Hasil untuk "Manufactures"

Mingyang Gong, Brendan Mumey

Motivated by green manufacturing, this paper investigates a scheduling with rejection problem subject to an energy consumption constraint. Machines are associated with non-uniform energy consumption rates, defined as the energy consumed per unit time. Each job is either rejected with a rejection penalty or accepted and scheduled on some machine for processing, which incurs energy consumption. The problem aims to minimize the makespan of the accepted jobs plus the total penalty of the rejected jobs while the total energy consumption is bounded by a given threshold. In this paper, when the number of machines is part of the input, we develop the first $(2+ε)$-approximation algorithm for any fixed constant $ε$ and a simple QPTAS as well as a PTAS for uniform energy consumption rates. Moreover, we present an FPTAS when the number of machines is a fixed constant.

Mohammed Aatif Shahab, Francesco Destro, Richard D. Braatz

The biopharmaceutical industry is increasingly developing digital twins to digitalize and automate the manufacturing process in response to the growing market demands. However, this shift presents significant challenges for human operators, as the complexity and volume of information can overwhelm their ability to manage the process effectively. These issues are compounded when digital twins are designed without considering interaction and collaboration with operators, who are responsible for monitoring processes and assessing situations, particularly during abnormalities. Our review of current trends in biopharma digital twin development reveals a predominant focus on technology and often overlooks the critical role of human operators. To bridge this gap, this article proposes a collaborative intelligence framework that emphasizes the integration of operators with digital twins. Approaches to system design that can enhance operator trust and human-machine interface usability are presented. Moreover, innovative training programs for preparing operators to understand and utilize digital twins are discussed. The framework outlined in this article aims to enhance collaboration between operators and digital twins effectively by using their full capabilities to boost resilience and productivity in biopharmaceutical manufacturing.

Chuan Zhang, Roland Böhm, Eduard Boos et al.

State-of-the-art additive manufacturing technologies are not only finding ever-wider applications in everyday life, but also assuming an increasingly important role in scientific research. This kind of advanced manufacturing method eliminates many of the constraints of conventional processes in fabricating components with complex external shapes or intricate internal structures, thereby providing enhanced flexibility for the design and realization of a new generation of more efficient particle accelerators and storage rings. The RACERS team initiated by the Stochastic Cooling Group at GSI, Germany, is worldwide one of the first teams working on this topic. Based on the metal 3D-printing technology, two novel accelerating structures and one efficient cooling plate for a future stochastic cooling system are under development at GSI and for the FAIR project, respectively. Some successful experience as well as learnt lessons will be presented.

Manonmani Sekar, Nasim Nezamoddini

Reconfigurable manufacturing systems (RMS) are critical for future market adjustment given their rapid adaptation to fluctuations in consumer demands, the introduction of new technological advances, and disruptions in linked supply chain sections. The adjustable hard settings of such systems require a flexible soft planning mechanism that enables realtime production planning and scheduling amid the existing complexity and variability in their configuration settings. This study explores the application of multi agent reinforcement learning (MARL) for dynamic scheduling in soft planning of the RMS settings. In the proposed framework, deep Qnetwork (DQN) agents trained in centralized training learn optimal job machine assignments in real time while adapting to stochastic events such as machine breakdowns and reconfiguration delays. The model also incorporates a negotiation with an attention mechanism to enhance state representation and improve decision focus on critical system features. Key DQN enhancements including prioritized experience replay, nstep returns, double DQN and soft target update are used to stabilize and accelerate learning. Experiments conducted in a simulated RMS environment demonstrate that the proposed approach outperforms baseline heuristics in reducing makespan and tardiness while improving machine utilization. The reconfigurable manufacturing environment was extended to simulate realistic challenges, including machine failures and reconfiguration times. Experimental results show that while the enhanced DQN agent is effective in adapting to dynamic conditions, machine breakdowns increase variability in key performance metrics such as makespan, throughput, and total tardiness. The results confirm the advantages of applying the MARL mechanism for intelligent and adaptive scheduling in dynamic reconfigurable manufacturing environments.

