Hasil untuk "Manufactures"

Thomas Hatzichronoglou

R. Solow

Sarv Devaraj, L. Krajewski, Jerry C. Wei

Abhishek Kumar

This paper presents a novel decoder-based approach for generating manufacturable 3D structures optimized for additive manufacturing. We introduce a deep learning framework that decodes latent representations into geometrically valid, printable objects while respecting manufacturing constraints such as overhang angles, wall thickness, and structural integrity. The methodology demonstrates that neural decoders can learn complex mapping functions from abstract representations to valid 3D geometries, producing parts with significantly improved manufacturability compared to naive generation approaches. We validate the approach on diverse object categories and demonstrate practical 3D printing of decoder-generated structures.

Matteo Ballegeer, Dries F. Benoit

Predicting the manufacturability of CAD designs early, in terms of both feasibility and required effort, is a key goal of Design for Manufacturing (DFM). Despite advances in deep learning for CAD and its widespread use in manufacturing process selection, learning-based approaches for predicting manufacturability within a specific process remain limited. Two key challenges limit progress: inconsistency across prior work in how manufacturability is defined and consequently in the associated learning targets, and a scarcity of suitable datasets. Existing labels vary significantly: they may reflect intrinsic design constraints or depend on specific manufacturing capabilities (such as available tools), and they range from discrete feasibility checks to continuous complexity measures. Furthermore, industrial datasets typically contain only manufacturable parts, offering little signal for infeasible cases, while existing synthetic datasets focus on simple geometries and subtractive processes. To address these gaps, we propose a taxonomy of manufacturability metrics along the axes of configuration dependence and measurement type, allowing clearer scoping of generalizability and learning objectives. Next, we introduce BenDFM, the first synthetic dataset for manufacturability assessment in sheet metal bending. BenDFM contains 20,000 parts, both manufacturable and unmanufacturable, generated with process-aware bending simulations, providing both folded and unfolded geometries and multiple manufacturability labels across the taxonomy, enabling systematic study of previously unexplored learning-based DFM challenges. We benchmark two state-of-the-art 3D learning architectures on BenDFM, showing that graph-based representations that capture relationships between part surfaces achieve better accuracy, and that predicting metrics that depend on specific manufacturing setups remains more challenging.

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

Runnan Yu, Miaoning Ou, Qirui Hou et al.

Carbon dots (CDs) have shown great potential for application in optoelectronics, owing to their merits of tunable fluorescence, biocompatibility, low toxicity, and solution processability. However, the intrinsic nature of CDs makes them prone to fluorescence quenching in the aggregated state. In addition, the emission peak width at half maximum of a single CD is usually greater than 60 nm, and the emission spectra may exhibit a multi-peak superposition state, resulting in poor monochromaticity. Further, the unsatisfactory quantum yield of CDs restricts their further application. Considering this, doping strategies have successfully improved the electrical, optical, and chemical properties of CDs. The intrinsic structure and electron distribution of CDs can be effectively adjusted by metal or nonmetal doping. Doping atoms generate n- or p-type charge carriers, changing the bandgap energy, and thereby improving the photophysical properties of the CDs. In this comprehensive review, we explore the intricate effects of various doping strategies on CDs and systematically categorize them. Notably, we elaborate on the diverse types of doped CDs and emphasize their photophysical properties, aiming to elucidate the fundamental mechanisms underlying the influence of doping on CD performance. Specifically, this review describes the extensive applications of doped carbon dots (X-CDs) in optoelectronic devices, information encryption, anti-counterfeiting measures, imaging techniques, and detection fields, to spur further X-CD exploration and application.

Jingwen Wu, Anfu Guo, Hailong Wu et al.

4D printing is an emerging advancement in additive manufacturing that enables responsive materials to undergo shape transformations, achieving dynamic behaviours. However, 4D printing of ceramics remains challenging due to their brittleness and limited deformability. In this study, we synthesise a novel multifunctional polymer-derived ceramic precursor (PDCP) with reconfigurability and shape memory properties for 4D printing. Programmable 4D printing via stereolithography was achieved by leveraging the glass transition and melting temperatures of the material. Unlike conventional ceramic precursors, which retain a static shape, the proposed PDCP exhibits autonomous deformation through a two-step curing process: first, photopolymerization establishes an initial form, followed by heating/melting to programme the final shape. Most PDCPs with shape memory effects can only set a single permanent shape, whereas ours allows for reconfiguration and resetting of a new permanent shape. Additionally, simulations are conducted to analyse the shape change behaviour of the PDCP. The incorporation of polylactic acid into the precursor significantly enhances its mechanical properties, with tensile strength increasing by 200% to 300% compared with previously studied flexible PDCPs. Furthermore, the shape memory function of polylactic acid is inherited by the PDCP, presenting promising applications in aerospace and military fields where adaptable ceramic structures are highly valuable.

