Practical Process Control Research Articles

Shear relaxation behavior for degraded polypropylene melt in torque rheometer

AbstractShear relaxation behavior for polymer melt exists in different processing techniques and influences the dimensional accuracy and production efficiency of polymer products. A micro physical and mathematical model based on microstructure of polymer chains and processing parameters was established and verified by experimental results obtained from a torque rheometer. The shear relaxation time of polypropylene degraded by di‐tert‐butyl peroxide was characterized with torque rheometer. The effects of microstructure and processing parameters on the shear relaxation time was investigated. The results showed that a wider molecular weight distribution could result in a decrease in shear relaxation time and polypropylene chains with higher molecular weight were more sensitive to molecular weight distribution. The shear relaxation time declined exponentially with shear rate and increased with internal pressure. The temperature could enhance the movement of polymer chains and reduce the shear relaxation time linearly, but polypropylene with lower molecular weight was less sensitive to temperature. The insight obtained from the mathematical and experimental results can be profoundly applied in practical process control to manufacture product with better dimensional stability and production efficiency for viscoelastic polymer.Highlights A physical and mathematical model based on microstructure and processing parameters of molecular chains was established to explore the shear relaxation mechanism. Shear relaxation time of polypropylene degraded by di‐tert‐butyl peroxide was characterized with the help of torque rheometer. The effects of microstructure and processing parameters on shear relaxation time was investigated to optimize the manufacturing of polypropylene products.

Energy-Saving Control Strategy for Multisleep States of CNC Machine Tool Considering Components Priority

As the representative energy-consuming equipment in the manufacturing industry, machine tools consume lots of energy during the non-machining period, which leads to low energy efficiency. The existing strategies do not consider the startup sequence among components, which causes the control parameters to fail to run during the practical startup process, and this paper conducts energy-saving research to address the issues. The relationship of priority among components was used as the constraint to obtain an energy-saving control strategy for multi-sleep states of machine tools considering components priority. Furthermore, the improved whale algorithm was applied to solve the energy-saving control strategy of the machine tool, and the search and convergence ability of the algorithm was improved by introducing the chaotic convergence factor and adaptive weight coefficient. Finally, the energy-saving strategy was utilized with a vertical CNC machine tool as an experimental example to mill the end face of the randomly arriving workpieces. The results show that this changed algorithm has a better solution speed and effect than the ordinary whale algorithm and some other improved whale algorithms. Compared with the practical control process and other control strategies, the control strategy considering components priority can obtain more stable productivity and better energy-saving effect.

Machine learning framework for extracting micro-viscoelastic and micro-structural properties of compressed oral solid dosage forms

A compressed pharmaceutical oral solid dosage (OSD) form is a strongly micro-viscoelastic material composite arranged as a network of agglomerated particles due to its constituent powders and their bonding and fractural mechanical properties. An OSD product’s Critical Quality Attributes, such as disintegration, drug release (dissolution) profile, and structural strength (“hardness”), are influenced by its micro-scale properties. Ultrasonic evaluation is direct, non-destructive, rapid, and cost-effective. However, for practical process control applications, the simultaneous extraction of the micro-viscoelastic and scattering properties from a tablet’s ultrasonic response requires a unique solution to a challenging inverse mathematical wave propagation problem. While the spatial progression of a pulse traveling in a composite medium with known micro-scale properties is a straightforward computational task when its dispersion relation is known, extracting such properties from the experimentally acquired waveforms is often non-trivial. In this work, a novel Machine Learning (ML)-based micro-property extraction technique directly from waveforms, based on Multi-Output Regression models and Neural Networks, is introduced and demonstrated. Synthetic waveforms with a given set of micro-properties of virtual tablets are computationally generated to train, validate, and test the developed ML models for their effectiveness in the inverse problem of recovering specified micro-scale properties. The effectiveness of these ML models is then tested and demonstrated for a set of physical OSD tablets. The micro-viscoelastic and micro-structural properties of physical tablets with known properties have been extracted through experimentally acquired waveforms to exhibit their consistency with the generated ML-based attenuation results.

