Closed-loop Process Control Research Articles

Straightening rollformed profiles by controlled partial rolling

Roll forming is a sheet metal forming process in which flat sheets are incrementally formed into a desired profile geometry. The process is characterized by high material utilization and output. Along with these advantages, process-related longitudinal strains occur, causing profile defects such as bowing and twisting of the profile, which require subsequent straightening. Conventional straightening methods use overbending and twisting to eliminate existing profile defects. However, this approach is purely experience-based and iterative due to the complex interactions between the various bending and twisting operations. Therefore, a closed-loop control of the straightening process is currently not implemented. This can be remedied by the novel knowledge-based straightening method of partial rolling.In this paper, the basic idea of partial rolling will be introduced and the idea of knowledge-based process control will be demonstrated. A simulation model is then presented to test different process control strategies and evaluate their impact on the profile defects. Finally, an outlook is given on how a closed-loop controlled partial rolling process could be set up in a real process.

Double-Closed-Loop Robust Optimal Control for Uncertain Nonlinear Systems.

Optimal control methods have gained significant attention due to their promising performance in nonlinear systems. In general, an optimal control method is regarded as an optimization process for solving the optimal control laws. However, for uncertain nonlinear systems with complex optimization objectives, the solving of optimal reference trajectories is difficult and significant that might be ignored to obtain robust performance. For this problem, a double-closed-loop robust optimal control (DCL-ROC) is proposed to maintain the optimal control reliability of uncertain nonlinear systems. First, a double-closed-loop scheme is established to divide the optimal control process into a closed-loop optimization process that solves optimal reference trajectories and a closed-loop control process that solves optimal control laws. Then, the ability of the optimal control method can be improved to solve complex uncertain optimization problems. Second, a closed-loop robust optimization (CL-RO) algorithm is developed to express uncertain optimization objectives as data-driven forms and adjust optimal reference trajectories in a close loop. Then, the optimality of reference trajectories can be improved under uncertainties. Third, the optimal reference trajectories are tracked by an adaptive controller to derive the optimal control laws without certain system dynamics. Then, the adaptivity and reliability of optimal control laws can be improved. The experimental results demonstrate that the proposed method can achieve better performance than other optimal control methods.

Model-based closed-loop process control for the manufacturing of hairpin coils

As a consequence of the increasing market share of hybrid and all-electric mobility solutions, there is a need for robust manufacturing technologies that enable the efficient production of powerful electric traction motors in automotive industry. In the context of high-quality distributed stator windings, the so-called hairpin technology already meets the automotive demand regarding productivity – but process reliability is still limited by variations in the mechanical and geometric properties of the enameled rectangular copper wire leading to different springback effects within the forming processes. Against this background, a model-based closed-loop process control for the sequential tool-bound bending of hairpin coils is introduced and validated in this paper.

In Situ Spatially Resolved Coating Thickness Measurements in Thermal Spraying

Achieving target coating thickness is one of the main objectives in thermal spraying. Despite this, there is a lack of measurement methods that could evaluate in situ the coating thickness with a sufficient accuracy that could be used as a robust feedback signal for online, closed-loop process control. This paper presents a novel approach for in situ spatially resolved coating thickness measurements. The measurement technique is based on a high-resolution 3D camera to capture the surface topography and include it in the thickness measurement. The technique provides results of total coating thickness with excellent accuracy when compared to the reference microscopical method. It also gives a 3D view of the coating thicknesses around the observed area as well as information about the thickness of individual coating layers. Moreover, the approach enables in situ evaluation of surface roughness, and a nondestructive estimation of coating porosity.

Embedding AI into laser pulse shaping closed-loop control

Laser pulse shaping is an essential task for power balance in the high-power laser facility. The precisely shaped laser pulse relies heavily on the waveform generation of arbitrary waveform generator (AWG), which provides the electric pulse drive. Based on a set of control model and iterative algorithm, we established a closed-loop control method for pulse shaping. However, it consumes too much time, greatly affecting the working efficiency of laser physics experiments. To improve the time efficiency of closed-loop control, we propose an artificial intelligence assisted method. By training a U-net model to predict the initial pulse waveform, the closed-loop control process of pulse shaping is greatly accelerated. Furthermore, given that the artificial intelligence model is not compatible with the current pulse shaping control platform, we introduce thrift as a fresh middle ware, and implement an RPC bridge. Thus the artificial intelligence model is smoothly embedded into the traditional closed-loop control process.