Tao Xi Wang, Hong Jie Ma, Xin Yin Ang et al.

Since the introduction of 4D printing in 2012, shape memory hybrids (SMHs) have emerged as a versatile solution for tailoring thermomechanical properties. This study developed UV cross-linkable hybrid resins for additive manufacturing (AM) with high performance and body-temperature programmable shape memory effect (SME). These resins combine polycaprolactone (PCL) as the transition component with a commercial elastic UV cross-linkable resin. The thermomechanical properties and shape memory performance were evaluated using differential scanning calorimeter, Shore hardness, and tensile tests. The SMHs exhibited tuneable properties and excellent SMEs, with shape fixing and recovery ratios exceeding 97.5% for 40% PCL (PCL-40). Increased PCL content improved Shore hardness at room temperature while enabling softness near body temperature for easy programming. Feasibility for AM was demonstrated using extrusion-based and volumetric additive manufacturing techniques. Proof-of-concept experiments showed successful 2D-to-3D shape transitions programmed at body temperature with full recovery upon reheating. These findings highlight the potential of UV cross-linkable SMHs for applications in wearable devices, medical tools, and other technologies requiring body-temperature shape adaptation.

Mattis van 't Schip

Many modern consumer devices rely on network connections and cloud services to perform their core functions. This dependency is especially present in Internet of Things (IoT) devices, which combine hardware and software with network connections (e.g., a 'smart' doorbell with a camera). This paper argues that current European product legislation, which aims to protect consumers of, inter alia, IoT devices, has a blind spot for an increasing problem in the competitive IoT market: manufacturer cessation. Without the manufacturer's cloud servers, many IoT devices cannot perform core functions such as data analysis. If an IoT manufacturer ceases their operations, consumers of the manufacturer's devices are thus often left with a dysfunctional device and, as the paper shows, hardly any legal remedies. This paper therefore investigates three properties that could support legislators in finding a solution for IoT manufacturer cessation: i) pre-emptive measures, aimed at ii) manufacturer-independent iii) collective control. The paper finally shows how these three properties already align with current legislative processes surrounding 'interoperability' and open-source software development.

Hongrui Chen, Aditya Joglekar, Zack Rubinstein et al.

Advances in CAD and CAM have enabled engineers and design teams to digitally design parts with unprecedented ease. Software solutions now come with a range of modules for optimizing designs for performance requirements, generating instructions for manufacturing, and digitally tracking the entire process from design to procurement in the form of product life-cycle management tools. However, existing solutions force design teams and corporations to take a primarily serial approach where manufacturing and procurement decisions are largely contingent on design, rather than being an integral part of the design process. In this work, we propose a new approach to part making where design, manufacturing, and supply chain requirements and resources can be jointly considered and optimized. We present the Generative Manufacturing compiler that accepts as input the following: 1) An engineering part requirements specification that includes quantities such as loads, domain envelope, mass, and compliance, 2) A business part requirements specification that includes production volume, cost, and lead time, 3) Contextual knowledge about the current manufacturing state such as availability of relevant manufacturing equipment, materials, and workforce, both locally and through the supply chain. Based on these factors, the compiler generates and evaluates manufacturing process alternatives and the optimal derivative designs that are implied by each process, and enables a user guided iterative exploration of the design space. As part of our initial implementation of this compiler, we demonstrate the effectiveness of our approach on examples of a cantilever beam problem and a rocket engine mount problem and showcase its utility in creating and selecting optimal solutions according to the requirements and resources.

Stefan Schröder, João Felipe, Sven Danz et al.

In this paper, a methodical approach to evaluate the potential of quantum computing for manufacturing simulation, using the example of multi-axis milling of thin-walled aerospace components, is discussed. A developed approach for identifying bottlenecks in manufacturing simulations, for which the application of quantum computing potentially provides a speed-up or increase in accuracy, is presented. Moreover, indicators of quantum computing suitability and feasibility are defined with the main objective of identifying whether a manufacturing simulation bottleneck is suitable for quantum computing applications. First results of testing a hybrid routine as an application approach for the milling dynamics simulation on quantum machines are presented.

