Control Of Process Variables Research Articles

Bayesian enhanced EWMA scheme for shape parameter surveillance in Inverse Gaussian models

Bayesian control charts (BCC) are increasingly recognized as highly effective statistical schemes to investigate manufacturing processes and effectively control process variability. This technique is mainly adept at handling uncertain parameters in the manufacturing sector. This study sets the surveillance threshold for the Inverse Gaussian Distribution (IGD) shape parameter. It develops several non-informative exponentially-weighted moving averages (EWMA) control charts (CCs) using different loss functions (LFs). The proposal and existing charts are assessed using different individual performance measures. The non-informative Bayesian (NIB) charts developed in this study are evaluated across various sample sizes. The simulation study determines that the proposed non-informative Bayesian EWMA CCs are more effective than conventional classical EWMA charts. These proposed EWMA chart techniques excel in identifying faults in the shape parameter and perform better than their classical counterpart in swiftly identifying shifts. Additionally, the procedure is applied to real data from the manufacturing industry, and the results validate the conclusions drawn from the simulation outcomes.

Surrogate gradient methods for data-driven foundry energy consumption optimization

In many industrial applications, data-driven models are more and more commonly employed as an alternative to classical analytical descriptions or simulations. In particular, such models are often used to predict the outcome of an industrial process with respect to specific quality characteristics from both observed process parameters and control variables. A major step in proceeding from purely predictive to prescriptive analytics, i.e., towards leveraging data-driven models for process optimization, consists of, for given process parameters, determining control variable values such that the output quality improves according to the process model. This task naturally leads to a constrained optimization problem for data-driven prediction algorithms. In many cases, however, the best available models suffer from a lack of regularity: methods such as gradient boosting or random forests are generally non-differentiable and might even exhibit discontinuities. The optimization of these models would therefore require the use of derivative-free techniques. Here, we discuss the use of alternative, independently trained differentiable machine learning models as a surrogate during the optimization procedure. While these alternatives are generally less accurate representations of the actual process, the possibility of employing derivative-based optimization methods provides major advantages in terms of computational performance. Using classical benchmarks as well as a real-world dataset obtained from an industrial environment, we demonstrate that these advantages can outweigh the additional model error, especially in real-time applications.

Deep-learning based in-situ micrograph analysis of high-density crystallization slurry using image and data enhancement strategy

Process analytical technology (PAT) plays a crucial role in optimizing crystalline powder product qualities, improving process stability, and reducing experimental costs by enabling real-time monitoring and control of process variables during the crystallization process. In-situ process imaging and analysis have gained significant attention due to their capacity to provide abundant information through images, leading to remarkable advancements in image processing technology, particularly with the support of artificial intelligence. However, the performance suffers when processing high-density slurry images due to challenges such as defocusing, overlapping, background and crystal edges blurriness, and inconsistencies in crystal scales. To address these challenges and explore research strategies based on deep learning for segmenting and analyzing high slurry density images effectively, this study constructed a specific dataset that contains high-density slurry crystal images which is well-labeled, and introduced state-of-the-art neural networks including YOLOv8, U2-net, and Mask R-CNN combined with image and data enhancement strategies. Comparative analysis revealed that YOLOv8 outperformed Mask R-CNN and U2-net by capturing multi-dimensional information in high-density crystal slurry scenarios. Finally, based on the segmentation results of the practical taurine crystallization process using proposed strategy, 1D crystal size, aspect ratio along with 2D size distributions were extracted for further evaluation of crystallization kinetics and crystal properties accurately and quickly.

