 1987–2019. We group the articles into five categories. We observe that less research has been done on CUSUM, high-dimensional and non-parametric type control charts for monitoring the process covariance matrix. We describe each proposed method, state their advantages, and limitations. Finally, we give suggestions for future research.

Overview about cryogenic distillation control and safety approach for Isotopes Separation Facility (ISF)

Abstract Isotopes separation technologies are used in both Tritium Removal Facilities associated with CANDU reactors (to remove tritium from heavy water) and fusion applications (eq. ITER Isotope Separation System) to recover and enrich tritium for further operation. The expertise gained during design, manufacturing, building, commissioning and operation of the Cryogenic Distillation System (CDS) with proprietary four column design built at ICSI Rm. Valcea, could be applied for various tritium recovery applications, regardless of the type of front-end process used, Liquid Phase Catalytic Exchange or Combined Electrolysis and Catalytic Exchange. Cryogenic Distillation feed is a mixture of hydrogen isotopes which presents hazards in operation (explosion and tritium contamination), therefore the CDS of PESTD was designed to meet industrial and nuclear applicable standards. In this paper we present an overview of the CDS focusing on the process control and safety approach which meet specific conditions of Romanian codes and standards required for operation. However, specific information may be used later as reference for design consideration for other CDS. The cryogenic distillation process from ICSI Rm. Valcea is designed as four columns cascade inside a cold-box which acts as second barrier against hydrogen release and thermal insulation. The CDS process control is based on a Programmable Automation Controller (PACs), a Supervisory Control and Data Acquisition (SCADA) and specific low temperature field equipment and devices. The control room operator supervises the process using a specific designed Human Machine Interface (HMI) which permits to monitor process parameters, alarms acknowledgment and acts to adjust the process or override normal PACs control.

A NEW CONTROL CHART FOR PROCESS MONITORING BASED ON THE UNIFIED CAPABILITY INDEX

Control charts based on capability index for monitoring process parameters have received wide attention recently. In this paper, we proposed new control charts for online monitoring of the process mean and variability using a unified capability index approach. Control chart coefficients are given for constructing the unified capability index-based mean ( ) and standard deviation (S) control charts. Real life data is given to demonstrate the advantages of the proposed control charts over the standard mean ( ) and standard deviation (S) charts for process monitoring. The results of study showed that the proposed unified capability index-based control charts outperform the standard and S control charts and it is efficient for simultaneous monitoring of process stability and measuring the capability of the process to meet specification. Article DOI: https://dx.doi.org/10.20319/mijst.2019.51.99114 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Optimization and analysis of process parameters for flank wear, cutting forces and vibration in turning of AISI 5140: A comprehensive study

Abstract Monitoring of process parameters enhances the productivity with increased tool life. Variations of cutting forces and Vb have direct influence on VB. In this study the effects of cutting parameters and tool geometry on process variables were investigated with ANOVA. The correlation between Vb and cutting forces with VB were examined and RSM based multiple optimization was performed during turning of AISI 5140. ANOVA results indicated that measured variables are reliable (FC (92.5%), FF (87.5%), FR (74.5%) Vb (81.3%), VB (91.4%)) which shows their availability for correlation. According to graphical results, cutting force signals can be utilized to monitor VB and Vb signals provide supportive information about tool condition. RSM provided optimization with a high success rate (87.5%) and confirmed with an additional experiment. The paper differs from past studies by demonstrating that VB, Vb and cutting forces are correlated with each other, statistically reliable and convenient for multiple optimization.

On the Efficient Monitoring of Multivariate Processes with Unknown Parameters

Control charts are commonly used tools that deal with monitoring of process parameters in an efficient manner. Multivariate control charts are more practical and are of greater importance for timely detection of assignable causes in multiple quality characteristics. This study deals with multivariate memory control charts to address smaller shifts in process mean vector. By adopting a new homogeneous weighting scheme, we have designed an efficient structure for multivariate process monitoring. We have also investigated the effect of an estimated variance covariance matrix on the proposed chart by considering different numbers and sizes of subgroups. We have evaluated the performance of the newly proposed multivariate chart under different numbers of quality characteristics and varying sample sizes. The performance measures used in this study include average run length, standard deviation run length, extra quadratic loss, and relative average run length. The performance analysis revealed that the proposed control chart outperforms the usual scheme under both known and estimated parameters. An application of the study proposal is also presented using a data set related to Olympic archery, for the monitoring of the location of arrows over the concentric rings on the archery board.

