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Over the past several decades, metal Additive Manufacturing (AM) has transitioned from a rapid prototyping method to a viable manufacturing tool. AM technologies can produce parts on-demand, repair damaged components, and provide an increased freedom of design not previously attainable by traditional manufacturing techniques. The increasing maturation of metal AM is attracting high-value industries to directly produce components for use in aerospace, automotive, biomedical, and energy fields. Two leading processes for metal part production are Powder Bed Fusion with laser beam (PBF-LB/M) and Directed Energy Deposition with laser beam (DED-LB/M). Despite the many advances made with these technologies, the highly dynamic nature of the process frequently results in the formation of defects. These technologies are also notoriously difficult to control, and the existing machines do not offer closed loop control. In the present work, the application of various Machine Learning (ML) approaches and in-situ monitoring technologies for the purpose of defect detection are reviewed. The potential of these methods for enabling process control implementation is discussed. We provide a critical review of trends in the usage of data structures and ML algorithms and compare the capabilities of different sensing technologies and their application to monitoring tasks in laser metal AM. The future direction of this field is then discussed, and recommendations for further research are provided.

Digitalization is an opportunity for manufacturing SMEs to transform, not only for the flexibility and agility, but also for improved responsiveness in meeting customer requirements. Quality, being an integral part of customer-orientation, needs to be at the forefront of transformation under digitalization. Also, the digitalization is the need of the hour for continuous improvement to avoid disparity between paper-based quality practices and the digitalized engineering value chain. In this research article, authors intend to address the digitalization priorities of quality practices for Small and Medium Enterprises (SMEs) in perspective of adoption of Industry 4.0 technologies. From the literature survey, authors observed that, there is extensive literature available regarding quality and its relevance for continuous improvement, while research insights regarding integration of quality practices in a digital environment are limited and even if considered, they have not been studied holistically. Authors have examined the available research to assimilate the effect of current market trends on quality practices. They have undertaken a survey of prevailing quality maturity in manufacturing SMEs. In current study, authors have understood the need of digitalization of quality processes and propose the priority digital approaches that can guide SMEs with a holistic view of transformation including quality. The key findings based on proposed approaches for adopting digital quality processes are: Firstly, to address the challenges of shorter time-to-market, increased complexity, flexibility and innovation, SMEs need to adopt digitalization across the PDCA quality cycle. Secondly, digitalization of quality practices needs to be considered holistically across value chain. Thirdly, five processes have been prioritized for digitalization based on their adoption maturity i.e. design for quality, compliance, incoming & outgoing goods control, statistical process control and complaint management. Based on finding of this study authors recommend that manufacturing SMEs take cognizance of these findings and adopt digital quality to successfully thrive in the emerging global market.

Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical applicability for some analytes. This study introduces an extended method, regression-adjusted real-time quality control (RARTQC), to improve the performance of real-time quality control protocols. In contrast to the PBRTQC, RARTQC has an additional regression adjustment step before using a common statistical process control algorithm, such as the moving average, to decide whether an analytical error exists. We used all patient test results of 4 analytes in 2019 from Zhongshan Hospital, Fudan University, to compare the performance of the 2 frameworks. Three types of analytical error were added in the study to compare the performance of PBRTQC and RARTQC protocols: constant, random, and proportional errors. The false alarm rate and error detection charts were used to assess the protocols. The study showed that RARTQC outperformed PBRTQC. RARTQC, compared with the PBRTQC, improved the trimmed average number of patients affected before detection (tANPed) at total allowable error by about 50% for both constant and proportional errors. The regression step in the RARTQC framework removes autocorrelation in the test results, allows researchers to add additional variables, and improves data transformation. RARTQC is a powerful framework for real-time quality control research.

This work is aimed at developing methods for increasing the production of heavy crude oil while optimizing energy costs. Various methods have been studied for recovering heavy oil from deep reservoirs. Based on the developed methods, a number of dynamic models have been obtained that describe the behavior of the temperature field in the tubing. Estimations of thermal deformation are carried out. On the basis of dynamic models, fundamentally new devices are obtained and registered in the prescribed manner, providing a subsystem for automated process control systems.

Control charts are popular tools in the statistical process control toolkit and the exponentially weighted moving average (EWMA) chart is one of its essential component for efficient process monitoring. In the present study, a new Bayesian Modified-EWMA chart is proposed for the monitoring of the location parameter in a process. Four various loss functions and a conjugate prior distribution are used in this study. The average run length is used as a performance evaluation tool for the proposed chart and its counterparts. The results advocate that the proposed chart performs very well for the monitoring of small to moderate shifts in the process and beats the existing counterparts. The significance of the proposed scheme has proved through two real-life examples: (1) For the monitoring of the reaming process which is used in the mechanical industry. (2) For the monitoring of golf ball performance in the sports industry.

