Hasil untuk "Manufactures"

T. Beck

The author explores a possible link between financial development and trade in manufactures. His theoretical model focuses on the role of financial intermediaries in facilitating large-scale, high-return projects. Results show that economies with better developed financial sectors have a comparative advantage in manufacturing industries. He provides evidence for this hypothesis, first proposed by Kletzer and Bardhan (1987), using a 30-year panel of data for 65 countries. Controlling for country-specific effects and possible reverse causality, he shows that financial development exerts a large causal impact on the level of both exports and the trade balance of manufactured goods.

James E. Anderson, Y. Yotov, Y. Yotov

Foivos Psarommatis, J. Sousa, J. Mendonça et al.

For manufacturing companies, quality management is a key feature for increasing the competitiveness, productivity, profitability, and sustainability of their systems. Quality improvement (QI) methods aim to achieve high-quality parts without reducing performance. The Industry 4.0 framework brought technological developments that cannot be used by traditional QI methods, such as Six Sigma, Lean, Lean Six, the Theory of Constraints, and Total Quality Management, which are widely used in manufacturing companies. The need for higher manufacturing sustainability and market requirements has led to the search for alternative QI methods with superior performance to traditional QI methods such as Zero-Defect Manufacturing (ZDM). The current paper is a position paper with a goal to present the ZDM approach and providing a clear definition about ZDM to align everyone in one common understanding of ZDM. Many researchers and manufactures are skeptical about ZDM, therefore, numerous argumentative questions have been created and answered, to convince them why they should migrate from traditional QI methods to ZDM. The migration to ZDM has already started, to support this statement numerous facts from the literature have been presented. Finally, several directions were identified, demonstrating that there is still plenty of room for research in several domains.

Vignesh Alagappan

Industrial IoT platforms in global manufacturing environments generate continuous operational data across production assets, utilities, and connected products. While data ingestion and storage capabilities have matured significantly, enterprises continue to face systemic challenges in governing IoT data at scale. These challenges are not rooted in tooling limitations but in the absence of a governance model that aligns with the realities of distributed operational ownership, heterogeneous source systems, and continuous change at the edge. This paper presents a federated governance model that emphasizes contract-driven interoperability, policy-as-code enforcement, and asset-centric accountability across global manufacturing organizations. The model addresses governance enforcement at architectural boundaries, enabling semantic consistency, quality assurance, and regulatory compliance without requiring centralized control of operational technology systems. This work contributes a systems architecture and design framework grounded in analysis of manufacturing IoT requirements and constraints; empirical validation remains future work

Yifei Li, Ruizhe Fu, Huihang Liu et al.

As the demand for mass customization increases, manufacturing systems must become more flexible and adaptable to produce personalized products efficiently. Additive manufacturing (AM) enhances production adaptability by enabling on-demand fabrication of customized components directly from digital models, but its flexibility remains constrained by fixed equipment layouts. Integrating mobile robots addresses this limitation by allowing manufacturing resources to move and adapt to changing production requirements. Mobile AM Robots (MAMbots) combine AM with mobile robotics to produce and transport components within dynamic manufacturing environments. However, the dynamic manufacturing environments introduce challenges for MAMbots. Disturbances such as obstacles and uneven terrain can disrupt navigation stability, which in turn affects printing accuracy and surface quality. This work proposes a universal mobile printing-and-delivery platform that couples navigation and material deposition, addressing the limitations of earlier frameworks that treated these processes separately. A real-time control framework is developed to plan and control the robot's navigation, ensuring safe motion, obstacle avoidance, and path stability while maintaining print quality. The closed-loop integration of sensing, mobility, and manufacturing provides real-time feedback for motion and process control, enabling MAMbots to make autonomous decisions in dynamic environments. The framework is validated through simulations and real-world experiments that test its adaptability to trajectory variations and external disturbances. Coupled navigation and printing together enable MAMbots to plan safe, adaptive trajectories, improving flexibility and adaptability in manufacturing.

GuanLin Feng, HongBin Dai, Fang Liu et al.