About some automated processes in the production of dairy products

When processing milk and producing dairy products, the key factor is the control of the quality indicators of milk and manufactured products using automatic control systems of SAK with the use of microprocessors and microcontrollers. When milk is taken, automated plants allow you to determine the composition and quality of milk, including the content of somatic cells, mass fat content and protein fraction. The milk storage tanks are equipped by the local automatic systems. The technological processes of milk normalization are associated with the adjustment of its parameters. The functional diagram of automatization of the milk normalization process in the flow involves the use of a microprocessor controller. The composition of the automated production area of drinking milk includes information sensors on the status of process parameters, digital instruments for monitoring process parameters. The process of producing liquid starter cultures and bacterial concentrates involves the possibility of changing the productivity of the cultivator. Automatization does not include the supply of milk to the process equipment, the transfer of equipment from one technological operation to another and many others. Further automatization of technological processes in dairy production should be based on the extensive use of robotics and flexible robotic lines.

Modified Hardware Security Algorithms for Process Industries Using Internet of Things

Embedded devices used in process industries are vulnerable to a variety of attacks due to its large number of deployments to reduce the measurement and process complexity. The security threats increase due to the wide usage of embedded systems, wireless networks, and the internet for process monitoring and control applications. Attackers will gain the process information from the embedded devices and wireless networks through a variety of security attacks and modify the critical process parameters of the final product. It is essential to protect the embedded system with wireless networks from unauthorized access. The novelty of this proposed work is the implementation of a modified security algorithm along with the dedicated hardware key using embedded system which allows secure transmission and monitoring of process parameters through the internet. This proposed algorithm generates large key size of 1024-bit for asymmetric encryption and 256-bit key size for symmetric encryption. This encryption algorithm is implemented using raspberry pi which consists of the ARM Cortex processor. This proposed security algorithm monitors process data through the internet strengthens the data security by generating large key size along with usage of dedicated hardware key. The authorized only hold this hardware key to compile the security algorithm and to access the process data in encrypted and decrypted form. It achieves a data transfer rate of 300 Megabits per second and more than 95 percent efficiency. It ensures secure monitoring and transmission of sensitive process information through the internet. It also provides cost-effective solutions in protecting the process equipment and plant information from unauthorized access as well as safety to plant operators.

Robust adaptive exponentially weighted moving average control charts with applications of manufacturing processes

Statistical process control (SPC) has its own importance in the field of quality control. In SPC, control charts are significant tools to monitor process parameters, and exponentially weighted moving average (EWMA) control chart is one such tool. It is a memory-type chart, which is used to target mainly the smaller shifts in the process parameters. Adaptive EWMA (AEWMA) scheme is used to identify small as well as large shifts. EWMA and AEWMA are based on the assumption of normality, which is quite hard to find in practice, and there are many situations where outliers are occasionally present. In the current study, we have proposed four robust adaptive EWMA schemes for monitoring process location parameter. We have investigated their performance under uncontaminated normal and contaminated normal environments. We have carried out comparisons amongst different competing charts based on average run length (ARL), standard deviation of run length (SDRL) and different percentiles of run length distribution. Two examples related to manufacturing processes are also provided for practical implementation of the proposed schemes.

Evaluation and validation of an inline Coriolis flowmeter to measure dynamic viscosity during laboratory and pilot-scale food processing

Abstract Inline process analytical technology tools are increasingly employed in industry as they facilitate real time process parameter monitoring and optimization of product quality. Use of an inline viscometer in milk powder manufacture allows greater process control by monitoring the dynamic viscosity of the process concentrate pumped to the spray drier. In this study, an inline Promass I300 was used to measure the dynamic viscosity of both a Newtonian fluid (10–60% w/w sucrose solutions) and a non-Newtonian fluid (10–40% w/w skim milk concentrate) at laboratory scale at 25 °C. Validation of the instrument for measurement of a non-Newtonian fluid was completed at pilot-scale under similar operating conditions. Coefficient of determination (R2 = 0.99) was obtained between inline and offline viscosity measurements for both Newtonian and non-Newtonian fluids investigated. This study demonstrated the potential of an inline Coriolis flowmeter for rapid and accurate measurement of dynamic viscosity during processing of dairy streams. Industrial relevance Control of milk concentrate viscosity during the manufacture of milk powder is critical to reduce energy consumption, reduce fouling and meet powder functional properties e.g. dispersability, flowability, particle size and powder hydration. The use of inline instruments for continuous monitoring of viscosity offers economic benefits to the producer as it can contribute to reduction in commonly encountered process issues such as fouling, blocking of nozzles and evaporator, which can result in process downtime and in extreme cases, product rework. Inline instruments can thus be employed to improve process control and reduce production waste during the manufacture of dairy powders.