Assessment of process-induced cracks in hot-working operations using crack susceptibility index based on plastic instability criteria

A processing map representing the efficiency of power dissipation as a function of the temperature and strain rate has been used as a guide to depict the hot workability of metallic alloys. However, it has limitations in indicating the local positions where process-induced cracks may occur during hot-working operations. A process-induced crack is formed during hot deformation because the plastic instability associated with crack formation is more favorable than normal flow under a given set of process conditions. This paper proposes a novel approach to assess local cracks using the crack susceptibility index (CSI), which is based on Ziegler's plastic instability criteria. This solution provides useful information regarding the effect of each process parameter on the workability required for high-temperature processes. Lowering the CSI value by controlling process variables, such as the temperature and strain rate, is recommended for a design free from process-induced cracks. More importantly, a coded finite element subroutine coupled with the CSI can help visualize local positions where cracks may be produced during a hot-working operation. For experimental validation, a Gleeble test was performed on Ti6Al4V specimens containing four notches with different depths. The successful application of the CSI to the process design of shop-floor unmanned aircraft components helped confirm the applicability and validity of this study.

In-process approach for editing the subsurface properties during single-lip deep hole drilling using a sensor-integrated tool

Single-lip deep-hole drilling (SLD) is characterized by high surface quality and compressive residual stress in the subsurface of the drill hole. These properties depend significantly on the thermomechanical conditions in the machining process. The desired subsurface properties can be adjusted in-process via process monitoring near the cutting zone with a sensor-integrated tool and closed loop control when the thermomechanical conditions are maintained in the optimum range. In this paper, a method is presented to control the thermomechanical conditions to adjust the properties in the subsurface. The process model integrated in the controller is implemented as a soft sensor and takes into account the residual stresses, the roughness, the hardness and the grain size in the surface as well as in the subsurface depending on the process control variables, such as the feed rate and cutting speed. The correlation between the process variables, the thermomechanical conditions of the cutting process and the subsurface properties are investigated both experimentally and by finite element (FE) simulations. Within a justified process parameter range, characteristic fields for the soft sensor were established for each property. In addition, the procedure of controller design and the employed hardware and interfaces are presented.

The role of the six sigma method in controlling and improving product quality

The Six Sigma method has been proven effective in industry for reducing process variability, increasing customer satisfaction, and optimizing operational efficiency. This research aims to analyze the role of the Six Sigma method as a systematic approach in controlling and improving product quality. This research uses qualitative methods with descriptive methods. The results of this research reveal that the application of the Six Sigma Method in controlling and improving product quality has a significant positive impact. Controlling process variability, focusing on customer satisfaction, and using statistical methods in data analysis have succeeded in increasing product consistency, building customer loyalty, and providing a strong basis for fact-based decision making. Implementation of the DMAIC concept provides a structured framework for continuous improvement, while cross-functional employee involvement creates a work environment that supports collaboration and innovation.

Shotcrete 3D Printing ‐ Effect of material‐process interaction on the global and local material density

AbstractShotcrete 3D Printing is a 3D printing process in which the concrete is applied layer by layer using a wet mix process. In the print process, a large number of different process as well as material parameters can be varied, which also influence the hardened concrete properties. A systematic understanding of the interaction between material and process is required to define limits for material and process control variables with respect to the resulting component quality. Therefore, in this paper we present results on the effect of concrete volume flow (0.4; 0.8 m3/h), air volume flow (30; 40; 50 m3/h) and accelerator dosage (0; 3; 6 % by weight of cement) on global density, local density distribution as well as flexural strength. The results show that the combination of an increasing concrete volume flow, a decreasing air volume flow and an increasing accelerator dosage lead to a decrease in global density and flexural strength. A high correlation between the global density and flexural strength can be demonstrated despite the layer‐by‐layer fabrication. In addition, an inhomogeneous density distribution over the cross section is shown. Finally, the underlying mechanisms are discussed regarding the influence of the material and process parameters.

