Flexible Process Research Articles

The response of the Irish pharmaceutical manufacturing sector to the COVID-19 pandemic with a focus on Lean and JIT.

Just in Time (JIT) and Lean manufacturing are concepts that originated in the automotive industry and were then adopted by pharmaceutical and biopharmaceutical companies during the 1990s. However, the Covid-19 pandemic and the urgent demand for pharmaceutical treatment challenged JIT and Lean manufacturing processes. Production of Covid-19-related medicines increased, putting pressure on global supply chains and operations. This also hindered the production of medicines using the same or similar materials. Thus, questions are raised concerning JIT and Lean supply chains in the pharmaceutical industry. The present study aimed to explore (1) if material and supply constraints occurred due to the Covid-19 pandemic, (2) how companies were impacted and managed and (3) if changes are required to future proof the JIT supply chain approach for future global events. A mixed-method cross-sectional survey design was used and focused on material supply, qualification and validation in Irish pharmaceutical manufacturing sites. Employees working in the Irish pharmaceutical manufacturing industry were recruited using convenience sampling through online advertisement using the social media platform 'LinkedIn'. Quantitative data was analysed using percentages and qualitative data from free-text responses were used to add context to the quantitative survey questions. A total of 41 participants were recruited. The results suggested that the pandemic had a negative effect on material availability according to 81% of participants. This translated to delays or stoppage of production activity and was mainly handled by sourcing new materials (70%). To cope with future global crises, 60% of participants recommended more flexibility in future validation processes while 78% of participants acknowledged the importance of validating additional suppliers. A hybrid model of manufacturing and supply chain management was also a preferred approach to exclusive Lean and JIT (42%). The production of non-Covid-19 medicines was adversely affected by the Covid-19 pandemic, but the pharmaceutical industry in Ireland demonstrated resilience and collaboration in response to these challenges. This study suggests that the JIT and Lean manufacturing model should be adjusted to ensure medicine supply chains are not disrupted during future global events.

Design and Optimization of Self-Supporting Surfaces with Arch Beams.

The paper presents a new method for constructing self-supporting surfaces using arch beams that are designed to convert their thrust into supporting force, thereby eliminating shear stress and bending moments. Our method allows for the placement of the arch beams on the boundary or within a surface and partitions the surface into multiple self-supporting parts. The use of arch beams enhances stability and durability, adds aesthetic appeal, and allows for greater flexibility in the design process. We develop an iterative algorithm for designing selfsupporting surfaces with arch beams that enables the user to control the shape of the beams and surface through intuitive parameters and specify the desired location of the arch beams. We verify the physical stability of the structure using finite element analysis. Experimental results show that our method can produce visually pleasing self-supporting surfaces that satisfy the equilibrium equation with high accuracy.

A robust data analytical method to investigate sequence dependence in flow-based peptide synthesis.

Computer-assisted methods, which hold the promise to transform synthetic organic chemistry, are often limited by experimental data lacking in quality, diversity, and quantity. In solid-phase peptide synthesis (SPPS), automated flow chemistry is well-suited to deliver such data, which is key for prediction and optimization of sequence-dependent "difficult couplings", and insights obtained in flow-SPPS can be transferred to batch-SPPS. The current data analysis techniques rely on the height and the width of fluorenylmethoxycarbonyl (Fmoc) deprotection peaks and perform well under standard conditions. Yet any deviation in parameters (e.g. temperature, flow rate, resin loading) leads to incomplete capture of information and exclusion from the dataset. Here, we present a flexible and robust processing and analysis method that is based on the Gaussian shape of the deprotection peaks to overcome these challenges, which drastically increases the interpretable size of our data set. Using this straightforward method retains the full information and data quality while the generation of hazardous dimethylformamide solvent waste is reduced by 50%. Overall, this work highlights how the interplay between synthetic and computational analysis enables the collection of high-quality data even under non-ideal, non-standard conditions.

Digital twin architecture for assembly line performance monitoring

The forthcoming industrial requirements to ensure increasingly flexible and agile production processes will require an ever-increasing ability to recognise, predict and optimise the state of processes with respect to the planned state. Advanced tools such as Digital Twins (DT) and Cyber Physical Systems (CPS) can help manage and address these problems. This paper presents some results of the authors’ research on the adoption of a basic methodology for the design and implementation of Digital Twins aimed at manufacturing applications and subsequent integration into a Cyber Physical Production Systems (CPPS). Reference publications were selected based on the level of detail of DT and CPS architectures. The aim is also to combine methodological development with industrial tools and requirements. The architecture is potentially applicable to different production scenarios. Finally, the first results of application on a scale production line are shown.

Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities

The advent of biobanks with vast quantities of medical imaging and paired genetic measurements creates huge opportunities for a new generation of genotype–phenotype association studies. However, disentangling biological signals from the many sources of bias and artifacts remains difficult. Using diverse medical images and time-series (ie, magnetic resonance imagings [MRIs], electrocardiograms [ECGs], and dual-energy X-ray absorptiometries [DXAs]), we show how registration, both spatial and temporal, guided by domain knowledge or learned de novo, helps uncover biological information. A multimodal autoencoder comparison framework quantifies and characterizes how registration affects the representations that unsupervised and self-supervised encoders learn. In this study we (1) train autoencoders before and after registration with nine diverse types of medical image, (2) demonstrate how neural network-based methods (VoxelMorph, DeepCycle, and DropFuse) can effectively learn registrations allowing for more flexible and efficient processing than is possible with hand-crafted registration techniques, and (3) conduct exhaustive phenotypic screening, comprised of millions of statistical tests, to quantify how registration affects the generalizability of learned representations. Genome- and phenome-wide association studies (GWAS and PheWAS) uncover significantly more associations with registered modality representations than with equivalently trained and sized representations learned from native coordinate spaces. Specifically, registered PheWAS yielded 61 more disease associations for ECGs, 53 more disease associations for cardiac MRIs, and 10 more disease associations for brain MRIs. Registration also yields significant increases in the coefficient of determination when regressing continuous phenotypes (eg, 0.36 ± 0.01 with ECGs and 0.11 ± 0.02 for DXA scans). Our findings reveal the crucial role registration plays in enhancing the characterization of physiological states across a broad range of medical imaging data types. Importantly, this finding extends to more flexible types of registration, such as the cross-modal and the circular mapping methods presented here.

Corrosion behavior of selective laser melted 6061 aluminum alloy electrodes for aluminum-air battery

6061 aluminum alloy can be used as an anode for aluminum-air batteries. Compared with cast one, three-dimensional (3D) printed aluminum alloy shows more flexible design and production process. Moreover, the self-corrosion behavior of 3D printed aluminum alloy without grinding its surface layer in alkaline solutions (electrolyte for aluminum-air batteries) is unclear. Here, the comparison between 3D-printed and cast 6061 aluminum alloy is investigated by self-corrosion testing, potentiodynamic polarization, electrochemical impedance spectroscopy, and hydrogen bubble visualization experimentation. The results show that although the corrosion resistance of the 3D-printed 6061 aluminum alloy decreases rapidly with time, it exhibits higher corrosion resistance at the initial stage of corrosion than that of cast 6061 aluminum alloy due to the “fish scale” layer and may be able to improve its discharge performance. The surface microscopy analysis results are in good agreement with those of the self-corrosion test and electrochemical characteristics.

New polishing concepts for optical components in flexible and efficient process chains

The flexible and efficient production of precise optical free-form surfaces requires advanced kinematic operating principles. Due to the complex surface geometries, polishing processes with point or linear contact are used. These generally result in longer polishing times due to the smaller footprint compared to large-area polishing tools. The creation of a high precision, polishable surface in the shaping steps of the process chain is therefore of great importance. This applies in particular to minimising the roughness, but also the depth of SSD (sub-surface damage) and the mid-spatial frequency errors before the polishing process. The article describes selected process chains for the production of free-form optics and presents options for optimising the required polishing steps.

КУЛЬТУРА БЕЗПЕРЕРВНОГО НАВЧАННЯ ТА СТРАТЕГІЇ ЇЇ РОЗВИТКУ

In the conditions of rapid technological changes and intensified competition, the ability of organizations to quickly adapt and innovate becomes a critical success factor. The formation of a culture of continuous learning, which encourages the continuous development of employee competencies, knowledge sharing and readiness for change, changes the key in ensuring the adaptability and competitiveness of the role of organizations. The article examines the importance of a culture of continuous learning and its impact on the effectiveness of teams and organizations. The authors address the key elements of such a culture, including leadership that encourages open dialogue and experimentation, psychological safety, flexible work processes, investment in staff development, structures for expanding knowledge, and systems of recognition and reward for learning. The article provides practical recommendations for creating an environment conducive to continuous learning and talent development. which emphasizes the importance of leadership that demonstrates commitment to the values of learning, radical openness and respect for diversity of opinion. It also highlights the role of psychological safety in encouraging employees to share ideas, experiment and recognize mistakes as opportunities for growth. Empirical studies confirm the positive impact of learning culture on innovativeness, productivity and adaptability of teams and organizations. The authors present an example of a questionnaire for measuring the level of psychological safety and interpreting the results, which allows organizations to monitor progress and identify areas for improvement. A comprehensive strategy for the development of a culture of lifelong learning, which covers both structural and cultural aspects of the organization, is also proposed. At the end, the direction of further research is outlined, including the empirical validation of approaches in various fields, the study of the impact of digital technologies on educational processes, the analysis of the relationship between the culture of learning and indicators of organizational effectiveness, as well as strategies for adaptation to the specifics of different types of organizations.