Third-harmonic generation monitoring of femtosecond-laser-induced in-volume functional modifications

During the last two decades, ultrafast in-volume laser-based processing of transparent materials has emerged as a key 3D-printing method for manufacturing a variety of complex integrated photonic devices and micro-parts. Yet, identifying suitable laser process parameters for a given substrate remains a tedious, time-consuming task. Using a single laser source for both processing and monitoring, we demonstrate a method based on in situ full-field third-harmonic generation (THG) microscopy that exploits the properties of a low-noise CMOS imager to rapidly identify the entire processing space, discriminating different types of laser-induced modifications, and extracting incubation laws governing the laser exposure process. Furthermore, we show that full-field THG monitoring is capable of identifying parameters leading to enhanced functional properties, such as laser-enhanced etching selectivity. These findings enable accelerated implementations of laser processes of arbitrarily chosen transparent materials and, due to the rapid acquisition time (>100FPS) of the imager, closed-loop process control.

Blockchain-IIoT-big data aided process control and quality analytics

The literature on quality management system (QMS) assumes that product and process performance data are authentic and easily accessible. This assumption, while ideologically sound, is questionable in practise because the authenticity and accessibility of data cannot be guaranteed in many circumstances. Inaccurate, incomplete, inconsistent, and inaccessible data are common in supply chains and prevent the QMS from achieving its goal: assuring product and process quality to meet customer requirements. This study is one of the first to examine the impact of data quality and data latency on process control and quality analysis which are elemental parts of daily QMS activities, from a supply chain visibility (SCV) perspective. In this study, five propositions are made to show the relationships between technology, SCV, and data issues. More importantly, the study proposes a platform that integrates Blockchain (BC) technology, Industrial Internet of Things (IIoT), and Big Data to solve data problems in SCV and QMS. We further perform fuzzy association rule mining (FARM) to show how the platform can solve quality analysis problems and complete a closed-loop process control cycle in manufacturing. We also explain the contributions of the integrated platform to QMS from four theoretical perspectives. Finally, we discuss the limitations of the platform and provide recommendations for future research.

Deterministic learning-based neural control for output-constrained strict-feedback nonlinear systems

This paper studies learning from adaptive neural control of output-constrained strict-feedback uncertain nonlinear systems. To overcome the constraint restriction and achieve learning from the closed-loop control process, there are several significant steps. Firstly, a state transformation is introduced to convert the original constrained system output into an unconstrained one. Then an equivalent n-order affine nonlinear system is constructed based on the transformed unconstrained output state in norm form by the system transformation method. By combining dynamic surface control (DSC) technique, an adaptive neural control scheme is proposed for the transformed system. Then all closed-loop signals are uniformly ultimately bounded and the system output tracks the expected trajectory well with satisfying the constraint requirement. Secondly, the partial persistent excitation condition of the radial basis function neural network (RBF NN) could be verified to achieve. Therefore, the uncertain dynamics can be precisely approximated by RBF NN. Subsequently, the learning ability of RBF NN is achieved, and the knowledge acquired from the neural control process is stored in the form of constant neural networks (NNs). By reutilizing the knowledge, a novel learning controller is established to improve the control performance when facing the similar or same control task. The proposed learning control (LC) scheme can avoid repeating the online adaptation of neural weight estimates, which saves computing resources and improves transient performance. Meanwhile, the LC method significantly raises the tracking accuracy and the speed of error convergence while satisfying of the constraint condition simultaneously. Simulation studies demonstrate the efficiency of this proposed control scheme.