Dynamic optimal control of flow front position in injection molding process: A control parameterization-based method

High dynamic and precise control of process variables within permissible limits is the key to high product quality in the injection molding industry. Among these variables, the injection flow rate of molten polymer is particularly significant. In this paper, an effective optimal open-loop and state feedback controller design method is designed to realize the optimal tracking control of the melt polymer flow front position flow of melt polymer fluid during injection molding. To this end, the dynamic model of the filling process is firstly established, taking into account the nonlinearity of hydraulic control system and the effect of molten polymer flow on the dynamics of the injection ram, and then the dynamic optimal tracking control problem of flow front position of molten polymer in injection molding machine is proposed. Then, an open-loop controller and a multi-level feedback control law are well designed, wherein the control signal and the control feedback kernels are parameterized by control parameterization. The controller design problems are transformed into sequential optimal parameter decision problems. The explicit expressions of the gradient information of the objective function and constraints on the parameters of the decision variables are solved by using the state sensitivity equation analysis method, and the gradient information is combined with the nonlinear programming algorithm to solve the optimization problems efficiently. Finally, the viability and effectiveness of the proposed design method are verified by numerical simulations.

Use of Machine Learning Techniques in the Modeling of an Industrial Reaction System

Purpose: The objective of the research is to address the industrial production of hydrogen and develop the modeling and simulation of hydrogen-related industrial reaction systems using Machine Learning techniques. Theoretical Framework: The research explores the innovation and promise of Machine Learning techniques in modeling industrial reaction systems, enabling the creation of flexible and adaptive models to deal with complexities in industrial processes. Method/Design/Approach: The method involves the application of machine learning methods, such as linear regressions and the kriging or Gaussian process method, to develop metamodels that analyze the steps of an industrial reaction involving hydrogen. Results and Conclusion: The results of the training and analysis have achieved satisfactory outcomes, with expected values assessed through constraint parameters for each output variable. Research Implications: The research aims to improve real-time prediction accuracy, process variable control, and early fault detection, resulting in greater sustainability and economic efficiency in the industry. Originality/Value: The values underpinning the research include the promotion of technological innovation, operational efficiency, and environmental sustainability, as well as valuing quality, safety, social responsibility, continuous improvement, global competitiveness, and regulatory compliance in the industry.

Investigating the performance evaluation of Cu-Cr tool fabricated by powder metallurgy during electrical discharge machining of EN9 steel

Purpose The purpose of this study is to explore the machining characteristics of electrical discharge machining (EDM) when a tool is fabricated using powder metallurgy. Because pure Cu tools obtained using conventional machining pose problems of high tool wear rate, tool oxidation causes loss of characteristics in tool shape. Design/methodology/approach The research investigation carried out experiments planned through Taguchi’s robust design of experiments and used analysis of variance (ANOVA) to carry out statistical analysis. Findings It has been found that copper and chromium electrodes give less metal removal rate as compared to the pure Cu tool. Analytical outcomes of ANOVA demonstrated that MRR is notably affected by the variable’s polarity, peak current, pulse on time and electrode type in the machining of EN9 steel with EDM, whereas the variables pulse on time, gap voltage and electrode type have a significant influence on EWR. Furthermore, the process also showed that the use of powder metallurgy tool effectively reduces the value of SR of the machined surface as well as the tool wear rate. The investigation exhibited the possibility of the use of powder metallurgy electrodes to upgrade the machining efficiency of EDM process. Research limitations/implications There is no major limitation or implication of this study. However, the composition of the powders used in powder metallurgy for the fabrication of tools needs to be precisely controlled with careful control of process variables during subsequent fabrication of electrodes. Originality/value To the best of the authors’ knowledge, this is the first study that investigates the effectiveness of copper and chromium electrodes/tools fabricated by means of powder metallurgy in EDM of EN9 steel. The effectiveness of the tool is assessed in terms of productivity, as well as accuracy measures of MRR and surface roughness of the components in EDM machining.