Wolfgang Rannetbauer, Simon Hubmer, Carina Hambrock et al.

The implementation of thermally sprayed components in steel manufacturing presents challenges for production and plant maintenance. While enhancing performance through specialized surface properties, these components may encounter difficulties in meeting modified requirements due to standardization in the refurbishment process. This article proposes updating the established coating process for thermally spray coated components for steel manufacturing (TCCSM) by integrating real-time data analytics and predictive quality management. Two essential components--the data aggregator and the quality predictor--are designed through continuous process monitoring and the application of data-driven methodologies to meet the dynamic demands of the evolving steel landscape. The quality predictor is powered by the simple and effective multiple kernel learning strategy with the goal of realizing predictive quality. The data aggregator, designed with sensors, flow meters, and intelligent data processing for the thermal spray coating process, is proposed to facilitate real-time analytics. The performance of this combination was verified using small-scale tests that enabled not only the accurate prediction of coating quality based on the collected data but also proactive notification to the operator as soon as significant deviations are identified.

Chathurangi Shyalika, Ruwan Wickramarachchi, Fadi El Kalach et al.

Rare events are occurrences that take place with a significantly lower frequency than more common regular events. In manufacturing, predicting such events is particularly important, as they lead to unplanned downtime, shortening equipment lifespan, and high energy consumption. The occurrence of events is considered frequently-rare if observed in more than 10% of all instances, very-rare if it is 1-5%, moderately-rare if it is 5-10%, and extremely-rare if less than 1%. The rarity of events is inversely correlated with the maturity of a manufacturing industry. Typically, the rarity of events affects the multivariate data generated within a manufacturing process to be highly imbalanced, which leads to bias in predictive models. This paper evaluates the role of data enrichment techniques combined with supervised machine-learning techniques for rare event detection and prediction. To address the data scarcity, we use time series data augmentation and sampling methods to amplify the dataset with more multivariate features and data points while preserving the underlying time series patterns in the combined alterations. Imputation techniques are used in handling null values in datasets. Considering 15 learning models ranging from statistical learning to machine learning to deep learning methods, the best-performing model for the selected datasets is obtained and the efficacy of data enrichment is evaluated. Based on this evaluation, our results find that the enrichment procedure enhances up to 48% of F1 measure in rare failure event detection and prediction of supervised prediction models. We also conduct empirical and ablation experiments on the datasets to derive dataset-specific novel insights. Finally, we investigate the interpretability aspect of models for rare event prediction, considering multiple methods.

W. Streit Cunningham, Eric Lang, David J. Sprouster et al.

A large body of literature within the additive manufacturing (AM) community has focused on successfully creating stable tungsten (W) microstructures due to significant interest in its application for extreme environments. However, solidification cracking and additional embrittling features at grain boundaries have resulted in poorly performing microstructures, stymying the application of AM as a manufacturing technique for W. Several alloying strategies, such as ceramic particles and ductile elements, have emerged with the promise to eliminate solidification cracking while simultaneously enhancing stability against recrystallization. In this work, we provide new insights regarding the defects and microstructural features that result from the introduction of ZrC for grain refinement and NiFe as a ductile reinforcement phase - in addition to the resulting thermophysical and mechanical properties. ZrC is shown to promote microstructural stability with increased hardness due to the formation of ZrO2 dispersoids. Conversely, NiFe forms into micron-scale FCC phase regions within a BCC W matrix, producing enhanced toughness relative to pure AM W. A combination of these effects is realized in the WNiFe+ZrC system and demonstrates that complex chemical environments coupled with the tuning of AM microstructures provides an effective pathway for enabling laser AM W materials with enhanced stability and performance.