How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory study

Generative Artificial Intelligence (AI) models such as OpenAI’s ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT’s ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

Characterization Of Gallium Oxide With A Novel Non-Contact Electrical Metrology, CnCV, For Wide Bandgap Semiconductors

Purpose of Work Cost savings and short feedback time are significant advantages of the corona non-contact capacitance voltage (CnCV) technique1, recently introduced for wide bandgap SiC, GaN, and AlGaN/GaN as a replacement for C-V measurements on fabricated electrical test structures. The goal of this work is to verify the basic applicability and effectiveness of CnCV in the characterization of Gallium Oxide. The results demonstrate excellent measurement conditions on Ga2O3 epitaxial and bulk grown wafers, enabling the determination of the entire set of electrical properties in Table 1 and in Figures 1 to 5. Results and Discussion Non-contact electrical measurements with the CnCV technique involve electric charge dosing, ΔQ, by corona discharge in air. The response of the semiconductor is monitored as a surface voltage change, ΔV, measured with a vibrating Kelvin probe. A unique combination of charge, voltage, capacitance (C=ΔQ/ΔV), and leakage current, J=CdV/dt, measured in depletion enables the determination of the whole set of electrical properties presented in Table 1. The state-of-the-art CnCV tool by Semilab SDI was modified to include a 255nm UV light source, which provided excellent corona charge removal, restoring the Ga2O3 surface to a pre-charged condition needed for repeated measurements. An absolute Kelvin-probe calibration with a Ag/AgCl reference half-cell allowed for the quantification of the work function, WF, and electron affinity, X.A critical requirement for CnCV applicability is the stability of deposited corona charge with respect to the magnitude of charge under the Kelvin probe and the aerial charge distribution. In this work, the assessment of charge stability on Ga2O3 surfaces was performed using surface voltage profiling after charging. An excellent stability condition was established with a thermal desorption treatment commonly used in semiconductor measurements. A constant and stable charging profile was maintained up to a maximum depletion field of about 3.5MV/cm for epitaxial surfaces and polished surfaces of bulk grown Ga2O3 wafers.In the CnCV metrology, surface voltage mapping is used for identification of the electrical activity of defects. Such mapping of Ga2O3 after full wafer charging revealed the absence of high leakage spots which is probably the most significant difference compared to SiC. This difference is evident from the corresponding maps in Fig. 1 and Fig. 2 for Ga2O3 and 4H-SiC, respectively. The red spots in the 4H-SiC map are defects with the largest electrical activity, such as 3C-SiC related micro-structures2,3. No defect spots were observed in Ga2O3 maps. This significant result may be a consequence of the low probability of the poly-type inclusions in Ga2O3.Examples of non-contact C-V and corresponding 1/C2 characteristics are shown in Fig. 3 for epi-Ga2O3. These results give a constant dopant depth profile in Fig. 4. According to the initial capacitance, C0, the Ga2O3 surfaces are initially in depletion with negative surface charges as given in Table 1. The presently determined electron affinity, X, was uniform across the sample (see Fig. 5). The value 3.6 ± 0.1 is close to the literature range.In summary, we conclude that CnCV is a powerful alternative to standard C-V and mercury C-V and shall prove very useful in the investigation of important fundamental properties of Ga2O3 and the practical development and process control of emerging Ga2O3 power devices.

Deep learning feature-based setpoint generation and optimal control for flotation processes

Computer vision-based control is a nonintrusive, cost-effective, and reliable technique for flotation process control. It is known that deep learning features can depict the complex behavior of the froth surface more comprehensively and accurately than handcrafted features. However, few studies have tried to use additional information to improve flotation performance through optimal control. To this end, we have attempted to develop a novel deep learning feature-based two-layer optimal control scheme. The first layer is proposed for setpoint generation of high-dimensional features using improved fuzzy association rule reasoning. Then, an offline conservative double Q-learning control layer that can learn from historical industrial records by mitigating bootstrapping error in action value functions is developed. The proposed method can adapt the setpoint to the change in process feeds. Meanwhile, in contrast to traditional approximate dynamic programming methods that need to interact with real/simulated process systems, this controller can work without any further interactions, which makes it possible to transfer the success of reinforcement learning algorithms to complex industrial process control where opportunities to explore are missing. Experiments demonstrate that the proposed method is effective and promising for practical flotation process control.