Comparative analysis of sublimation and thermal decomposition of TATB

AbstractThis experimental study investigated the effects of confinement, starting mass, and heating rate on TATB thermal decomposition and sublimation using a combined Thermo‐Gravimetric Analyzer and Differential Scanning Calorimetry (TGA/DSC) instrument. The confinement of volatile products was varied using different pinhole sizes with TGA/DSC pans. The measurements showed the open pan experiments without lids/pinholes resulted in complete sublimation of TATB between 320 °C and 360 °C. The heat of sublimation was determined to be 176 kJ/mol (42 kcal /mol), consistent with literature data obtained from other experimental techniques. The use of pinholes suppressed the sublimation of TATB such that the decrease in pinhole size resulted in 1) an increase in the enthalpy of reaction and an increase in the amount of carbonaceous material remaining at the end of decomposition, and 2) convergence of the two peak temperatures corresponding to maximum heat flow and maximum weight loss. Also, a transition from a two‐exotherm thermal decomposition behavior towards a single‐exotherm occurred as the pinhole size was decreased for a given starting mass or as the starting mass was increased for a given pinhole size. These results indicate the kinetics of TATB sublimation, TATB thermal decomposition, and gas diffusion out of a TGA/DSC pan can all compete and result in significantly different enthalpies, amounts of remaining materials, and peak temperatures depending on the pinhole size and starting mass used in the measurements. The results also indicate precise control of process variables (pinhole size, starting mass, and heating rate) in TGA/DSC measurements is required for thermal safety assessment of explosives.

Development of a Matlab-Based Fuzzy PID Controller and The Simulation

Fuzzy PID control is a process control technique combining the advantages of fuzzy logic and PID control. It maintains precise control over processes by combining fuzzy logic and proportional-integral-derivative (PID) controllers. With this method, it is possible to achieve higher levels of accuracy and stability in controlling process variables such as temperature, pressure, flow rate, and position. This makes Fuzzy PID control an ideal cordial application requiring precise process control. Fuzzy PID control uses the PID parameter tuning experience to make the fuzzy controller automatically tune its parameters, giving PID control a better control effect. In this paper, a fuzzy PID controller is designed to control the temperature of an electric boiler. The control effects of PID control, fuzzy control, and fuzzy PID control are compared by simulation analysis. The simulation results show that the unclear PID control effect is ideal and has a good application prospect.

Selection and Control of Process Conditions Enable the Preparation of Curcumin-Loaded Poly(lactic-co-glycolic acid) Nanoparticles of Superior Performance.

Curcumin (CUR) is one natural bioactive compound acknowledged for diverse therapeutic activities, but its use is hindered by its poor bioavailability, fast metabolism, and susceptibility to pH variations and light exposure. Thus, the encapsulation in poly(lactic-co-glycolic acid), or PLGA, has been successfully used to protect and enhance CUR absorption in the organism, making CUR-loaded PLGA nanoparticles (NPs) promising drug delivery systems. However, few studies have focused beyond CUR bioavailability, on the environmental variables involved in the encapsulation process, and whether they could help obtain NPs of superior performance. Our study evaluated pH (3.0 or 7.0), temperature (15 or 35 °C), light exposure, and inert atmosphere (N2) incidence in the encapsulation of CUR. The best outcome was at pH 3.0, 15 °C, without light incidence, and without N2 usage. This best nanoformulation showed NP size, zeta potential, and encapsulation efficiency (EE) of 297 nm, -21 mV, and 72%, respectively. Moreover, the CUR in vitro release at pH values 5.5 and 7.4 suggested different potential applications for these NPs, one of which was demonstrated by the effective inhibition of multiple bacteria (i.e., Gram-negative, Gram-positive, and multi-resistant) in the minimal inhibition concentration assay. Besides, statistical analyses confirmed a significant impact of temperature on the NP size; in addition, temperature, light, and N2 affected the EE of CUR. Thus, the selection and control of process variables resulted in higher CUR encapsulation and customizable outcomes, ultimately enabling more economical processes and providing future scale-up guidelines.

