Hasil untuk "Manufactures"

Hąbek Patrycja

The Corporate Sustainability Reporting Directive (CSRD) introduces stringent and standardised environmental, social, and governance (ESG) reporting obligations across the European Union, presenting significant challenges for manufacturing companies due to their resource-intensive operations and complex value chains. This study investigates the digital transformation of sustainability reporting by evaluating how ESG software tools support CSRD compliance. A comparative analysis was conducted on eight widely used ESG software solutions. Thirteen assessment criteria were applied, covering functionality, regulatory compliance, integration capabilities, cost transparency, and scalability. The role of artificial intelligence and advanced analytics in enhancing ESG data quality, automating reporting processes, and generating actionable insights is also explored as a critical enabler of reporting efficiency and accuracy. The findings show that while no tool meets all needs universally, specific solutions offer comprehensive capabilities for large manufacturers, while others are well-suited for SMEs. The study concludes with targeted software selection recommendations and outlines future research directions.

Zaripov Zufar I., Nakipov Ruslan R., Mazanov Sergey V. et al.

This paper presents the results of an experimental study on the phase equilibrium diagram of the binary system “Freon R404A–acetone” at a temperature of T = 356 K. These results are crucial for selecting optimal thermodynamic conditions for the supercritical fluid extraction of acetone from aqueous solutions using R404A freon as an extractant. This process is particularly relevant for addressing wastewater disposal challenges at the Bisphenol-A production facility at PJSC Kazanorgsintez (Russian Federation), which manufactures phenol and acetone. The studied binary mixture exhibits type I–II phase behavior, characterized by the termination of the two-phase “liquid–vapor” equilibrium at critical points corresponding to various temperatures, a continuous critical curve, and a gas-phase region of complete miscibility beyond the binodal. The ability to approach the critical point-where the mixture displays anomalous changes in properties- may significantly enhance the efficiency of the supercritical fluid extraction process compared to systems exhibiting type V phase behavior. At the studied temperature, the critical pressure was determined to be Pkр = 4.14 MPa.

Ciachorowski Damian, Lis Anna Maria, Miranda Elem

This paper investigates how existing literature has approached the concept of sustainability in the context of digital transformation.

Suk Ki Lee, Ronnie F. P. Stone, Max Gao et al.

Manufacturing processes are inherently dynamic and uncertain, with varying parameters and nonlinear behaviors, making robust control essential for maintaining quality and reliability. Traditional control methods often fail under these conditions due to their reactive nature. Model Predictive Control (MPC) has emerged as a more advanced framework, leveraging process models to predict future states and optimize control actions. However, MPC relies on simplified models that often fail to capture complex dynamics, and it struggles with accurate state estimation and handling the propagation of uncertainty in manufacturing environments. Machine learning (ML) has been introduced to enhance MPC by modeling nonlinear dynamics and learning latent representations that support predictive modeling, state estimation, and optimization. Yet existing ML-driven MPC approaches remain deterministic and correlation-focused, motivating the exploration of generative. Generative ML offers new opportunities by learning data distributions, capturing hidden patterns, and inherently managing uncertainty, thereby complementing MPC. This review highlights five representative methods and examines how each has been integrated into MPC components, including predictive modeling, state estimation, and optimization. By synthesizing these cases, we outline the common ways generative ML can systematically enhance MPC and provide a framework for understanding its potential in diverse manufacturing processes. We identify key research gaps, propose future directions, and use a representative case to illustrate how generative ML-driven MPC can extend broadly across manufacturing. Taken together, this review positions generative ML not as an incremental add-on but as a transformative approach to reshape predictive control for next-generation manufacturing systems.

