Hasil untuk "Manufacturing industries"

Malte Brettel, Niklas Friederichsen, Michael Keller et al.

D. Gu, W. Meiners, K. Wissenbach et al.

D. Camacho, P. Clayton, William J. O'brien et al.

S. H. Abdul-Rashid, N. Sakundarini, R. Ghazilla et al.

Zhixiong Li, Ziyang Zhang, Junchuan Shi et al.

Abstract Additive manufacturing (AM), also known as 3D printing, has been increasingly adopted in the aerospace, automotive, energy, and healthcare industries over the past few years. While AM has many advantages over subtractive manufacturing processes, one of the primary limitations of AM is surface integrity. To improve the surface integrity of additively manufactured parts, a data-driven predictive modeling approach to predicting surface roughness in AM is introduced. Multiple sensors of different types, including thermocouples, infrared temperature sensors, and accelerometers, are used to collect temperature and vibration data. An ensemble learning algorithm is introduced to train the predictive model of surface roughness. Features in the time and frequency domains are extracted from sensor-based condition monitoring data. A subset of these features is selected to improve computational efficiency and prediction accuracy. The predictive model is validated using the condition monitoring data collected from a set of AM tests conducted on a fused filament fabrication (FFF) machine. Experimental results have shown that the proposed predictive modeling approach is capable of predicting the surface roughness of 3D printed components with high accuracy.

Foivos Psarommatis, Gökan May, Paul Dreyfus et al.

This paper provides a literature review on zero defect manufacturing based on the content analysis performed for 280 research articles published from 1987 to 2018 in a variety of academic journals and conference proceedings. The review summarises the state-of-the-art, highlights shortcomings and further directions in research. Accordingly, we investigated how zero defect manufacturing was implemented and evaluated the main research patterns in the sample by analysing key factors. Based on the extensive review of the zero defect manufacturing literature, we identified and highlighted four distinctive strategies based on overarching themes for zero defect manufacturing, i.e. detection, repair, prediction, and prevention. Evaluation of current research and descriptive analysis highlighted six major shortcomings of current research in zero defect manufacturing: (i) focus on a single strategy instead of a holistic approach for global optima; (ii) certain industries are under-researched; (iii) full potential of industry-academia collaboration is not achieved; (iv) not enough focus on the beginning of manufacturing lifecycle; (v) cost–benefit comparative analysis is not evident; (vi) standard and clear definition of terms are missing. Finally, we presented four further directions in which an advance of the topic would stimulate scholarly and practical needs: (i) shift from local to global solutions; (ii) investigate pros and cons; (iii) role of people and human activities in manufacturing; (iv) new business models for zero defect manufacturing.

Georg Reischauer

We are witnessing an increasing adoption of digital technologies in manufacturing industries around the globe. This trend is often debated under the label Industry 4.0. A key claim put forward in these debates is that Industry 4.0 represents a revolution that will reshape manufacturing industries akin to previous industrial revolutions. Despite the popularity of this claim, it provides little help to clarify the identity of Industry 4.0. Such a clarification is however much needed given the worldwide proliferation of digital technologies in manufacturing industries. I address this gap by arguing to view Industry 4.0 as policy-driven innovation discourse in manufacturing industries that aims to institutionalize innovation systems that encompass business, academia, and politics. This clarification of the identity of Industry 4.0 adds to a better understanding of the relationship between manufacturing and politics as well as technological change in manufacturing.

A. Fernandes

Oana Emilia Constantin, Genica Florina Oncica, Florina Stoica et al.