Penerapan Bagan Kendali MEWMA-MEWMV pada Pengendalian Kualitas Lulusan Prodi Statistika FMIPA Universitas Syiah Kuala

Statistical technique used to analyze quality problems and improve process performance is Statistical Process Control (SPC). In this study, multivariate control chart is used to control the quality of the graduates of Statistics Study Program, FMIPA USK, namely using Multivariate Exponentially Weighted Moving Average (MEWMA) control chart to control process average, Multivariate Exponentially Weighted Moving Variance (MEWMV) to control process variability, and analysis Process Capability to assess the entire process. Data used is secondary data, namely GPA data, duration of thesis preparation, and length of study for graduates of Statistics Study Program, FMIPA USK in 2016-2021 as many as 122 people. Results of the study, using MEWMA control chart, it was found that the average process for quality of graduates was statistically controlled in phase II. Selection of the most optimal weighting is λ=0.9. Meanwhile, for application of the MEWMV control chart, it was found that the process variability in the quality of graduates was also statistically controlled. The selection of the most optimal weights is λ =0.9 and ω=0.3. The results of the calculation of process capability, multivariately the GPA variable, length of the final project preparation, and length of the study show that all three are not capable.

Cyber-Physical Systems Framework for Predictive Metrology in Semiconductor Manufacturing Process

The process of semiconductor manufacturing is very complex, and the downtime caused by equipment degradation at any process stage reduces yield and production efficiency. Thus, semiconductor machines’ maintenance and calibration are important in maintaining stable production output and yield improvement. Traditionally, semiconductor manufacturers emphasize the importance of product quality to distinguish product defects. However, as semiconductor reaches nanometer-level precision, monitoring process quality is necessary to control process variability and achieve optimal yields. This paper presented a 5-level Cyber Physical Systems (CPS) predictive metrology architecture to investigate the process quality. The proposed architecture performs a peer-to-peer comparison to satisfy self-comparison ability, which is critical for chamber-to-chamber matching in the semiconductor manufacturing process. This concept has been demonstrated using the 2016 PHM public dataset in which the material removal rate was predicted in the chemical-mechanical planarization process. The results show that this innovative 5-level CPS predictive metrology framework is promising and feasible.

Experimental Research on Sheared Edge Formation in the Shear-Slitting of Grain-Oriented Electrical Steel Workpieces.

This study sought to experimentally develop guidelines for shaping 0.3-mm-thick cold-rolled grain-oriented ET 110-30LS steel using a shear-slitting operation. Coated and non-coated steel was used for the analysis. The coated sheet had a thin inorganic C-5 coating on both sides applied to the C-2 substrate. The first part of this paper presents an analysis of the quality of the cut surface depending on the adopted machining parameters, which were the control variables on the production lines. The second part presents an analysis of the magnetic parameters of the cut samples, which allowed for the specific impact of the quality of the cut edge on the selected magnetic features. Finally, an optimization task was developed to obtain a set of acceptable solutions on the plane of controllable process variables such as slitting speed and horizontal clearance. The obtained results can be used to control the shear-slitting process on production lines and obtain high-quality workpieces.

A REVIEW STUDY ON INSTRUMENTATION AND CONTROL ENGINEERING

In the present review, instrumentation and control engineering (ICE) is defined as a branch of engineering that studies the measurement and control of process variables, and the design and implementation of systems that incorporate them. Instrumentation and control engineering (ICE) combines two branches of engineering. Instrumentation engineering is the science of the measurement and control of process variables within a production or manufacturing area. Meanwhile, control engineering, also called control systems engineering, is the engineering discipline that applies control theory to design systems with desired behaviors. Control engineers are responsible for the research, design, and development of control devices and systems, typically in manufacturing facilities and process plants. Control methods employ sensors to measure the output variable of the device and provide feedback to the controller so that it can make corrections toward desired performance. Automatic control manages a device without the need of human inputs for correction, such as cruise control for regulating a car's speed. In the present study a comprehensive review study on instrumentation and control engineering have been presented. The study was considered from different viewpoints which includes general introduction to instrumentation and control engineering; a comprehensive instrumentation that deliberates the present subject from the consideration of introduction, historical background and development, applications, measurement parameters, instrumentation engineering, impact of modern development; control engineering from the consideration of introduction, overview, history, control theory, control systems, control engineering education, control engineering careers, and recent advancement; and the last section is the conclusions.