This study systematically investigates the microstructure of wire-arc additively manufactured heat-resistant aluminium alloys through crystallographic parameter analysis, focusing on gradient evolution mechanisms and their implications. The distinct Ni incorporation methods yield different microstructure: The ACN specimens characteristic gradients transitioning from coarse-grained (CG) to fine-grained (FG) regions, evolving from high-dislocation-density columnar grains (exhibiting elevated strain and predominant low-angle boundaries) to low-dislocation-density equiaxed grains (dominated by high-angle boundaries and enhanced Schmid factors). Conversely, ACNP specimens achieve homogeneous equiaxed microstructures through rapid dissolution of interlayer Ni powder that forms microscale liquid pools. Complementary to the crystallographic analysis, the ACNP specimens exhibiting uniformly dispersed micro-pores, Weakened the stress concentration effect, further homogenising plastic flow at both micro- and meso-scales. The crystallographic gradient analysis establishes direct correlations between thermal history, dislocation substructures, and boundary evolution, revealing how powder-induced nucleation fundamentally transforms solidification dynamics by replacing directional growth with isotropic grain development – a novel insights for next-generation WAAM-processed heat-resistant alloys requiring balanced thermomechanical performance.

Seungheon Shin, Byeonghyeon Goh, Youngtaek Oh et al.

Topology optimization produces designs with intricate geometries and complex topologies that require advanced manufacturing techniques such as additive manufacturing (AM). However, insufficient consideration of manufacturability during the optimization process often results in design modifications that compromise the optimality of the design. While multi-axis AM enhances manufacturability by enabling flexible material deposition in multiple orientations, challenges remain in addressing overhang structures, potential collisions, and material anisotropy caused by varying build orientations. To overcome these limitations, this study proposes a novel space-time topology optimization framework for multi-axis AM. The framework employs a pseudo-time field as a design variable to represent the fabrication sequence, simultaneously optimizing the density distribution and build orientations. This approach ensures that the overhang angles remain within manufacturable limits while also mitigating collisions. Moreover, by incorporating material anisotropy induced by diverse build orientations into the design process, the framework can take the scan path-dependent structural behaviors into account during the design optimization. Numerical examples demonstrate that the proposed framework effectively derives feasible and optimal designs that account for the manufacturing characteristics of multi-axis AM.

Sujová Erika, Vysloužilová Daniela

In today’s highly competitive business environment, enhancing the efficiency of manufacturing processes is of critical importance. This research focuses on the application of the SMED methodology to increase the efficiency of the changeover process of a CNC machining center. The core of the study is a detailed analysis of the current state of production and changeover procedures on a selected machine. The process was monitored using video analysis of work operations. Based on this analysis, internal and external changeover activities were identified, and by eliminating inefficient steps, a set of improvement measures was proposed. As a result, a standardized changeover procedure was developed to optimize the overall process duration and improving maintenance systems. The final part of the study presents the outcomes of the improvement, expressed through reduced changeover time, along with a proposed economic evaluation of the implemented optimization measures.

Yunqing Li, Binil Starly

Sourcing and identification of new manufacturing partners is crucial for manufacturing system integrators to enhance agility and reduce risk through supply chain diversification in the global economy. The advent of advanced large language models has captured significant interest, due to their ability to generate comprehensive and articulate responses across a wide range of knowledge domains. However, the system often falls short in accuracy and completeness when responding to domain-specific inquiries, particularly in areas like manufacturing service discovery. This research explores the potential of leveraging Knowledge Graphs in conjunction with ChatGPT to streamline the process for prospective clients in identifying small manufacturing enterprises. In this study, we propose a method that integrates bottom-up ontology with advanced machine learning models to develop a Manufacturing Service Knowledge Graph from an array of structured and unstructured data sources, including the digital footprints of small-scale manufacturers throughout North America. The Knowledge Graph and the learned graph embedding vectors are leveraged to tackle intricate queries within the digital supply chain network, responding with enhanced reliability and greater interpretability. The approach highlighted is scalable to millions of entities that can be distributed to form a global Manufacturing Service Knowledge Network Graph that can potentially interconnect multiple types of Knowledge Graphs that span industry sectors, geopolitical boundaries, and business domains. The dataset developed for this study, now publicly accessible, encompasses more than 13,000 manufacturers' weblinks, manufacturing services, certifications, and location entity types.