Suk Ki Lee, Hyunwoong Ko

Dynamic manufacturing processes exhibit complex characteristics defined by time-varying parameters, nonlinear behaviors, and uncertainties. These characteristics require sophisticated in-situ monitoring techniques utilizing multimodal sensor data and adaptive control systems that can respond to real-time feedback while maintaining product quality. Recently, generative machine learning (ML) has emerged as a powerful tool for modeling complex distributions and generating synthetic data while handling these manufacturing uncertainties. However, adopting these generative technologies in dynamic manufacturing systems lacks a functional control-oriented perspective to translate their probabilistic understanding into actionable process controls while respecting constraints. This review presents a functional classification of Prediction-Based, Direct Policy, Quality Inference, and Knowledge-Integrated approaches, offering a perspective for understanding existing ML-enhanced control systems and incorporating generative ML. The analysis of generative ML architectures within this framework demonstrates control-relevant properties and potential to extend current ML-enhanced approaches where conventional methods prove insufficient. We show generative ML's potential for manufacturing control through decision-making applications, process guidance, simulation, and digital twins, while identifying critical research gaps: separation between generation and control functions, insufficient physical understanding of manufacturing phenomena, and challenges adapting models from other domains. To address these challenges, we propose future research directions aimed at developing integrated frameworks that combine generative ML and control technologies to address the dynamic complexities of modern manufacturing systems.

Noël Hallemans, Philipp Dechent, David Howey et al.

Lithium-ion batteries (LIBs) have an important role in the shift required to achieve a global net-zero carbon target of 2050. Electrode manufacture is amongst the most expensive steps of the LIB manufacturing process and, despite its apparent maturity, optimised manufacturing conditions are arrived at by largely trial and error. Currently, LIB manufacturing plants are controlled to follow the fixed "recipe" obtained by trial and error, which may nonetheless be suboptimal. Moreover, regulating the process as a whole to conform to the set conditions is not widespread. Inspired by control approaches used in other film and sheet processes, we discuss opportunities for implementing real-time process control of electrode-related products, which has the potential to reduce the electrode manufacturing cost, CO2 emissions, usage of resources by increases in process yield, and throughput. We highlight the challenges and significant opportunities of implementing real-time process control in LIB electrode production lines.

Supun Dissanayaka, Alexander Ferrein, Till Hofmann et al.

The RoboCup Logistics League is a RoboCup competition in a smart factory scenario that has focused on task planning, job scheduling, and multi-agent coordination. The focus on production logistics allowed teams to develop highly competitive strategies, but also meant that some recent developments in the context of smart manufacturing are not reflected in the competition, weakening its relevance over the years. In this paper, we describe the vision for the RoboCup Smart Manufacturing League, a new competition designed as a larger smart manufacturing scenario, reflecting all the major aspects of a modern factory. It will consist of several tracks that are initially independent but gradually combined into one smart manufacturing scenario. The new tracks will cover industrial robotics challenges such as assembly, human-robot collaboration, and humanoid robotics, but also retain a focus on production logistics. We expect the reenvisioned competition to be more attractive to newcomers and well-tried teams, while also shifting the focus to current and future challenges of industrial robotics.

Paul McHard, Florent P. Audonnet, Oliver Summerell et al.

Surface defects are a primary source of yield loss in manufacturing, yet existing anomaly detection methods often fail in real-world deployment due to limited and unrepresentative datasets. To overcome this, we introduce 3D-ADAM, a 3D Anomaly Detection in Additive Manufacturing dataset, that is the first large-scale, industry-relevant dataset for RGB+3D surface defect detection in additive manufacturing. 3D-ADAM comprises 14,120 high-resolution scans of 217 unique parts, captured with four industrial depth sensors, and includes 27,346 annotated defects across 12 categories along with 27,346 annotations of machine element features in 16 classes. 3D-ADAM is captured in a real industrial environment and as such reflects real production conditions, including variations in part placement, sensor positioning, lighting, and partial occlusion. Benchmarking state-of-the-art models demonstrates that 3D-ADAM presents substantial challenges beyond existing datasets. Validation through expert labelling surveys with industry partners further confirms its industrial relevance. By providing this benchmark, 3D-ADAM establishes a foundation for advancing robust 3D anomaly detection capable of meeting manufacturing demands.

Manpreet K. Gakhal, Anant Bakshi, Min Gu et al.