Eleni Zavrakli, Andrew Parnell, Subhrakanti Dey

With the rapid development of Additive Manufacturing (AM) comes an urgent need for advanced monitoring and control of the process. Many aspects of the AM process play a significant role in the efficiency, accuracy and repeatability of the process, with temperature regulation being one of the most important ones. In this work, we solve the problem of optimal tracking control for a state space temperature model of a Big Area Additive Manufacturing (BAAM) system. In particular, we address the problem of designing a Linear Quadratic Tracking (LQT) controller when access to the exact system state is not possible, except in the form of measurements. We initially solve the problem with a model-based approach based on reinforcement learning concepts, with state estimation through an observer. We then design a model-free reinforcement-learning based controller with an internal state estimation step and demonstrate its performance through a simulator of the systems' behaviour. Our results showcase the possibility of achieving comparable results while learning optimal policies directly from process data, without the need for an accurate, intricate model of the process. We consider this outcome to be a significant stride towards autonomous intelligent manufacturing.

Mahmoud Yaseen, Dewen Yushu, Peter German et al.

Advanced Manufacturing (AM) has gained significant interest in the nuclear community for its potential application on nuclear materials. One challenge is to obtain desired material properties via controlling the manufacturing process during runtime. Intelligent AM based on deep reinforcement learning (DRL) relies on an automated process-level control mechanism to generate optimal design variables and adaptive system settings for improved end-product properties. A high-fidelity thermo-mechanical model for direct energy deposition has recently been developed within the MOOSE framework at the Idaho National Laboratory (INL). The goal of this work is to develop an accurate and fast-running reduced order model (ROM) for this MOOSE-based AM model that can be used in a DRL-based process control and optimization method. Operator learning (OL)-based methods will be employed due to their capability to learn a family of differential equations, in this work, produced by changing process variables in the Gaussian point heat source for the laser. We will develop OL-based ROM using Fourier neural operator, and perform a benchmark comparison of its performance with a conventional deep neural network-based ROM.

Muhammad Awais Khan

Purpose – Building on the self-determination theory (SDT), the purpose of this study is to empirically examine the influence of work meaningfulness (WM) on employees' involvement in playful work design (PWD) in the context of software development firms in Pakistan. Design/methodology/approach – For the present study, a two-wave employee survey (online questionnaire) was used for data collection. The data were collected through an adopted questionnaire from employees working in software development firms in Pakistan. structural equation modeling and Hayes Process Macro of SPSS were used to analyze data collected from 305 respondents. Findings – The findings of this study show that work meaningfulness and epistemic curiosity (EC) positively and significantly influence employee playful work design strategies. Moreover, the relationship between work meaningfulness and playful work design was partially mediated by employee epistemic curiosity. This mediating role of epistemic curiosity is strengthened by the presence of the perceived leader's autonomous support (LAS). Research limitations/implications – Employees improve their personal work experience through playful work design. Theoretically, this study contributes to the body of knowledge on the factors (work meaningfulness, epistemic curiosity and leader's autonomous support) that can influence employees' self-determination to design fun and competition into their work. This study contributes to the theory by introducing the antecedents (work meaningfulness and epistemic curiosity), of employee playful work design and explores the role of epistemic curiosity as a mediator and the leader's autonomous support as a moderator through SDT perspective. Practical implications – For practitioners, this study pinpoints that software development firms can consider improving employees' perception of work meaningfulness, which can lead them to become epistemically curious to proactively design their work experience for their psychological need fulfillment, well-being and better functioning. Moreover, leader's autonomous support can support involvement in playful work design. Originality/value – The current study is the first investigation in the Asian context to study the antecedents of playful work design and a critical boundary condition. This study extends the literature on the antecedents of employee playful work design and explores the role of epistemic curiosity as a mediator and the leader's autonomous support as a moderator specifically through a self-determination perspective.

Maximilian Kertel, Stefan Harmeling, Markus Pauly

Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to existing applications, we do not assume linear relationships leading to more informative results.

Meadhbh O'Neill, Jeff Morgan, Kevin Burke

Process visualizations of data from manufacturing execution systems (MESs) provide the ability to generate valuable insights for improved decision-making. Industry 4.0 is awakening a digital transformation where advanced analytics and visualizations are critical. Exploiting MESs with data-driven strategies can have a major impact on business outcomes. The advantages of employing process visualizations are demonstrated through an application to real-world data. Visualizations, such as dashboards, enable the user to examine the performance of a production line at a high level. Furthermore, the addition of interactivity facilitates the user to customize the data they want to observe. Evidence of process variability between shifts and days of the week can be investigated with the goal of optimizing production.