Tiara Cahyaning Tyas, Ida Giyanti

Quality control is intended to ensure that the products are in accordance with the predefined standards. PT. XYZ is a garment company that manufactures products with global target market. Hence, product quality assurance becomes an important issue for PT. XYZ. This research focuses on the Just Brand-MCJA216142 jacket product at the sewing work station on line-4 of PT. XYZ. Preliminary observations show that the number of reworked-products was experiencing an increasing trend. This study aims to determine whether the company has carried out quality control properly. Specifically, the research objectives are to identify the type and level of product defects, identify the factors causing product defects, and provide proper improvement suggestions to reduce the occurrence of product defects. This study applied DMAIC (Define–Measure–Analyze–Improve–Control) Six Sigma concept. The results showed that the quality of the Just Brand-MCJA216142 jacket product has exceeded the Indonesian industry average and is classified as the USA industry average. However, quality improvement is still needed since the products are targeted for the export market. Based on Pareto diagram at the Analyze stage, it was found that the most dominant defects occurred in the Just Brand-MCJA216142 jacket production process were broken threads and puckering. The frequency of occurrence for these two types of defect reaches 23% of the total 16 types of defects. The defects were caused by human, machine, material, method, and environmental factors. Recommendations for improvement at the Improve stage are based on root cause analysis of each causative factor that is identified using a fishbone diagram. This research results strengthen the previous related researches regarding the effectivity of DMAIC Six Sigma for analyzing quality control of products.

Garrett Mathesen, Charlotte Wehner, Julian Merrick et al.

Additive manufacturing is a versatile technique that shows promise in providing quick and dynamic manufacturing for complex engineering problems. Research has been ongoing into the use of additive manufacturing for potential applications in radiofrequency (RF) component technologies. Here we present a method for developing an effective prototype load produced out of 316L stainless steel on a direct metal laser sintering machine. The model was tested within simulation software to verify the validity of the design. The load structure was manufactured utilizing an online digital manufacturing company, showing the viability of using easily accessible tools to manufacture RF structures. The produced load was able to produce an S$_{11}$ value of -22.8 dB at the C-band frequency of 5.712 GHz while under vacuum. In a high power test, the load was able to terminate a peak power of 8.1 MW. Discussion includes future applications of the present research and how it will help to improve the implementation of future accelerator concepts.

Amra Peles, Vincent C. Paquit, Ryan R. Dehoff

With a goal of accelerating fabrication of additively manufactured components with precise microstructures, we developed a method for structural characterization of key features in additively manufactured materials and parts. The method utilizes deep learning based on an image-to-image translation conditional Generative Adversarial Neural Network architecture and enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing induced defects visible in etched optical images. These structural details are heterogeneous in nature. Our method specifies the microstructure state of an additive built via statistical distribution of structural details, based on an ensemble of collected images. Extensions of the method are proposed to address Artificial Intelligence implementation of developed machine learning model for in real time control of additive manufacturing.

Ilaria Malanchini, Nicola Michailow, Patrick Agostini et al.

The roll out of 5G has been mainly characterized by its distinct support for vertical industries, especially manufacturing. Leveraging synergies among these two worlds, namely production facilities and network systems, is a fundamental aspect to enable flexibility and economic viability in future factories. This work highlights the potential for intelligent networking and advanced machine learning-based solutions in 5G-and-beyond systems in the context of Industry 4.0 and flexible manufacturing. The intersection thereof allows to create versatile machines and dynamic communication networks that can adapt to changes in the manufacturing process, factory layout and communication environment, supporting real-time interaction between humans, machines, and systems. We present a vision and corresponding framework by introducing the network-aware and production-aware principles, outlining results achieved in this context and summarizing them into three key use cases. Finally, we discuss a selection of remaining open challenges in private networks as well as give an outlook on future 6G research directions.