Carrots, scientifically referred to as <i>Daucus carota</i> L., are widely recognized as one of the most consumed vegetables, frequently utilized in culinary applications and juice manufacturing, both commercially and domestically. This results in significant amounts of waste, primarily from the pomace. Carrot pomace represents a promising low-cost raw material for the production of value-added ingredients for the food and feed industries. The extraction of total carotenoids (TC) and the evaluation of antioxidant activity (AA) were optimized in this study by employing environmentally friendly methods, including ultrasonication. The Central Composite Design (CCD) was utilized in order to establish a response surface approach for the purpose of evaluating the impacts of extraction duration, temperature, and the ratio of material to solvent on the recovery of TC and AA. Under optimal conditions, the TC content was 38.20 mg/g of dry weight, with an antioxidant capacity of 1522.02 μmol TE/g DW, as determined by the ABTS assay. According to the findings, the optimal parameters for extraction were a temperature of 58.9 °C, a solvent mixture ratio of 20.35 mL/g, and a duration of 51 min. The proposed ultrasound-assisted process provides a sustainable and scalable approach for carrot pomace valorization, contributing to the development of circular and resource-efficient agro-industrial processing.

Yingjie Chen, O. Yang, Chaitanya Sampat et al.

The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.

S. Kim, Jun Ho Kong, Sang Won Lee et al.

The recent advances in artificial intelligence have already begun to penetrate our daily lives. Even though the development is still in its infancy, it has been shown that it can outperform human beings even in terms of intelligence (e.g., AlphaGo by DeepMind), implying a massive potential for its broader application in various industrial sectors. In particular, the growing public interest in industry 4.0, which focuses on revolutionizing the traditional manufacturing scene, has stimulated a deeper investigation of its possible applications in the related industries. Since it has several limitations that hinder its direct usage, research on the convergence of artificial intelligence with other engineering fields, including precision engineering and manufacturing, is ongoing. This overview looks to summarize some of the important achievements made using artificial intelligence in some of the most influential and lucrative manufacturing industries in hopes of transforming the manufacturing sites.

Natthakritta Rungtalay, Somyot Kaitwanidvilai

ABSTRACT This study aims to predict hard disk drives (HDDs) that pass initial testing but fail during reliability testing, using historical data from 8968 records with 218 features, such as head position and flying height of the read/write head. Since reliability testing is time‐intensive, early failure prediction can significantly accelerate problem detection and resolution. The research focuses on detecting fly height modulation, a key symptom of HDD failure, and introduces an adaptive machine learning (ML) framework integrating AutoML for optimised model selection and hyperparameter tuning with MLOps for deployment, monitoring and continuous updates. Building on a previously proposed dual‐stage classification framework that combines novelty detection and supervised learning, the proposed framework addresses the inefficiencies of manual hyperparameter tuning inherent in the earlier methods. The proposed framework achieves 92% accuracy in novelty detection and 100% in supervised learning, outperforming prior approaches. This integration of AutoML and MLOps offers a scalable, robust solution for early failure prediction, enabling real‐time adaptability with minimal human intervention. Future work will focus on enhancing computational efficiency and responsiveness to data shifts and drifts, advancing data‐driven decision‐making in reliability testing.

Kai Xu, Hang Zhao, Ruizhen Hu et al.

Driven by breakthroughs in next-generation artificial intelligence, embodied intelligence is rapidly advancing into industrial manufacturing. In flexible manufacturing, industrial embodied intelligence faces three core challenges: accurate process modeling and monitoring under limited perception, dynamic balancing between flexible adaptation and high-precision control, and the integration of general-purpose skills with specialized industrial operations. Accordingly, this survey reviews existing work from three viewpoints: Industrial Eye, Industrial Hand, and Industrial Brain. At the perception level (Industrial Eye), multimodal data fusion and real-time modeling in complex dynamic settings are examined. At the control level (Industrial Hand), flexible, adaptive, and precise manipulation for complex manufacturing processes is analyzed. At the decision level (Industrial Brain), intelligent optimization methods for process planning and line scheduling are summarized. By considering multi-level collaboration and interdisciplinary integration, this work reveals the key technological pathways of embodied intelligence for closed-loop optimization of perception-decision-execution in manufacturing systems. A three-stage evolution model for the development of embodied intelligence in flexible manufacturing scenarios, comprising cognition enhancement, skill transition, and system evolution, is proposed, and future development trends are examined, to offer both a theoretical framework and practical guidance for the interdisciplinary advancement of industrial embodied intelligence in the context of flexible manufacturing.