Autogenous grinding mill process control practices

This paper considers pre-requisite conditions for developing grinding mill control systems and describes the main problems of control and existing constraints. The author selected a set of grinding equipment that consists of autogenous grinding mills and ball mills. The paper considers the selected mill types as control objects. It is shown that there exists a good correlation between the mill charge and its acoustic and vibration noise. The paper examines the relationship between the level of vibroacoustic noise and the mill motor power draw and defines what can be considered an extreme dependence, as well as the conditions in which a stable operation of the automatic system can be maintained excluding the mill overload mode. The paper specifies what hardware and software means would be necessary to implement such system and describes the mill charge analyzer VAZM-1M developed by Soyuztsvetmetavtomatika that was selected for this application. The author looks at certain downsides typical of the conventional control scheme when the head mill feed rate changes as the mill motor power draw changes without allowing for changing physical and mechanical properties of the ore material. The author also considers the capability of the VAZM-1M analyzer in terms of mill load estimation accuracy. This laid the basis for developing mill protection and optimization algorithms for the AG and Ball Mill comminution circuit. The paper features a block diagram of the control algorithm and its brief description. The algorithm consists of blocks, which are responsible for the following actions: they receive key process parameters from the process control system database, check them for validity, perform initial processing and filtering; after that they analyze the trends of the measured parameters and analyze if an overload condition is probable. As decided by the mill operator, the ore flow rate can be adjusted. The paper describes a case study of running an AG mill control system on the basis of the above described algorithm and using the VAZM-1M analyzer. It is noted that this algorithm can be implemented both as an adviser for the operator and for automatic control of the mill when running in overload mode.

Quantitative Tuning of Active Disturbance Rejection Controller for FOPTD Model With Application to Power Plant Control

Active disturbance rejection controller (ADRC) has achieved soaring success in motion controls featured by rapid dynamics. However, it turns obstreperous to implement it in the power plant process with considerable time-delay, largely because of the tuning difficulty. To this end, this article proposes a quantitative tuning rule for the time-delayed ADRC (TD-ADRC) structure based on the typical first order plus time delay (FOPTD) model. By compensating the FOPTD process as an integrator plus time delay in low frequencies, the gain parameter of TD-ADRC can be related to a scaled time constant which shapes the closed-loop tracking performance. Bandwidth parameter of extended state observer is scaled as a dimensionless parameter. A sufficient stability condition of TD-ADRC is theoretically derived in terms of the scaled parameter pair, the range of which falls within the practical interest. Relative delay margin is revealed as a critical robustness metric among others, a default pair of scaled parameter setting is recommended as well as an explicit retuning guideline according to the user's preference for performance or robustness. Simulation and laboratory water tank experiment validate the tuning efficacy and a coal mill temperature control test depicts a promising prospective of the proposed method in process control practice.

Robust extended Kalman filter based state estimation for nonlinear dynamic processes with measurements corrupted by gross errors