In situ characterization of material extrusion printing by near-infrared spectroscopy

Material extrusion printing of reactive resins and inks present a unique challenge due to the time-dependent nature of the rheological and chemical properties they possess. As a result, careful print optimization or process control is important to obtain consistent, high quality prints via additive manufacturing. We present the design and use of a near-infrared (NIR) flow through cell for in situ chemical monitoring of reactive resins during printing. Differences between in situ and off-line benchtop measurements are presented and highlight the need for in-line monitoring capability. Additionally, in-line extrusion force monitoring and off-line post inspection using machine vision is demonstrated. By combining NIR and extrusion force monitoring, it is possible to follow cure reaction kinetics and viscosity changes during printing. When combined with machine vision, the ability to automatically identify and quantify print artifacts can be incorporated on the printing line to enable real-time, artificial intelligence-assisted quality control of both process and product. Together, these techniques form the building blocks of an optimized closed-loop process control strategy when complex reactive inks must be used to produce printed hardware.

Advanced pharmaceutical manufacturing: A functional definition

AbstractThe term “Advanced Pharmaceutical Manufacturing” (APM) has become an ubiquitous buzzword with deep potential policy implications. There is a real danger that APM will be seen as a general panacea for solving economic woes and drug shortages, devoiding it from specific meaning, and depriving the technical community of focus much needed for its development and implementation. This paper first discusses several semantic definitions of APM that have been proposed in the field. A functional definition of APM is then presented as: A system that is designed using predictive models, where automation minimizes human intervention while enabling closed loop process control and real time quality assurance, where performance has been optimized to maximize desired process goals, where flexible amounts of product with equivalent attributes can be manufactured, and where equivalent processes can be implemented at multiple locations to manufacture products with equivalent critical quality attributes. The proposed definition is presented to motivate discussion and elicit contributions from other stakeholders, and to contribute to the development of a consensus framework for design, development, implementation, and evaluation of APM.

Spatio-temporal LPV model of 2D workpiece temperature for Direct Laser Deposition

Additive manufacturing of metallic components presents challenges regarding quality assurance and consistency, owing to the workpiece's complex spatially-dependent temperature history and a lack of suitable models for closed-loop temperature control of the fabrication process. The selected case study is the laying of cladding material (workpiece) through Direct Laser Deposition. The paper proposes identifying a linear spatio-temporal parameter-varying model for the workpiece temperature for control-oriented applications. For this, the temperature distribution is reconstructed from data measured by an infrared camera. The model structure is derived from the inhomogeneous heat equation. Finally, the model parameters are identified using a nonlinear least-squares solver. The relationships between theoretical and estimated model parameters are presented.

On the Controllability and Observability of Temperature States in Metal Powder Bed Fusion

AbstractPowder bed fusion (PBF) is an additive manufacturing (AM) process that builds parts in a layer-by-layer fashion out of a bed of metal powder via the selective melting action of a laser or electron beam heat source. Despite its transformational manufacturing capabilities, PBF is currently controlled in the open loop and there is significant demand to apply closed-loop process monitoring and control to the thermal management problem. This paper introduces a controls theoretic analysis of the controllability and observability of temperature states in PBF. The main contributions of the paper are proofs that certain configurations of PBF are classically controllable and observable, but that these configurations are not strongly structurally controllable and observable. These results are complemented by case studies, demonstrating the energy requirement of state estimation under various, industry relevant PBF configurations. These fundamental characterizations of controllability and observability provide a basis for realizing closed-loop PBF temperature estimation.

A flow forming process model to predict workpiece properties in AISI 304L

The control of workpiece properties enables an application-oriented and time-efficient production of components. In reverse flow forming, e.g., the control of the microstructure profile, in contrast to the adjustment of the geometry, is not yet part of the state of the art. This is particularly challenging when forming seamless tubes made of metastable austenitic stainless AISI 304L steel. In this steel, a phase transformation from austenite to martensite can occur due to mechanically and/or thermally induced energy. The α’-martensite has different mechanical and micromagnetic properties, which can be advantageous depending on the application. For the purpose of local property control, the resulting α’-martensite content should be measured and controlled online during the forming process. In this paper, results from an empirical correlation model of process parameter combinations and resulting α’-martensite content as well as geometry will be presented. Based on this, the focus of the paper will be on process modeling by means of FEM in order to create the transition to a numerically supported process model. Furthermore, it will be specified how the numerical process model can be used in a predictive manner for an online closed-loop process control.