Michael Sony, J. Antony, Olivia Mc Dermott et al.

Abstract Industry 4.0 marks a new paradigm and has expanded its domain from theoretical concepts to real-world applications. Industry 4.0 is, however, still in the state of infancy and conceptual state wherein it is not clear as to how to incorporate many dynamic technological concepts in different sectors. Previous studies have conceptually delineated the benefits, challenges, and CSFs of Industry 4.0, however, there is yet to be an empirical study that critically examines the differences in benefits, challenges, and critical success factors (CSFs) of Industry 4.0 in both manufacturing and service industries and rank them. This study through an online survey captures the view of senior management professionals who have experience in Industry 4.0 implementation in major companies in Asia, Europe, and North America. 96 senior management professionals participated in this study through an online survey. The qualitative data on benefits and challenges were analysed using thematic analyses. The quantitative data on critical success factors were ranked using the normalisation of the mean to find the most important factors. Further agreement analysis was conducted in the manufacturing and service sectors for the CSFs. This study identifies the top five benefits and challenges in the manufacturing and service industries. The CSFs for Industry 4.0 was put forward and ranked in both the manufacturing and service industries.

L. Camarinha-Matos, A. Rocha, Paula Graça

In recent years, the manufacturing sector is going through a major transformation, as reflected in the concept of Industry 4.0 and digital transformation. The urge for such transformation is intensified when we consider the growing societal demands for sustainability. The notion of sustainable manufacturing has emerged as a result of this trend. Additionally, industries and the whole society face the challenges of an increasing number of disruptive events, either natural or human-caused, that can severely affect the normal operation of systems. Furthermore, the growing interconnectivity between organizations, people, and physical systems, supported by recent developments in information and communication technologies, highlights the important role that collaborative networks can play in the digital transformation processes. As such, this article analyses potential synergies between the areas of sustainable and resilient manufacturing and collaborative networks. The work also discusses how the responsibility for the various facets of sustainability can be distributed among the multiple entities involved in manufacturing. The study is based on a literature survey, complemented with the experience gained from various research projects and related initiatives in the area, and is organized according to various dimensions of Industry 4.0. A brief review of proposed approaches and indicators for measuring sustainability from the networked manufacturing perspective is also included. Finally, a set of key research challenges are identified to complement strategic research agendas in manufacturing.

Feihong Gai, Lianrui Yang, Shifa Chen

Evolving from ethnographic research that determines what meanings are shared within a culture, studies regarding Speech Codes Theory have been conducted with implications for intercultural communication. Arguing for transitivity as speech codes in diverse cultures, this paper aims to describe and explain the transitivity attributes of English-medium Corporate Social Responsibility (CSR) reports by Chinese and British/American corporations, and the transitivity variances between industries by Chinese corporations. Results reveal that frequencies of transitivity features in the Chinese CSR reports are significantly lower than those in the British/American reports, and that the relational process dominates in the corpus under investigation. Also, cross-industrial comparisons among Chinese corporations show that the mechanical manufacturing enterprises and the iron & steel enterprises share similar transitivity codes, but they differ from the energy enterprises distinctively. The paper concludes that linguistic/cultural contexts, translation strategies, moves in the CSR reports, and industry attributes together contribute to the transitivity codes and communicative conducts in different cultural and industrial backgrounds. This paper might help writers/translators of CSR reports in China better understand lexical and functional conformities and peculiarities reflected in Chinese and British/American CSR reports, enhance their corporate narration in the international communities, and thus reinforce communication with the general public by appropriately expressing and accurately translating transitivity processes.

Tianyi Xiang, Borui Li, Xin Pan et al.