Abstract The quality of on-line measurement data usually cannot meet the demands of practical process monitoring and control due to the influence of measurement noise. Although extended Kalman filter (EKF) is able to improve the quality by reconciling the measurement data to fit the dynamic process model describing nonlinear and Gaussian processes, it rarely considers the presence of different types of gross errors, such as outliers, bias and drift. However, the three types of gross errors often simultaneously appear in dynamic process systems. We propose a robust EKF combined with measurement compensation (MC-REKF) approach, in which a statistical test method is used to detect the abnormal measurement data, where gross error identification is accomplished within a moving window. Subsequently, the magnitudes of gross errors are estimated and used for measurement compensation. Finally, the compensated measurements are updated to re-estimate the accurate states via EKF. The effectiveness of the proposed MC-REKF is demonstrated through a complex nonlinear dynamic chemical process system, namely the free radical polymerization of styrene. With three different types of gross errors, the mean squared error (MSE) of reconciled measurements based on the MC-REKF decreases 37 fold compared to EKF. The magnitude of the residuals between the estimated states and the true states falls below 1.0E−6 when using the MC-REKF. The implementation results imply that the proposed MC-REKF can identify and estimate different types of gross errors and finally decreases their influence on state estimation and measurement reconciliation.

Means versus goals at the starting line: Performance and conditions of effectiveness of entrepreneurial action

ABSTRACT Various theoretical perspectives suggest that a means-oriented approach to new venture development can be a viable alternative to the conventional approach, which emphasizes predetermined goals, and that the former is favored by expert entrepreneurs. However, it is still unclear whether, and under which conditions, means-based action positively affects entrepreneurial performance and whether it would also be effective for novices. This study demonstrates the new venture performance impact of means orientation. We further contribute to various strands of entrepreneurship research by highlighting two moderating factors that are salient in the early-stage entrepreneurial process: opportunity recognition beliefs and process control practices.

Key Parameter Identification and Defective Wafer Detection of Semiconductor Manufacturing Processes Using Image Processing Techniques

The semiconductor industry has become fully automated during the manufacturing process and abundant process parameters are collected online by sensors for fault detection and classification purposes. Analyzing process parameters and identifying a smaller set of key parameters that have crucial influence on wafer quality will bring great benefits in stabilizing the manufacturing process and enhancing productive yield. Typically, this type of the parameter set is called the “raw trace data.” This paper considers image processing techniques as a novel approach for analyzing and visualizing the raw trace data. First, the 1-D time series data of a wafer batch was transformed into a 2-D image. Fisher’s criterion ratios of the labeled good and defective wafer image maps are computed to identify the key parameters. The key parameters identified by the proposed image processing technique are consistent with the technical experience of the process engineers. Furthermore, the texture analysis technique with 2-D Fourier transform is utilized to analyze the images of the key parameters to detect defective wafers. The proposed key parameter identification and wafer classification method proves to be a viable solution under the paradigm of advanced process control practice for semiconductor manufacturing.

Robust predictor based control of state multiplicative noisy retarded systems

Linear, continuous-time systems with stochastic uncertainties and delayed input are considered. The problems of H∞ state-feedback control and static output-feedback control are solved, for nominal systems, applying a Luenberger-type predictor. The two problems are solved via the input–output approach, where the system is replaced by a non-retarded system with deterministic norm-bounded uncertainties. The cost function in these two problems is the expected value of the standard H∞ performance index with respect to the stochastic parameters.The above problems are solved also for the case where the deterministic parameters of the system encounter either norm-bounded or polytopic-type uncertainties. In the uncertain state-feedback control problem the solution is obtained by applying a vertex-dependent approach that is based on the Finsler lemma. An illustrative example is given that compares several control solution methods. An additional example of practical process control problem is also solved.

A cybermanufacturing and AI framework for laser powder bed fusion (LPBF) additive manufacturing process

In Laser Powder Bed Fusion (LPBF) along, more than 50 process parameters are known to affect print quality. The current state-of-the-art practice in process control only considers a small fraction of them – mainly on laser power and scanning speed affecting temperature gradient and geometry of a melting pool. This letter proposes a system-wide platform involving various machine learning principles and leveraging production data stored in the cloud. The proposed framework aims to identify process parameters that may affect print quality so that a viable process control strategy can be formulated.