In order to monitor the quality of parts in printing, the methodology to monitor the geometric quality of the printed parts in fused deposition modeling process is researched. A non-contact measurement method based on machine vision technology is adopted to obtain the precise complete geometric information. An image acquisition system is established to capture the image of each layer of the part in building and image processing technology is used to obtain the geometric profile information. With the above information, statistical process control method is applied to monitor the geometric quality of the parts during the printing process. Firstly, a border signature method is applied to transform complex geometry into a simple distance-angle function to get the profile deviation data. Secondly, monitoring of the profile deviation data based on profile monitoring method is studied and applied to achieve the goal of layer-to-layer monitoring. In the research, quantile-quantile plot method is used to transform the profile deviation point cloud data monitoring problem into a linear profile relationship monitoring problem and EWMA control charts are established to monitor the parameters of the linear relationship to detect shifts occurred in the Fused Deposition Modeling process. Finally, laboratory experiments are conducted to demonstrate the effectiveness of the proposed approach.

The ongoing paradigm shift in industrial biotechnology requires advanced process control strategies for maximum productivity, especially for the anticipated future mass production of biotechnological food proteins.Molecular process control creates a missing link between molecular and macroscopic bioprocess design, thus offering multilayered control.The independent control of growth and product formation rates in fermentation processes is one of the key advantages of applying molecular process control.By using molecular process control as a tool for precision fermentation, the last mile in process optimization can be covered.High-performance bioprocesses and knowledge-based control solutions contribute to achieving the goals of the bioeconomy. The development of sustainable and economically competitive biotechnological processes is a central challenge of modern industrial biotechnology. Conventional strategies such as macroscopic and molecular bioprocess design are often insufficient to exploit their full potential. To circumvent this, molecular process control provides the missing link to further consolidate various optimization strategies to achieve multilayered process design. This review highlights the molecular mechanisms that can be exploited for molecular process control. These can either be endogenous or specifically implemented into the organism, and comprise regulatory mechanisms at the transcriptional, translational, and system levels. In addition to serving as a design tool to enhance existing bioprocesses, molecular process control is the gateway to biotechnological advances that will extend the boundaries of future process design. The development of sustainable and economically competitive biotechnological processes is a central challenge of modern industrial biotechnology. Conventional strategies such as macroscopic and molecular bioprocess design are often insufficient to exploit their full potential. To circumvent this, molecular process control provides the missing link to further consolidate various optimization strategies to achieve multilayered process design. This review highlights the molecular mechanisms that can be exploited for molecular process control. These can either be endogenous or specifically implemented into the organism, and comprise regulatory mechanisms at the transcriptional, translational, and system levels. In addition to serving as a design tool to enhance existing bioprocesses, molecular process control is the gateway to biotechnological advances that will extend the boundaries of future process design.

The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5 × 10 10  cm −2 down to 3.8 × 10 8  cm −2 or up to 1.4 × 10 11  cm −2 . Compared to traditional methods, our approach can dramatically expedite the optimization process and improve the reproducibility of MBE. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for optoelectronic and microelectronic industries. Finding the process parameters in molecular beam epitaxy for a specific density of quantum dots is a multidimensional optimization challenge. Here, the authors demonstrate real-time feedback controlled self-assembled InAs/GaAs QDs growth based on machine learning (ML) outputs.

Biopharmaceuticals have revolutionized the field of medicine in the types of active ingredient molecules and treatable indications. Adoption of Quality by Design and Process Analytical Technology (PAT) frameworks has helped the biopharmaceutical field to realize consistent product quality, process intensification, and real-time control. As part of the PAT strategy, Raman spectroscopy offers many benefits and is used successfully in bioprocessing from single-cell analysis to cGMP process control. Since first introduced in 2011 for industrial bioprocessing applications, Raman has become a first-choice PAT for monitoring and controlling upstream bioprocesses because it facilitates advanced process control and enables consistent process quality. This paper will discuss new frontiers in extending these successes in upstream from scale-down to commercial manufacturing. New reports concerning the use of Raman spectroscopy in the basic science of single cells and downstream process monitoring illustrate industrial recognition of Raman’s value throughout a biopharmaceutical product’s lifecycle. Finally, we draw upon a nearly 90-year history in biological Raman spectroscopy to provide the basis for laboratory and in-line measurements of protein quality, including higher-order structure and composition modifications, to support formulation development.

BackgroundThose working in healthcare today are challenged more than ever before to quickly and efficiently learn from data to improve their services and delivery of care. There is broad agreement that healthcare professionals working on the front lines benefit greatly from the visual display of data presented in time order.AimTo describe the run chart—an analytical tool commonly used by professionals in quality improvement but underutilised in healthcare.MethodsA standard approach to the construction, use and interpretation of run charts for healthcare applications is developed based on the statistical process control literature.DiscussionRun charts allow us to understand objectively if the changes we make to a process or system over time lead to improvements and do so with minimal mathematical complexity. This method of analyzing and reporting data is of greater value to improvement projects and teams than traditional aggregate summary statistics that ignore time order. Because of its utility and simplicity, the run chart has wide potential application in healthcare for practitioners and decision-makers. Run charts also provide the foundation for more sophisticated methods of analysis and learning such as Shewhart (control) charts and planned experimentation.