Magdalena Broda

Erratum

Ika Suciati, Indah Susilowati

The potential of fisheries resources is still massive as well as the challenges.  One of the challenges faced in the fisheries sector is climate change occurring massively. Climate change that occurs disrupts the productivity and activities of fishermen. Fishermen live with uncertainty because their livelihoods are directly related to nature. So that fishermen are required to be able to adapt and mitigate to climate change that is occurring rapidly. Information innovation and communication are widely developed to help fishermen in sea activities. The technology created is available in various forms, such as android-based applications, SMS broadcasts, Whatsapp groups, GPS, Fishfinder, etc. The objective of this study is to identify fishermen's perception of the benefits of technology and service communication used, ICT relationship with fishermen's income, and stakeholder role strategies in the use of ICT. The mix-method approach is used to acknowledge study objectives using the software SPSS 23 and Atlas. Ti 8. The result shows that fishermen's perception of the benefits of ICT namely facilitating communication, reducing production costs, improving safety, increasing fishermen's knowledge, and increasing income. There is a relationship between the use of ICT and fishermen's income, as well as stakeholders who have an important role in the use of ICT in the fishing community.

Jože M. Rožanec, Patrik Zajec, Klemen Kenda et al.

There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users' feedback, and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed as a general concern. We align the proposed architecture with the Big Data Value Association Reference Architecture Model. We tailor it for the domain of demand forecasting and validate it on a real-world case study.

Benjamin Maschler, Thi Thu Huong Pham, Michael Weyrich

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated and compared based on a real industrial metal forming dataset.

OLARU Sabina, POPESCU Georgeta, TOMA Doina et al.

The paper presents the virtual prototyping of protective clothing for oversized female and male subjects, highlighting the importance of personalization and its competitive advantages. Customisation of protective clothing for oversized subjects offered the possibility to individualise the products to each wearer with different conformation and specific work activities. Customized protective clothing involves the dimensional and conformational aspects of the body, respectively the product size as well as the quality-linked functionality criterion, aspects regarding its wearability and protection tested in accredited laboratories, the effects over the individual comfort. The research implementation involved 3D body scanning for analysis and determination of anthropometric measurements and conformation, 3D CAD technology for automatic rapid design of patterns in Made to measure system, modeling and simulation of product in the virtual environment on customized mannequin, highlighting the body-product correspondence. New technologies in the context of Industry 4.0 are the seeds for disruptive innovation in the clothing industry, by increasing the digital capacity and flexibility to satisfy the customer requests and implement more dedicated services.

Siti Fatimah, Mirza Chusnainy, Fifi Khumairo et al.

PT Aplikasi Karya Anak Bangsa or Gojek is an online transportation service company that was founded in 2010.The research objective is to analyze and describe Gojek's marketing strategy through YouTube social media. This research uses a qualitative approach, with analysis using netnographic studies. The research subjects are 20 netizens who commented on Gojek's Indonesian Youtube account related to the video marketing advertising Gojek. The results showed that Gojek's marketing strategy made use of the internet as a means to promote the services offered. The promotion process is very intensively carried out by the Gojek Company, one of which is through social media. With the sophistication of advertising done through media social, users will automatically be treated to various promotions from the CompanyGojek when opening its social media pages. This strategy is very appropriate because almost the entire process of purchasing a Gojek service is done ina smartphone application, this means that the potential consumers targeted are in accordance with the ad audience that is installed. The conclusion in this study is the implementation of marketing 7P on online Gojek services which include: product, price, place, people, process, and physical evidence has been implemented well.

S. Berns, A. Boyarintsev, S. Hugon et al.

Plastic scintillator detectors are widely used in particle physics thanks to the very good particle identification, tracking capabilities and time resolution. However, new experimental challenges and the need for enhanced performance require the construction of detector geometries that are complicated using the current production techniques. In this article we propose a new production technique based on additive manufacturing that aims to 3D print polystyrene-based scintillator. The production process and the results of the scintillation light output measurement of the 3D-printed scintillator are reported.