Observer and filter design for linear transport-reaction systems

The present work extends known finite dimensional Luenberger observer and Kalman filter designs to the realm of linear transport-reaction systems frequently present in chemical engineering practice. A unified modelling framework for distributed parameter systems (DPS) which does not account for any type of spatial approximation or order reduction is developed. The Cayley–Tustin transformation of continuous linear distributed parameter system yields structure and properties preserving discrete distributed parameter models, amenable to observer and filter design developments. Designs presented here explore well known state reconstruction methodologies starting from least square estimation, continuous and discrete Luenberger observers and one-step predictor Kalman filter realization. Simple implementation and realization account for the appealing nature of the discrete system observers and filter designs for linear transport-reaction systems. The simulation scenarios cover the majority of representative examples found in the common engineering process control practice.

Process Control for Advanced Packaging Metallization

Despite advances in optical interconnect, metal interconnect remains the dominant technology, however the type and diversity of metal structures is rapidly changing. Wire-bonding cannot meet required interconnect density and is being replaced with electrodeposition-based flip chip technology. Modern packaging scheme required high diversity of interconnects within the same device: RDL, mircobump, pillar, TSV, tall (mega) pillar, etc. using sequential deposition of different metals such as Cu, Ni, SnAg/Sn, Au. Diverse challenging requirement leads to evolution of chemistries and process control approaches. Many 20th century chemistries were limited to 1 organic additive for Cu process. Today, up to 4 individual additives are used to dial the process. While most of semiconductor front end processing enjoys the luxury of single-use chemistry, it is not yet considered a viable option for electrodeposition. The same electrolyte can be used for processing over 1000 parts in replenishment mode. Maintaining performance under these conditions requires close monitoring of component breakdown and contaminants accumulated in the processes. There is also a real possibility for disproportional trends for individual components of replenishment package. It is no longer enough to monitor just main 3–6 components of the bath. Successful high volume metallization for advanced packaging requires comprehensive metrology/process control. Presentation will provide specific examples of metrology and process control strategy for various metallization solution with emphasize on breakdown products and contaminants. There is no single technology which can cover diversity of metrology needs. CVS is a versatile technology which can be used not only for control of main additives but also to monitor breakdown products of accelerator, suppressor, leveler, Cu(I) contaminants, H2O2 contaminant and even leached photoresist. In case of Ni chemistry, it is useful to monitor breakdown products of sulfamate. UV-Vis spectroscopy shines at monitoring of leached photoresists, Fe(III), breakdown products of sulfamate as well as control of many main components: Cu, Ni, Co, Au, additives. ICP is the best for ultra low level of metal contaminants which can harms deposition process, as well as low level of some target components, like Tl in Au. Novel approach is developed to track additive turnover process based on ICP results. HPLC is valuable to characterize multiple breakdown products of organic additives, while ion chromatography helps to track transformation of ions, such as oxidation of sulfite to sulfate in Au solution. Surface tension aids monitoring of surfactants and their degradation. It is important to differentiate between R&D study and robust “fit-to-purpose” practical process control. While Ion Chromatography approach would be more appropriate for academic study and perhaps process development, simple robust methods like titration, specific gravity are preferable for industrial process control. Similarly, HPLC is a great scientific tool but CVS, spectroscopy and titration are better fit and safer alternative for in-fab operation. Presentation will provide specific examples and comparative analysis of different analytical methods.

Process control and quality performance: the role of shop-floor leadership practices

Human Resource Management issues are a crucial factor in the effective implementation of tools and practices in the field of Quality Management. Our study addresses this issue by considering the existence of complementarities between Process Control (PC) and two shop-floor leadership practices (i.e. Shop-Floor Contact (SFC) and Supervisory Interaction Facilitation (SIF)), directed to promote contact and interaction between middle managers and supervisors with blue collar workers to explain quality performance, which is measured by Quality of Conformance (QC) and Customer Satisfaction (CS). The data used in this study were gathered from 317 manufacturing plants, with more than 100 workers, operating in 10 countries all over the world. Our results confirm the positive effects that PC practices have on both QC and CS. In addition, we observed that leadership practices moderate the relationship between PC and quality performance measures. Specifically, it seems that the SFC interacts with PC to improve CS; whereas, SIF can be an aid for PC to increase QC.