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

AbstractCement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

A Study on Metal 3D Printing Internal Pore Control for the Fabrication of Metal Scaffolds

In the treatment of diseases using medical bone scaffolds, it is necessary to design a customized bone scaffold according to the type of individual damage to restore normal bone function based on the damage information of each patient. In addition, the bone scaffold should serve to promote the regeneration of damaged bone in the patient. Compared to a rigid scaffold, a patterned scaffold inserted into a living body promotes bone formation more effectively. Therefore, using a metal 3D printer, it is important to incorporate artificial voids by controlling process variables so that the metal scaffold that replaces damaged bone can form a good bond with live tissue cells. In this study, Ti-6Al-4V ELI, which is actively used in the fields of medical implants and medical devices because of its excellent biocompatibility, was used to fabricate a metal hybrid scaffold that also provides external support. As for the process method, specimen production was conducted by controlling process parameters of the selective laser melting (SLM) method, which is one of the metal 3D printing techniques. Specimens fabricated for each scenario were analyzed by measuring flexural strength, and by imaging via optical microscopy (OM) and scanning electron microscopy (SEM). For OM analysis, the porosity was measured for each case and compared with the flexural strength. Thus, it was possible to adjust the strength by varying the internal pores via the process variables of the metal 3D printer. Accordingly, basic research was conducted to fabricate a metal osseous scaffold with strength similar to that of bone.

High suspended solids removal of Indian drain water with a down-scaled Dissolved Air Flotation (DAF) for water recovery. Assessing water-type dependence on process control variables

The Barapullah drain crosses through New Delhi, India, and transports millions of cubic meters of stormwater, municipal sewage and industrial sewage to the Yamuna River. Seasonal variations and ambiguous annual discharges cause 20-fold fluctuations in hydraulic flows, pollutants type and concentration. Furthermore, New Delhi is among the most densely populated areas on the planet, with limited surface area and high water stress. Dissolved Air Flotation (DAF) units are known to be highly compact, robust, and an efficient suspended solids separation technology that enables further water recovery in a treatment train. Thus, a down-scaled column DAF was designed and used to determine the total suspended solids removal efficiencies, under different influent conditions. Three influents that resemble the Barapullah drain seasonal variations in composition, and a fourth that imitates the feed of DAF when located after an anaerobic bioreactor were tested. A total of 60 batch DAF experiments were completed and used to assess seven independent control variables for DAF operation, which are influent Total Suspended Solids (TSS), pH, temperature, DAF particles residence time, white water pressure, coagulants and flocculants concentration, and coagulation and flocculation time. Results showed that the down-scaled DAF could be steered from low to high removal efficiencies, comparable to full-scale systems. Maximum TSS removal varied between 92 and 96%. The effect and statistical relevance of the different performance variables on the measured separation efficiencies depended on the influent type. All variables, except temperature and pH, had a significant performance effect with a p-value below 0.1, for at least one influent. Pressure had a positive effect on separation efficiency, due to its importance in bubble formation. Moreover, the down-scaled DAF system had low removal efficiency for particles with spherical shapes, and diameters below 10 µm. Based on the high TSS removal for all tested influents, and the effect of the studied control variables, a full-scale DAF could efficiently remove the suspended solids of the Barapullah drain. The unit robustness for different flows and pollutant concentrations, and small footprint, show DAF suitability as part of a treatment train for water recovery, in densely populated areas.