Huizhong Cao, Henrik Söderlund, Mélanie Derspeisse et al.

In response to the transformation towards Industry 5.0, there is a growing call for manufacturing systems that prioritize environmental sustainability, alongside the emerging application of digital tools. Extended Reality (XR) - including Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) - is one of the technologies identified as an enabler for Industry 5.0. XR could potentially also be a driver for more sustainable manufacturing: however, its potential environmental benefits have received limited attention. This paper aims to explore the current manufacturing applications and research within the field of XR technology connected to the environmental sustainability principle. The objectives of this paper are two-fold: (1) Identify the currently explored use cases of XR technology in literature and research, addressing environmental sustainability in manufacturing; (2) Provide guidance and references for industry and companies to use cases, toolboxes, methodologies, and workflows for implementing XR in environmental sustainable manufacturing practices. Based on the categorization of sustainability indicators, developed by the National Institute of Standards and Technology (NIST), the authors analyzed and mapped the current literature, with criteria of pragmatic XR use cases for manufacturing. The exploration resulted in a mapping of the current applications and use cases of XR technology within manufacturing that has the potential to drive environmental sustainability. The results are presented as stated use-cases with reference to the literature, contributing as guidance and inspiration for future researchers or implementations in industry, using XR as a driver for environmental sustainability. Furthermore, the authors open up the discussion for future work and research to increase the attention of XR as a driver for environmental sustainability.

Prithwish Basu Roy, Mudit Bhargava, Chia-Yun Chang et al.

Digital Manufacturing (DM) refers to the on-going adoption of smarter, more agile manufacturing processes and cyber-physical systems. This includes modern techniques and technologies such as Additive Manufacturing (AM)/3D printing, as well as the Industrial Internet of Things (IIoT) and the broader trend toward Industry 4.0. However, this adoption is not without risks: with a growing complexity and connectivity, so too grows the cyber-physical attack surface. Here, malicious actors might seek to steal sensitive information or sabotage products or production lines, causing financial and reputational loss. Of particular concern are where such malicious attacks may enter the complex supply chains of DM systems as Trojans -- malicious modifications that may trigger their payloads at later times or stages of the product lifecycle. In this work, we thus present a comprehensive overview of the threats posed by Trojans in Digital Manufacturing. We cover both hardware and software Trojans which may exist in products or their production and supply lines. From this, we produce a novel taxonomy for classifying and analyzing these threats, and elaborate on how different side channels (e.g. visual, thermal, acoustic, power, and magnetic) may be used to either enhance the impact of a given Trojan or utilized as part of a defensive strategy. Other defenses are also presented -- including hardware, web-, and software-related. To conclude, we discuss seven different case studies and elaborate how they fit into our taxonomy. Overall, this paper presents a detailed survey of the Trojan landscape for Digital Manufacturing: threats, defenses, and the importance of implementing secure practices.

Tongam Sihol Nababan, Elvis Fresly Purba

The manufacturing industry sector was expected to generate new employment opportunities and take on labour. Gradually, however, it emerged as a menace to the sustenance of its workers. According to the findings of this study, 24 manufacturing subsectors with ISIC 2 digits in Indonesia exhibited regressive and abnormal patterns in the period 2012-2020. This suggests that, to a great extent, labour absorption has been limited and, in some cases, even shown a decline. Anomalous occurrences were observed in three subsectors: ISIC 12 (tobacco products), ISIC 26 (computer, electronic and optical products), and ISIC 31 (furniture). In contrast, regressive phenomena were present in the remaining 21 ISIC subsectors. Furthermore, the manufacturing industry displayed a negative correlation between employment and efficiency index, demonstrating this anomalous and regressive phenomenon. This implies that as the efficiency index of the manufacturing industry increases, the index of labour absorption decreases