This paper has proposed an easily replicable and novel approach for developing a Digital Twin (DT) system for industrial robots in intelligent manufacturing applications. Our framework enables effective communication via Robot Web Service (RWS), while a real-time simulation is implemented in Unity 3D and Web-based Platform without any other 3rd party tools. The framework can do real-time visualization and control of the entire work process, as well as implement real-time path planning based on algorithms executed in MATLAB. Results verify the high communication efficiency with a refresh rate of only $17 ms$. Furthermore, our developed web-based platform and Graphical User Interface (GUI) enable easy accessibility and user-friendliness in real-time control.

Siddarth Reddy Karuka, Abhinav Sunderrajan, Zheng Zheng et al.

Errors or failures in a high-volume manufacturing environment can have significant impact that can result in both the loss of time and money. Identifying such failures early has been a top priority for manufacturing industries and various rule-based algorithms have been developed over the years. However, catching these failures is time consuming and such algorithms cannot adapt well to changes in designs, and sometimes variations in everyday behavior. More importantly, the number of units to monitor in a high-volume manufacturing environment is too big for manual monitoring or for a simple program. Here we develop a novel program that combines both rule-based decisions and machine learning models that can not only learn and adapt to such day-to-day variations or long-term design changes, but also can be applied at scale to the high number of manufacturing units in use today. Using the current state-of-the-art technologies, we then deploy this program at-scale to handle the needs of ever-increasing demand from the manufacturing environment.

Yiwei Li, Huaqin Zhao, Hanqi Jiang et al.

The rapid advances in Large Language Models (LLMs) have the potential to transform manufacturing industry, offering new opportunities to optimize processes, improve efficiency, and drive innovation. This paper provides a comprehensive exploration of the integration of LLMs into the manufacturing domain, focusing on their potential to automate and enhance various aspects of manufacturing, from product design and development to quality control, supply chain optimization, and talent management. Through extensive evaluations across multiple manufacturing tasks, we demonstrate the remarkable capabilities of state-of-the-art LLMs, such as GPT-4V, in understanding and executing complex instructions, extracting valuable insights from vast amounts of data, and facilitating knowledge sharing. We also delve into the transformative potential of LLMs in reshaping manufacturing education, automating coding processes, enhancing robot control systems, and enabling the creation of immersive, data-rich virtual environments through the industrial metaverse. By highlighting the practical applications and emerging use cases of LLMs in manufacturing, this paper aims to provide a valuable resource for professionals, researchers, and decision-makers seeking to harness the power of these technologies to address real-world challenges, drive operational excellence, and unlock sustainable growth in an increasingly competitive landscape.

Qixiang Luo, John D. Shimanek, Timothy W. Simpson et al.

The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final mechanical performance. To this end, the present study investigates a machine learning approach for predicting mechanical properties for Ti-6Al-4V fabricated through laser powder bed fusion (PBF-LB) AM using in situ photodiode processing signals. Samples were fabricated under different processing parameters, varying laser powers and scan speeds for the purpose of probing a wide range of microstructure and property variations. Photodiode data were collected during fabrication, later to be arranged in image format and extracted to information-dense vectors by the transferal of deep convolutional neural network (DCNN) structures and weights pre-trained on a large computer vision benchmark image database. The extracted features were then used to train and test a newly designed regression model for mechanical properties. Average cross-validation accuracies were found to be 98.7% (r2 value of 0.89) for the prediction of ultimate tensile strength, which ranged from 900 to 1150 MPa in the samples studied, and 93.1% (r2 value of 0.96) for the prediction of elongation to fracture, which ranged from 0 to 17%. Thus, with high accuracy and hardware accelerated inference speeds, we demonstrate that a transfer learning framework can be used to predict strength and ductility of metal AM components based on processing signals in PBF-LB, illustrating a potential route toward real-time closed-loop control and process optimization of PBF-LB in industrial applications.