Abstract A reported 20% of dental staff will fail their fit test for a disposable FFP3 respirator. This needs to be factored into future pandemic workforce and PPE supply planning. At present there are no scientifically or universally accepted facial shape criteria to design and produce facial masks that will fit the entire work force. This study presents differences in facial shape, volume and surface area between individuals who passed on several FFP3 masks (pass group) and participants who passed on only one FFP3 mask (fail group). Three dimensional images of 50 individuals, 25 in each group, were taken at rest and at maximum smile using a DI4D SNAP 6200 camera system. The images were processed, and four “average faces” were produced—pass group at rest, fail group at rest, pass group at maximum smile and fail group at maximum smile. Simple Euclidian linear and angular measurements, geodesic surface distances and volume and surface area enclosed within the mask were analysed. The results of the study show that individuals who are more likely to pass a mask fit test have longer faces, wider mouths, greater geodesic surface distances and a greater volume and surface area of soft tissue enclosed within the mask boundary. This would suggest that some manufactures masks may be too large, and they need to reduce the size of their masks or produce a category of sizes, accepting the fact that one size does not fit all.

Ke Ren, Gang Wang, Yuelan Di et al.

Phase transformation is a crucial factor that determines the quality and deformation of components in laser-directed energy deposition (L-DED). Owing to the limitations of in situ observation methods, there is a lack of effective means to study the complete phase transformation and their impacts on deformation during the deposition process. In this study, multilayer heterogeneous deposition was investigated with the high-carbon high-speed steel by modelling the coupled phase transformation-thermomechanical physics. The methodology was proposed to identify the specific phase transformations by iteratively comparing the whole deformation history between digital image correlation (DIC) measurements and simulations. Besides the thermo-mechanical coupling effect and basic volumetric phase transformation, carbon partitioning between precipitated carbides and matrix was manifested, which also resulted in a rise of the Ms point by 208.4 °C and a reduction of the transformation rate by 73.2%. Additionally, an increase of 13.8% in energy absorption could be attributed to the surface oxidation of the deposited layer. The model predictions exhibited good consistency with the DIC in-situ measurement data, indicating that the deformation history contained sufficient information on the phase transformations. Based on the proposed transformation-thermomechanical coupling model, it helps to understand the complete phase transformation during deposition.

Mazur Magdalena, Korenko Maroš, Žitňák Miroslav et al.

The article deals with the use of the 5S methodology in an organization. It focuses on the implementation of 5S in an organization involved in the production and processing of metal components for the automotive industry. 5S is a Japanese methodology that was first applied in the automotive industry to improve productivity. The article is dedicated to the basic principles of lean manufacturing.The practical part analyses the state of the workplaces before the introduction of 5S and the actual implementation of this methodology. The implementation of 5S brings a number of benefits to the organization, including:

Dennis Gross, Helge Spieker, Arnaud Gotlieb et al.

This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case. The methodology entails the initial training of ML models, followed by a fine-tuning phase where irrelevant features identified through explainability methods are eliminated. This procedural refinement results in performance enhancements, paving the way for potential reductions in manufacturing costs and a better understanding of the trained ML models. This study highlights the usefulness of explainability techniques in both explaining and optimizing predictive models in the manufacturing realm.

Md Habibor Rahman, Mohammed Shafae

The increasing cybersecurity threats to critical manufacturing infrastructure necessitate proactive strategies for vulnerability identification, classification, and assessment. Traditional approaches, which define vulnerabilities as weaknesses in computational logic or information systems, often overlook the physical and cyber-physical dimensions critical to manufacturing systems, comprising intertwined cyber, physical, and human elements. As a result, existing solutions fall short in addressing the complex, domain-specific vulnerabilities of manufacturing environments. To bridge this gap, this work redefines vulnerabilities in the manufacturing context by introducing a novel characterization based on the duality between vulnerabilities and defenses. Vulnerabilities are conceptualized as exploitable gaps within various defense layers, enabling a structured investigation of manufacturing systems. This paper presents a manufacturing-specific cyber-physical defense-in-depth model, highlighting how security-aware personnel, post-production inspection systems, and process monitoring approaches can complement traditional cyber defenses to enhance system resilience. Leveraging this model, we systematically identify and classify vulnerabilities across the manufacturing cyberspace, human element, post-production inspection systems, production process monitoring, and organizational policies and procedures. This comprehensive classification introduces the first taxonomy of cyber-physical vulnerabilities in smart manufacturing systems, providing practitioners with a structured framework for addressing vulnerabilities at both the system and process levels. Finally, the effectiveness of the proposed model and framework is demonstrated through an illustrative smart manufacturing system and its corresponding threat model.

