Process control
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Process defects and<i>in situ</i>monitoring methods in metal powder bed fusion: a review Open
Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications…
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In-process sensing in selective laser melting (SLM) additive manufacturing Open
Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated indus…
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In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review Open
Process monitoring and sensing is widely used across many industries for quality assurance, and for increasing machine uptime and reliability. Though still in the emergent stages, process monitoring is beginning to see strong adoption in t…
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Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing Open
Adoption of Quality by Design (QbD) principles, regulatory support of QbD, process analytical technology (PAT), and continuous manufacturing are major factors effecting new approaches to pharmaceutical manufacturing and bioprocessing. In t…
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Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0 Open
Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor appl…
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A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes Open
Additive manufacturing technologies are increasingly used in the development of new products. However, variations in part quality in terms of material properties, dimensional tolerances, surface roughness and defects limit its broader acce…
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Control Co‐Design: An engineering game changer Open
Over the last few decades, control engineers have focused on developing innovative control theories and algorithms to regulate systems. These control efforts are usually at the last stage of a sequential strategy that allows engineering de…
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Quality by control: Towards model predictive control of mammalian cell culture bioprocesses Open
The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end‐produc…
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Process Analytical Technologies – Advances in bioprocess integration and future perspectives Open
Process Analytical Technology (PAT) instruments include analyzers capable of measuring physical and chemical process parameters and key attributes with the goal of optimizing process controls. PAT in the form of a probe or sensor is design…
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Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification Open
Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods rec…
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In-Mold Sensors for Injection Molding: On the Way to Industry 4.0 Open
The recent trend in plastic production dictated by Industry 4.0 demands is to acquire a great deal of data for manufacturing process control. The most relevant data about the technological process itself come from the mold cavity where the…
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LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors Open
This work thoroughly compares the efficiency of Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Unit (GRU) neural networks as models of the dynamical processes used in Model Predictive Control (MPC). Two simulated industrial pr…
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Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing Open
Metal-based powder-bed-fusion additive manufacturing (PBF-AM) is gaining increasing attention in modern industries, and is a promising direct manufacturing technology. Additive manufacturing (AM) does not require the tooling cost of conven…
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Bioprocess Control: Current Progress and Future Perspectives Open
Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due …
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Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm Open
The concentration of dissolved oxygen (DO) in the aeration tank(s) of an activated sludge system is one of the most important process control parameters. The DO concentration in the aeration tank(s) is maintained at a desired level by usin…
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Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes Open
The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain …
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Intelligent Injection Molding on Sensing, Optimization, and Control Open
Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimi…
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Real-time model predictive control of a wastewater treatment plant based on machine learning Open
Two separate goals should be jointly pursued in wastewater treatment: nutrient removal and energy conservation. An efficient controller performance should cope with process uncertainties, seasonal variations and process nonlinearities. Thi…
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In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing Open
In recent years, technological advancements have led to the industrialization of the laser powder bed fusion process. Despite all of the advancements, quality assurance, reliability, and lack of repeatability of the laser powder bed fusion…
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Membrane Crystallization for Process Intensification and Control: A Review Open
Crystallization is a fundamental separation technology used for the production of particulate solids. Accurate nucleation and growth process control are vitally important but difficult. A novel controlling technology that can simultaneousl…
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Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review Open
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes …
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Temperature measurements during friction stir welding Open
The increasing industrial demand for lighter, more complex and multi-material components supports the development of novel joining processes with increased automation and process control. Friction stir welding (FSW) is such a process and h…
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Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning Open
Machine learning has attracted extensive interest in the process engineering field, due to the capability of modeling complex nonlinear process behavior. This work presents a method for combining neural network models with first-principles…
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On-line soft sensing in upstream bioprocessing Open
This review provides an overview and a critical discussion of novel possibilities of applying soft sensors for on-line monitoring and control of industrial bioprocesses. Focus is on bio-product formation in the upstream process but also th…
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The case for digital twins in metal additive manufacturing Open
The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its …
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Reinforcement learning for industrial process control: A case study in flatness control in steel industry Open
Funding Information: This work was supported by China Scholarship Council (No. 202006080008 ), the National Natural Science Foundation of China (Grant Nos. 52074085 and U21A20117 ), the Fundamental Research Funds for the Central Universiti…
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In-process closed-loop control for stabilising the melt pool temperature in selective laser melting Open
Additive manufacturing processes are gaining more importance in the industrial production of metal components, as they enable complex geometries to be produced with less effort. The process parameters used to manufacture a wide variety of …
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Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic Open
The implementation of automation in the multistep flow synthesis is essential for transforming laboratory-scale chemistry into a reliable industrial process. In this review, we briefly introduce the role of automation based on its applicat…
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Quality Prediction and Control in Wire Arc Additive Manufacturing via Novel Machine Learning Framework Open
Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited sha…
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Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing Open
In metal additive manufacturing (AM), the material microstructure and part geometry are formed incrementally. Consequently, the resulting part could be defect- and anomaly-free if sufficient care is taken to deposit each layer under optima…