Chukwuemeke William Isaac, Fabian Duddeck

The emergence of 4D printing from additive manufacturing has opened new frontiers in crashworthiness application. Energy-absorbing structures with fixed geometrical shapes and irreversible deformation stages can be programmed such that after mild or extreme deformation, their initial shapes, properties and functionalities can be recovered with time when actuated by external stimuli. This survey delves into the recently-accelerated progress of shape memory/recovery energy-absorbing metamaterials (EAMM) and energy-absorbing smart/intelligent structures (EASS). First, the introduction gives some fundamental concepts of metamaterials and their application to energy-absorbing structures. Next, some common 3D printing technologies that have led to 4D printed EAMM and EASS are succinctly described. Shape memory materials, their functional properties and recovery process, are then discussed. Finally, various recoverable/reversible energy absorbers with their future challenges and perspectives, are presented. With well-tailored 4D printed EAMM and EASS, reusability with minimal maintenance and higher energy absorption capacity can be retained.

Marinković Veljko, Lazarević Jovana, Marić Dražen

Background: The new circumstances of life due to the proclamation of the COVID 19 virus pandemic have caused numerous changes both in general people's lives and in consumption. Purpose: The aim of this paper is to identify changes in the degree of consumer ethnocentrism when choosing products during the COVID 19 virus pandemic, compared to the period before its occurrence. In addition, differences in consumer preferences for certain domestic products and services before and during the pandemic were analyzed. The paper also deals with differences in ethnocentric tendencies during the pandemic between different socio-demographic consumer segments. Study design/methodology/approach: The primary data were collected from 176 respondents by using the survey method. A paired samples t test is used for hypotheses testing. Independent samples t test and Anova, post hoc Scheffe test, were conducted for analysing differences in ethnocentric tendencies between observed consumer segments during the pandemic. Findings/conclusions: Higher level of consumer ethnocentrism is confirmed in period during the pandemic, especially when it comes to choice of domestic medical products. On the other hand, lower level of consumer ethnocentrism is observed for fashion products and insurance during the pandemic. Older consumers and pensioners exhibit stronger ethnocentric tendencies during the pandemic. Limitations/future research: The main limitation of the paper relates to the use of only a few of the 17 statements within the CET scale for measuring ethnocentric tendencies before and during the pandemic. Also, the research did not cover all categories of domestic products and services. According to the limitations, future studies are recommended to fully apply the CET scale for measuring consumer ethnocentrism. Also, the recommendation is to observe higher number of categories of products and services, and to break down the categories into several subcategories. Finally, future studies can also include some of the determinants of consumer ethnocentrism in the research model.

Antony Orth, Kathleen L. Sampson, Yujie Zhang et al.

Additive manufacturing techniques are revolutionizing product development by enabling fast turnaround from design to fabrication. However, the throughput of the rapid prototyping pipeline remains constrained by print optimization, requiring multiple iterations of fabrication and ex-situ metrology. Despite the need for a suitable technology, robust in-situ shape measurement of an entire print is not currently available with any additive manufacturing modality. Here, we address this shortcoming by demonstrating fully simultaneous 3D metrology and printing. We exploit the dramatic increase in light scattering by a photoresin during gelation for real-time 3D imaging of prints during tomographic volumetric additive manufacturing. Tomographic imaging of the light scattering density in the build volume yields quantitative, artifact-free 3D + time models of cured objects that are accurate to below 1% of the size of the print. By integrating shape measurement into the printing process, our work paves the way for next-generation rapid prototyping with real-time defect detection and correction.

Wasin Meesena, Robert Thompson

Lean manufacturing is a production method focused on reducing production times, eliminating waste, and synchronizing production with fluctuating demand. A standard lean manufacturing methodology is the product wheel, a repeating sequence of production of various items. If this product wheel sequence is short, it is easier to interrupt or alter production to adjust for failures or fluctuations in demand, so the manufacturing process is leaner. However, a sequence that is too short results in frequent changeover from the production of one item to the next, yielding higher costs. This study formulates the product wheel methodology as an optimization problem and proposes two approaches to solving this problem: one via a relaxation to integer linear programming, and another via the probabilistic optimization technique of simulated annealing. We assess the performance of these two approaches through simulations and analyze the relationships between production leanness and costs.