Ting Wu, Yaojia Ren, Luxin Liang et al.

Glass fibre (GF) and glass bead (GB)–reinforced polyamide1212 (PA1212) was additively manufactured by selective laser sintering. The effects of laser power and GF content on the tensile and tribological properties of the printed specimens with a base GB weight fraction of 40 wt.% were investigated. The strengthening mechanism of GFs/GBs was illustrated by analyzing the interfacial adhesion between the fillers and the PA1212 matrix. The specimens with 40 wt.% GBs and 10 wt.% GFs fabricated at a laser power of 30 W exhibited a strength of 52 MPa, a friction coefficient of 0.23, and a wear rate of 0.0011 mm3/N·m. The selected optimal laser power and GF addition contributed to the strong interfacial adhesion, which realised flat surface morphology and an adequate encapsulation of fillers in the specimen. The reinforcement of GBs/GFs in PA1212 can serve as a reference for a deeper understanding of the strengthening mechanisms for other additively manufactured engineering plastics.

Sungho Suh, Vitor Fortes Rey, Sizhen Bian et al.

Manufacturing industries strive to improve production efficiency and product quality by deploying advanced sensing and control systems. Wearable sensors are emerging as a promising solution for achieving this goal, as they can provide continuous and unobtrusive monitoring of workers' activities in the manufacturing line. This paper presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line. To handle these multimodal sensor data, we propose and compare early, and late sensor data fusion approaches for multi-channel time-series convolutional neural networks and deep convolutional LSTM. We evaluate the proposed hardware and neural network model by collecting and annotating sensor data using the proposed sensing prototype and Apple Watches in the testbed of the manufacturing line. Experimental results demonstrate that our proposed methods achieve superior performance compared to the baseline methods, indicating the potential of the proposed approach for real-world applications in manufacturing industries. Furthermore, the proposed sensing prototype with a body capacitive sensor and feature fusion method improves by 6.35%, yielding a 9.38% higher macro F1 score than the proposed sensing prototype without a body capacitive sensor and Apple Watch data, respectively.

Haotian Zhang, Semujju Stuart Dereck, Zhicheng Wang et al.

Although the applications of artificial intelligence especially deep learning had greatly improved various aspects of intelligent manufacturing, they still face challenges for wide employment due to the poor generalization ability, difficulties to establish high-quality training datasets, and unsatisfactory performance of deep learning methods. The emergence of large scale foundational models(LSFMs) had triggered a wave in the field of artificial intelligence, shifting deep learning models from single-task, single-modal, limited data patterns to a paradigm encompassing diverse tasks, multimodal, and pre-training on massive datasets. Although LSFMs had demonstrated powerful generalization capabilities, automatic high-quality training dataset generation and superior performance across various domains, applications of LSFMs on intelligent manufacturing were still in their nascent stage. A systematic overview of this topic was lacking, especially regarding which challenges of deep learning can be addressed by LSFMs and how these challenges can be systematically tackled. To fill this gap, this paper systematically expounded current statue of LSFMs and their advantages in the context of intelligent manufacturing. and compared comprehensively with the challenges faced by current deep learning models in various intelligent manufacturing applications. We also outlined the roadmaps for utilizing LSFMs to address these challenges. Finally, case studies of applications of LSFMs in real-world intelligent manufacturing scenarios were presented to illustrate how LSFMs could help industries, improve their efficiency.

Mojtaba A. Farahani, M. R. McCormick, Robert Gianinny et al.