Characterization of pre-treatments on wood chips prior to delignification by near infrared spectroscopy

A near infrared (NIR) spectroscopy-based method for predicting yields and lignin contents of differently pre-treated silver/white birch (Betula pendula/B. pubescens) and Scots pine (Pinus sylvestris) chips was developed. The approach was to create multivariate calibration models from the NIR data by the partial least squares (PLS) method. Both parameters are important factors when adjusting adequate conditions for pre-treatments either with hot-water (HW) as such and slightly acidified HW (collectively referred to as autohydrolysis) or dilute alkaline aqueous solutions prior to alkaline pulping. Pre-treatment conditions were varied with respect to temperature (130 °C and 150 °C) and treatment time (from 30 min to 120 min). In the case of alkaline pre-treatments, the NaOH charge was 1% to 8% NaOH on wood dry solids (DS). The yields varied in the range 81.2% to 99.3% (in autohydrolyses) and 83.5% to 97.9% (in alkaline pre-treatments). High correlation coefficients and low prediction errors in relation to conventional yield and lignin content data clearly indicated the suitability of NIR spectroscopy combined with the multivariate modeling as an effective and fast tool for this purpose. This technique also showed promising possibilities for developing practical process control methods to follow such pre-treatments.

Application of Configuration Software in Process Control Course Teaching

According to the characteristics of configuration software and process control course, the configuration software is applied to teaching practice of process control. Configuration software can realize the mathematical model and the design of the digital controller of the controlled object in the process control, and can build different process control system. In the teaching practice of process control, the combination of theory and simulation practice not only makes the boring process control theory more vivid and helps students to understand and remember the theoretical knowledge easily, but also improves the students’ learning interest, efficiency and enthusiasm. Practices prove that this method is an effective way to teach process control system. And it also provides a new idea for such kind of control courses.

Process control using finite Markov chains with iterative clustering

Abstract Finite state Markov Decision Processes (MDP) for process control are considered. MDP provide robust tools to perform optimization in closed-loop, and their finite state description enables an easy implementation of Bayesian state estimation. An approach to tackle the curse of dimensionality problem, yet retaining the benefits of the finite state MDP in control and estimator design, is proposed. The suggested approach uses iterative re-discretization based on clustering of closed-loop data. An efficient modification of the k-means clustering technique is proposed. The performance of the approach is demonstrated using a challenging benchmark from chemical engineering, the van der Vusse continuous stirred tank reactor control problem. It is shown that the requirements of the benchmark are met, and that the suggested iterated clustering significantly improves the performance. It is concluded that the finite state MDP approach is a viable alternative for small-to-medium scale problems of practical process control and state estimation.

Implementation of an adaptive fuzzy compensator for coupled tank liquid level control system

In this paper, an adaptive fuzzy control (AFC) system is proposed to realize level position control of two coupled water tanks, often encountered in practical process control. The fuzzy control system includes an adaptive model identifier and controller. The gains of AFC are obtained by using the fuzzy identifier model which is defined by real system outputs and control inputs. The parameters of fuzzy identifier model are adjusted online by using recursive least square algorithm. Because the controller has a recursive form it treats model uncertainties and external disturbances in an implicit way. Thus there is no need to specify uncertainty and disturbances for this controller design in advance. A well-tuned conventional proportional integral (PI) controller is also applied to the two coupled tank system for comparison with the AFC system. Experimentation of the coupled tank system is realized in two different configurations, namely configuration #1 and configuration #2 respectively. In configuration #1, the water level in the top tank is controlled by a pump. In configuration #2, the water level in the bottom tank is controlled by the water flow coming out of the top tank. Experimental results prove that the AFC shows better trajectory tracking performance than PI controller in that the plant transient responses to the desired output changes have shorter settling time and smaller magnitude overshot/undershoot. Robustness of the AFC with respect to water level variation and capability to eliminate external disturbances are also achieved. Experimental results show that AFC is a strong and a practical choice for liquid level control.