Interval Type-2 Fuzzy Analysis and Comprehensive Evaluation for Neonatal Pathological Jaundice

Neonatal pathological jaundice (NPJ) is easy to cause bilirubin encephalopathy, which has high mortality and sequelae rate. Therefore, accurate risk evaluation can help clinicians take appropriate measures to timely intervene in neonatal jaundice level and avoid complications. In this article, five indexes are extracted as the factor set for the risk evaluation of NPJ, and the diagnostic criteria are determined. Then, five index sets are described by interval type-2 fuzzy sets, and the corresponding membership functions and membership function figures are provided. The feasibility of interval type-2 fuzzy comprehensive evaluation in risk evaluation of NPJ is demonstrated through example, and the fuzzy rule bases of prevention and treatment for NPJ are constructed according to the results of risk evaluation. Finally, this article demonstrates that the proposed risk evaluation and treatment process of NPJ is actually a dynamic closed-loop control process, which is consistent with the clinical treatment process. This article provides a new solution for the aided diagnosis and decision-making treatment of NPJ, which is of great significance in reducing neonatal mortality and alleviating the pressure of medical staff.

An Expanded Framework for Situation Control.

There is an extensive body of literature on the topic of estimating situational states, in applications ranging from cyber-defense to military operations to traffic situations and autonomous cars. In the military/defense/intelligence literature, situation assessment seems to be the sine qua non for any research on surveillance and reconnaissance, command and control, and intelligence analysis. Virtually all of this work focuses on assessing the situation-at-the-moment; many if not most of the estimation techniques are based on Data and Information Fusion (DIF) approaches, with some recent schemes employing Artificial Intelligence (AI) and Machine Learning (ML) methods. But estimating and recognizing situational conditions is most often couched in a decision-making, action-taking context, implying that actions may be needed so that certain goal situations will be reached as a result of such actions, or at least that progress toward such goal states will be made. This context thus frames the estimation of situational states in the larger context of a control-loop, with a need to understand the temporal evolution of situational states, not just a snapshot at a given time. Estimating situational dynamics requires the important functions of situation recognition, situation prediction, and situation understanding that are also central to such an integrated estimation + action-taking architecture. The varied processes for all of these combined capabilities lie in a closed-loop “situation control” framework, where the core operations of a stochastic control process involve situation recognition—learning—prediction—situation “error” assessment—and action taking to move the situation to a goal state. We propose several additional functionalities for this closed-loop control process in relation to some prior work on this topic, to include remarks on the integration of control-theoretic principles. Expanded remarks are also made on the state of the art of the schemas and computational technologies for situation recognition, prediction and understanding, as well as the roles for human intelligence in this larger framework.

Innovative Online Measurement and Modelling Approach for Property-Controlled Flow Forming Processes

The production of complex multi-functional, high-strength parts is becoming increasingly important in the industry. Especially with small batch size, the incremental flow forming processes can be advantageous. The production of parts with complex geometry and locally graded material properties currently depicts a great challenge in the flow forming process. At this point, the usage of closed-loop control for the shape and properties could be a feasible new solution. The overall aim in this project is to establish an intelligent closed-loop control system for the wall thickness as well as the α’-martensite content of AISI 304L-workpieces in a flow forming process. To reach this goal, a novel sensor concept for online measurements of the wall thickness reduction and the martensite content during forming process is proposed. It includes the setup of a modified flow forming machine and the integration of the sensor system in the machine control. Additionally, a simulation model for the flow forming process is presented which describes the forming process with regard to the plastic workpiece deformation, the induced α’-martensite fraction, and the sensor behavior. This model was used for designing a closed-loop process control of the wall thickness reduction that was subsequently realized at the real plant including online measured feedback from the sensor system.