The Applications of Generative Adversarial Networks in Architecture Design: A Systematic Review

Purpose: To review and analyze the research status of the application of generative adversarial networks (GANs) in the field of architecture design. Methods: This study followed the latest PRISMA guidelines and systematically collected relevant research literature from domestic and international sources. In-depth analysis was conducted on 95 representative Chinese and English language articles. During the analysis process, three dimensions of intelligent architectural generation methods were comprehensively and deeply explored: the data application scale, algorithm type, and architectural design process. Results: (1) GANs have been applied to various processes in architectural design practice, from design generation, development, and evaluation to final solution expression; (2) GANs can be applied at different architectural scales, ranging from large-scale complex functional buildings to refined layout designs of residential units; (3) the appearance of GANs enables designers to quickly present design results at various stages, greatly improving work efficiency; and (4) at present, GANs also have many shortcomings, mainly reflected in the accuracy of the data and the usability of tools. Conclusion: Improvements in computer performance and the enrichment of data resources have promoted the widespread application of intelligent architectural generation methods. GANs have great potential in the field of architectural design and can provide a more efficient, flexible, and diverse design process. However, further research and development are needed to address the challenges and limitations of using GANs in this field.

Bayesian optimization algorithm based support vector regression analysis for estimation of resilient modulus of crushed rock materials for pavement design

The precise determination of the resilient modulus (MR) of the base and sub-base materials is a major preoccupation and a key criterion in the flexible pavement design process. The experimental determination of MR implies a challenging process which requires usually very difficult test procedures and extreme precautions and manpower. This is why soft computing techniques are increasingly popular and of growing importance. Many prediction techniques based primarily on linear and non-linear regression could not provide flexible use and consistent prediction of MR for practical engineering. This article introduces a hybrid of the Bayesian optimization algorithm (BOA) and support vector regression (SVR) as a new modelling tool for the MR prediction of crushed stone materials used as base and sub-base layers for pavement design. For this purpose, an experimental database was utilized to generate the hybrid BOA-SVR model of indirect estimation of the resilient modulus based on material type, basic engineering characteristics and loading conditions. The database consists of 260 experimental datasets obtained from repeated loading triaxial tests performed by the laboratory of the Central Transportation Agency located in Algiers, Algeria. To develop the model, all hyperparameters were optimised using the BOA technique. It was found that the average, median, standard deviation, minimum, maximum and interquartile range of the expected values of the developed hybrid model are very close to the experimental results. Results revealed that the hybrid BOA-SVR model predict the MR of the crushed stone materials with a coefficient of determination of 99.91% and root mean squared error of 3.55. Comparisons with traditional and other Artificial intelligence models showed that BOASVR hybrid model predictions are more accurate and robust than those of other models.

Workpiece Pose Classification Framework for Flexible Part Feeding Systems Using Machine Learning and Synthetic Datasets

In previous work, a concept for a novel flexible part feeding system based on aerodynamic feeding was presented. In contrast to conventional part feeding systems, such as vibratory bowl feeders, aerodynamic part feeding systems use controlled pressurized air jets to reorient the workpieces. The proposed concept aims to increase flexibility and eliminate retooling times to meet the challenges of modern automated production with regard to increasing uncertainties, shorter product life cycles and higher cost pressure. Regarding these objectives, the determination of the workpiece poses in the feeding system requires flexible image processing, which will be enabled using machine learning and synthetic datasets.This work presents a framework which enables the classification of workpiece poses using a standard industrial camera and convolutional neural networks (CNN). The training of CNNs usually requires large datasets. However, in order to eliminate the need to create and label datasets of real images, which would cause machine downtimes and increase the retooling effort, the CNNs will be trained with synthetic datasets. Based on the CAD-data of the workpieces, artificial images of each pose are rendered using the open source engine Blender. Several CNN architectures are selected, trained with the artificial datasets and evaluated using real images of the workpieces. The results show that high classification accuracies of over 99 % can be achieved. It is concluded that the presented approach enables the classification of workpiece poses with high accuracy, while eliminating the need for the cumbersome creation of real image datasets. The approach is not limited to the use in the aerodynamic part feeding system but can be transferred to other applications in which the orientation of a workpiece has to be classified.