Since the inception of Industry 4.0 in 2012, emerging technologies have enabled the acquisition of vast amounts of data from diverse sources such as machine tools, robust and affordable sensor systems with advanced information models, and other sources within Smart Manufacturing Systems (SMS). As a result, the amount of data that is available in manufacturing settings has exploded, allowing data-hungry tools such as Artificial Intelligence (AI) and Machine Learning (ML) to be leveraged. Time-series analytics has been successfully applied in a variety of industries, and that success is now being migrated to pattern recognition applications in manufacturing to support higher quality products, zero defect manufacturing, and improved customer satisfaction. However, the diverse landscape of manufacturing presents a challenge for successfully solving problems in industry using time-series pattern recognition. The resulting research gap of understanding and applying the subject matter of time-series pattern recognition in manufacturing is a major limiting factor for adoption in industry. The purpose of this paper is to provide a structured perspective of the current state of time-series pattern recognition in manufacturing with a problem-solving focus. By using an ontology to classify and define concepts, how they are structured, their properties, the relationships between them, and considerations when applying them, this paper aims to provide practical and actionable guidelines for application and recommendations for advancing time-series analytics.

Min Yi, Wei Tang, Yiqi Zhu et al.

Additive manufacturing (AM) technology is undergoing rapid development and emerging as an advanced technique that can fabricate complex near-net shaped and light-weight metallic parts with acceptable strength and fatigue performance. A number of studies have indicated that the strength or other mechanical properties of AM metals are comparable or even superior to that of conventionally manufactured metals, but the fatigue performance is still a thorny problem that may hinder the replacement of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure dominates. This article reviews the state-of-art published data on the fatigue properties of AM metals, principally including $S$--$N$ data and fatigue crack growth data. The AM techniques utilized to generate samples in this review include powder bed fusion (e.g., EBM, SLM, DMLS) and directed energy deposition (e.g., LENS, WAAM). Further, the fatigue properties of AM metallic materials that involve titanium alloys, aluminum alloys, stainless steel, nickel-based alloys, magnesium alloys, and high entropy alloys, are systematically overviewed. In addition, summary figures or tables for the published data on fatigue properties are presented for the above metals, the AM techniques, and the influencing factors (manufacturing parameters, e.g., built orientation, processing parameter, and post-processing). The effects of build direction, particle, geometry, manufacturing parameters, post-processing, and heat-treatment on fatigue properties, when available, are provided and discussed. The fatigue performance and main factors affecting the fatigue behavior of AM metals are finally compared and critically analyzed, thus potentially providing valuable guidance for improving the fatigue performance of AM metals.

Wen Chiat Lee, Boo Ho Voon

Service sector is an important sector in Sarawak and it contributes about 36 percent to Sarawak’s Gross Domestic Product (GDP) in 2020.  The sector also contributes 56 percent of total employment of the state.  However, there are some challenges in service sector. Among the key challenges are low productivity growth, shortage of skilled workforce and weak internet connectivity in rural area that restricted the development of service sector.  This paper presents the performance of service sector in Sarawak and the challenges of the service sector.  The proposed recommendations to face the challenges include, providing training to semi-skilled and low-skilled workers to improve the productivity, providing quality and cheap food to attract tourists, and developing internet infrastructure in rural Sarawak.

Roman Froschauer, Aljosha Köcher, Kristof Meixner et al.

Industry 4.0 envisions Cyber-Physical Production Systems (CPPSs) to foster adaptive production of mass-customizable products. Manufacturing approaches based on capabilities and skills aim to support this adaptability by encapsulating machine functions and decoupling them from specific production processes. At the 2022 IEEE conference on Emerging Technologies and Factory Automation (ETFA), a special session on capability- and skill-based manufacturing is hosted for the fourth time. However, an overview on capability- and skill based systems in factory automation and manufacturing systems is missing. This paper aims to provide such an overview and give insights to this particular field of research. We conducted a concise literature survey of papers covering the topics of capabilities and skills in manufacturing from the last ten years of the ETFA conference. We found 247 papers with a notion on capabilities and skills and identified and analyzed 34 relevant papers which met this survey's inclusion criteria. In this paper, we provide (i) an overview of the research field, (ii) an analysis of the characteristics of capabilities and skills, and (iii) a discussion on gaps and opportunities.