Overview of the State of the Art in the Production Process of Automotive Wire Harnesses, Current Research and Future Trends

Today’s automotive wire harness manufacturing process involves a high volume of manual work. However, current and future application requirements like miniaturization of electronic components, monitoring of process parameters, increasing demand of process documentation and rising wages call for a higher degree of automation.This paper presents the state of the art in wire harness assembly, reviews currently available special purpose machinery and equipment as well as their limitations and highlights the challenges of automation. It discusses potentials for automation, current innovations and research and concludes with an overview of current trends in the automotive industry.

Towards intelligent and sustainable production systems with a zero-defect manufacturing approach in an Industry4.0 context

The paper addresses intelligent and sustainable production achieved through combination and integration of online predictive maintenance, monitoring of process parameters and continuous quality control of both input materials and output from the process. This enables production systems, within both manufacturing and process industries, to move towards zero-defect manufacturing. Such a zero-defect manufacturing approach allows for earlier identification of problems or issues, which will or already negatively affect the output. The paper outlines the first part of the second cycle of an action research effort at Gestamp HardTech AB in Sweden, whose objective is to keep its position as a world-leading provider of press-hardened vehicle parts. In order to fully implement the zero-defect manufacturing approach, 4-6 action research cycles are expected to be needed in order to iteratively refine the approach. During the first cycle, various methods and solutions for some of the individual issues/problems have been conceptualized, realized and initially tested. The selected design criteria for the action research efforts were: simplicity, low cost, robustness, high-quality output and future-proofing. The result from the research in the second cycle so far is an action plan for the technical change and a set of challenges/problems which need additional investigation.

An Assorted Design for Joint Monitoring of Process Parameters: An Efficient Approach for Fuel Consumption

Due to high fuel consumption, we face the problem of not only the increased cost, but it also affects greenhouse gas emission. This paper presents an assorted approach for monitoring fuel consumption in trucks with the objective to minimize fuel consumption. We propose a control charting structure for joint monitoring of mean and dispersion parameters based on the well-known max approach. The proposed joint assorted chart is evaluated through various performance measures such as average run length, extra quadratic loss, performance comparison index, and relative average run length. The comparison of the proposed chart is carried out with existing control charts, including a combination of X and S, the maximum exponentially weighted moving average (Max-EWMA), combined mixed exponentially weighted moving average-cumulative sum (CMEC), maximum double exponentially weighted average (MDEWMA), and combined mixed double EWMA-CUSUM (CMDEC) charts. The implementation of the proposed chart is presented using real data regarding the monitoring of fuel consumption in trucks. The outcomes revealed that the joint assorted chart is very efficient to detect different kinds of shifts in process behaviors and has superior performance than its competitor charts.

Advanced algorithmic model for poly-optimization of biomass fuel production from separate combustible fractions of municipal wastes as a progress in improving energy efficiency of waste utilization

Usage of generated fuel components from segregated municipal waste groups in combined combustion processes with traditional fuels results in significant savings by partial replacement of fossil fuels and the reduction of environmental inconvenience that arises directly from the migration of waste to the environment. This article develops technological process of fuel components production which is controlled by advanced embedded SCADA control systems, providing monitoring of process parameters, process visualization and implementation, with the help of logic controllers of freely programmable PLC and complex control algorithms (including adaptive, predictive and inferential control algorithms) by implementation of the polymorphic model of fuel components obtaining process multi-stream optimization.

Analysis of Biomass Blend Co-Firing for Post Combustion CO2 Capture

The correct conduction of the CO2 capture process in coal-fired power plants with the use of monoethanolamine (MEA) requires constant process parameter monitoring and ensuring a specific flue gas chemical composition. One of the most common problems in these types of installations is the progressive corrosion and degradation of the valuable solvent. Despite the established reduction levels of oxygen and impurities entering into irreversible reactions with the absorber, the flue gas composition may change as a daily and annual function of time. The article presents a detailed analysis of the flue gas components that have the greatest influence on carbon dioxide capture installation technical safety, i.e., SOx, NOx, O2, and fly ash. The analysis was based on the results of experiments conducted at the Jaworzno III Tauron Wytwarzanie SA Polish coal power plant. The results show a significant influence of the flue gas desulfurization (FGD) process on MEA oxidative degradation. The amount of oxygen in flue gas during biomass and coal blend co-firing was nearly twice as low compared to pure coal combustion. Differences were also observed in the amounts of gas impurities with relation to the time of year and time of day of power plant operation.