A Case Study of Open- and Closed-Loop Control of Hydrostatic Transmission with Proportional Valve Start-Up Process

This paper concerns the start-up process of a hydrostatic transmission with a fixed displacement pump, with particular emphasis on dynamic surplus pressure. A numerically controlled transmission using a proportional directional valve was analysed by simulation and experimental verification. The transmission is controlled by the throttle method, and the variable resistance is the throttling gap of the proportional spool valve. A mathematical description of the gear start-up process was obtained using a lumped-parameters model based on ordinary differential equations. The proportional spool valve was described using a modified model, which significantly improved the performance of the model in the closed-loop control process. After assuming the initial conditions and parameterization of the equation coefficients, a simulation of the transition start-up was performed in the MATLAB–Simulink environment. Simulations and experimental studies were carried out for control signals of various shapes and for various feedback from the hydraulic system. The pressure at the pump discharge port and the inlet port of the hydraulic motor, as well as the rotational speed of the hydraulic motor, were analysed in detail as functions of time. In the experimental verification, complete measuring lines for pressure, speed of the hydraulic motor, flow rate, and temperature of the working liquid were used.

A novel inductive angular displacement sensor based on time-grating

The paper proposes a novel inductive angular displacement sensor to measure large-diameter hollow rotating mechanical components in large science facilities. The sensor design includes a stator, a rotor with two layers of windings. The stator is fabricated according to the dimension of the rotor, which is the large-diameter hollow rotary mechanical components. Based on the principle of electromagnetic induction, the theoretical model of the proposed sensor is acquired by mathematical equations in detail. The proposed sensor design ensures integration of the large-diameter hollow rotating mechanical components with the stator, and solves the problem that the sensors cannot be coaxially installed in the closed-loop control process of large mechanical rotating parts. The performance of the sensor is evaluated by tests of the prototype sensor fabricated by printed circuit board technology, which manifests the effectiveness of the proposed sensor. The results demonstrate the value of original dynamic relative error is ±97.6″ over 0°–360° range, and the measurement error mainly includes first-order and second-order harmonic components. Therefore, the original errors are reduced by optimizing the angular displacement algorithm of the sensor, optimized results demonstrate the final relative accuracy of the sensor is ±3.5″ over the 0°–360° measurement range.

Pyrometry-based closed-loop control of the melt pool temperature in Laser Metal Deposition with coaxial wire feeding

Laser metal deposition with coaxial wire feeding is a directed energy deposition process that enables near-net-shape and direction-independent additive manufacturing of metal parts. However, the high sensitivity of the process to disturbances poses a major challenge, as this frequently leads to defective components and rejects. To counteract this, a pyrometer was coaxially coupled into the beam path of the laser processing head to accurately determine the melt pool temperature without limiting accessibility. Applying deterministic test signals, a system model describing the dynamic relationship between the temperature and the laser power was identified. Based on this model, a closed-loop process control was designed. The control system was validated by introducing various disturbances, for which the reference temperature could be tracked with good approximation. The designed temperature control results in reduced effort for tuning the deposition process as well as a lower probability of defects, which saves manufacturing time and cost.

Sustainability potentials of an innovative technology and plant system in non-ferrous foundries

The planetary boundaries of our earth, the increasing economic importance of resource efficiency as well as the responsibility for the livelihoods of future generations require, today more than ever, the development of sustainable production solutions in which the necessary resources are used efficiently instead of being consumed. The energy-intensive industrial sectors, such as the foundry industry, are particularly committed to the set goals of sustainability and resource efficiency. In this context, an innovative technology and plant system for a flexible and automatable melt supply in non-ferrous foundries was developed and presented. This system enables closed-loop process control, which completely eliminates the previously mandatory ladling processes and reveals considerable potential for saving energy costs and CO2 emissions. As a consequent continuation, the presented paper focuses on a closer examination of the central innovative burner technology, which for the first time enables the safe and efficient processing of preheated combustion air with a control quality previously only known from electrical heating solutions. The effects that can be achieved with the new system in terms of energy demand and CO2 emissions, logistical flexibility, reduced production costs and the elimination of the previously required measures for melt cleaning (chemical and mechanical) are presented. Finally, the potentials for further sustainability improvements are discussed.