Experimental validation of the plane-based penetration calculation for the gear skiving of internal gears

Gear skiving is a flexible and productive process for the manufacturing of internal and external gears. Due to the variable cutting conditions and the complex process kinematics, a simulative investigation of the skiving process is necessary for an efficient and economic process design. The plane-based penetration calculation is an established method for simulating the machining of gears. In this paper, this simulation approach is validated based on experimental results for the gear skiving process of internal gears. For almost all simulation results, the results of the gear skiving simulation for internal gears agreed with the experimental results.

Designing a System to Facilitate the process of Connecting 4.0-Generation Industrial Machines to the internet

Industry 4.0 aims to create more efficient and flexible manufacturing processes that can respond quickly to changes in demand and customer requirements. The integration of digital technologies and physical systems allows data to be collected, analysed, monitored and controlled in real time. This can increase productivity, reduce downtime and improve product quality. In this paper, remote control, monitoring and online data storage of industrial machines based on human-machine interface (HMI) module are facilitated. HMI integrates the control system with the Industrial Internet of Things (IIOT). On the other hand, it can be controlled and monitored anywhere in the world via the Internet using a Virtual Private Network (VPN) to open different navigation private channel and HMI interface. It was clear from the collection of recorded data and the results of practical testing that the designed system facilitated the process of accessing the industrial machine

Efficient Privacy-Friendly and Flexible Wearable Data Processing With User-Centric Access Control

Efficient Privacy-Friendly and Flexible Wearable Data Processing With User-Centric Access Control

Influence of electrode characteristics on the laser cutting of lithium-ion battery anodes

Laser cutting of lithium-ion battery (LIB) electrodes is a highly flexible process receiving wider industry adoption. However, the manufacturing of LIB electrodes involves numerous steps, where variations can impact the electrode cutting process. Additionally, material chemistry is identified as one of the influential parameters affecting laser cutting. There are numerous combinations of state-of-the-art laser sources and anode materials. Conducting extensive examinations would be necessary to choose the optimized laser source for electrode cutting. On the other hand, understanding the influence of thickness and chemistry of anode materials on the processability is important. This study examines methodologically the characteristics of the anodes and their impact on laser cutting performance. The effects of electrode thickness and active material chemistry on the laser cutting performance using a ps-pulsed laser with burst mode capability were studied. Examinations were conducted on four various combinations of LIB anodes, encompassing four different thicknesses and two distinct chemistries.

An Integrated Decision-Making Method of Flexible Process Plan and Cutting Parameter Considering Dynamic Machining Resources

An Integrated Decision-Making Method of Flexible Process Plan and Cutting Parameter Considering Dynamic Machining Resources

Art and design entanglements for renewable energy education: Renewable energy art and design approach

The ongoing energy transition presents a necessary shift towards renewable sources, such as wind and solar power, in the face of the challenges presented by the changing climates. This change, however, requires an understanding of the operation of renewable energy systems. In other words, the shift towards clean energy generation demands education. By combining art, design, and renewable energy education the renewable energy art and design (READ) approach provides a path for renewable energy education. READ approach is a customizable and flexible process developed to enable the creation of solar-powered devices while sharing knowledge on diverse subjects such as art, design, electronics, electricity, and computer science. Due to its versatility, READ is expected to enhance energy education and contribute to the transition towards a renewable-powered society.

Method for the Derivation of Flexible Process Modules

Matrix production systems are modular, cycle time-independent, and flow-oriented production systems. They combine flexibility plus productivity and consist of flexibly linked and freely accessible process modules. The derivation and design of these process modules in flexible structures is still very time-consuming due to many degrees of freedom and limiting constraints. This paper presents a method for deriving flexible process modules, taking into account the creation of increased automation potentials, flexible order flows, and specialization in processes. The method consists of seven steps to derive harmonized process modules for multiple products. It is suitable for all manufacturing industries to reduce the planning effort. The defined process modules can be further used for layout planning.

Comparative Evaluation of the Power-to-Methanol Process Configurations and Assessment of Process Flexibility

This paper compares different power-to-methanol process configurations encompassing electrolyser, adiabatic reactor (s) and methanol purification configurations. Twelve different power-to-methanol configurations based on direct CO2 hydrogenation with H2 derived from H2O-electrolysis...

A first approach of Laser Mesh Cladding

Controlling the heat input and exploring alternate feedstocks for DED is necessary to improve the versatility of this technique. A novel approach DED is investigated by implementing a new control method for energy beam scanning oscillation and utilising a new feedstock: metallic meshes. This represents the first exploration of ‘laser mesh cladding (LMC)’. Under certain conditions, the clads show similar results of wire and powder DED producing dense clads with no large pores, grain refinements at the edges of the clads and a layer height of 0.6 mm. Secondly, with the same laser process parameters for two different meshes, LMC produced fully dense clads with the denser mesh creating slightly smaller melted area. This then manifests as a new cladding operation where material input and energy input can be manipulated during the deposition for a total process flexibility.