Hamidreza Samadi, Md Manjurul Ahsan, Shivakumar Raman

Metrology, the science of measurement, plays a key role in Advanced Manufacturing (AM) to ensure quality control, process optimization, and predictive maintenance. However, it has often been overlooked in AM domains due to the current focus on automation and the complexity of integrated precise measurement systems. Over the years, Digital Twin (DT) technology in AM has gained much attention due to its potential to address these challenges through physical data integration and real-time monitoring, though its use in metrology remains limited. Taking this into account, this study proposes a novel framework, the Metrology and Manufacturing-Integrated Digital Twin (MM-DT), which focuses on data from two metrology tools, collected from Coordinate Measuring Machines (CMM) and FARO Arm devices. Throughout this process, we measured 20 manufacturing parts, with each part assessed twice under different temperature conditions. Using Ensemble Machine Learning methods, our proposed approach predicts measurement deviations accurately, achieving an R2 score of 0.91 and reducing the Root Mean Square Error (RMSE) to 1.59 micrometers. Our MM-DT framework demonstrates its efficiency by improving metrology processes and offers valuable insights for researchers and practitioners who aim to increase manufacturing precision and quality.

Mesjasz-Lech Agata, Kemendi Ágnes, Michelberger Pál

The article aims (1) to evaluate material flows in the manufacturing process reflecting the level of circular manufacturing of European Union countries and (2) to estimate the relationship between the level of circular manufacturing and the volume of e-waste put on the market, illustrating the implementation effect of Industry 5.0 technologies. A systematic country classification was created according to development conditions for environmentally sustainable enterprises and trends in e-waste volumes. Multidimensional data analysis and the linear ordering method were used to achieve the research objectives. The dynamics of changes in the identified variables were analysed using dynamics indexes and the average annual rate of change. Relationships were estimated using Pearson’s linear correlation coefficient. The main research result is the estimated synthetic development measure illustrating the level of circular manufacturing in the context of material flows. Significant differences were observed between the synthetic development measure values representing the level of circular manufacturing in European Union countries. This means countries’ circular manufacturing levels are significantly higher than others. Moreover, the values of correlation coefficients were estimated between the level of circular manufacturing and the volume of e-waste put on the market and between the average annual rate of change of the synthetic development measure and the average annual rate of change of the e-waste volume. The coefficient values do not confirm a statistically significant relationship between the indicated variables. Most countries have average conditions for developing environmentally sustainable businesses, but at the same time, they show negative trends in the volume of e-waste generated.

DOCIU Maria-Ariana, GHERGHEL Sabina

The textile industry is one of the most flourishing and developed at the level of the European Union and as a result, being such an important field, it is imperative to have some legislative acts at the European level in order to regulate and harmonise the conduct of the Member States. The main goal of the Regulation No 1007/2011 of the European Parliament and of the Council is to set up common rules on both fabric names and the composition of textile products and, at the same time, to harmonise the indications that appear on the labels, markings and documents which accompany such products throughout the process of their production and distribution. In addition to this Regulation, there are other normative acts at the level of the European Union with a particular impact on the textile industry. We are taking into consideration, alongside with the above-mentioned Regulation, the Directive 2001/95/EC of the European Parliament and of the Council, which refers to the general product safety and other acts in connection with these. The aim of this paper is to briefly present the provisions of these legislative acts, as they are closely connected to the textile industry.

Kremer, Sebastian, Konrad, Leon, Stroh, Max-Ferdinand et al.

Originating in 2011, Industry 4.0 describes the digital revolution of industry and has since become a collective term for smart, mutable and data driven factories. During the last decade systemic and methodical solutions were designed and implemented that enable corresponding data driven use cases for producers. Today's system providers offer complex data ecosystems in which data-driven use cases are built-in and implementers offer focused digitalisation projects to rapidly address quick wins. While an assessment of expectations around Industry 4.0 results in requirements within the domains of modifiability, connectivity, data and organisation for an IT-architecture, many such solutions are found to be violating essential requirements as systemic flexibility and data-availability. Not only is this a relevant matter for architectural purists, but it highlights real problems that industry is still facing while applying digitalisation measures in pursuit of Industry 4.0. While event-driven architectures go back to the design of modern operating systems, the emergence of powerful, resilient and cheap broker-technologies has risen the polarity of event-driven IT-architectures for businesses in the last decade. Although its occurrence is predominantly represented in ecommerce, finance and insurance, many prominent manufactures have since begun their transformation into an event-driven IT-architecture. Reasons for this architectural adaptation include exceptional data availability, resilience, scalability and especially data sovereignty. An assessment of event-driven IT-architecture's properties and implications reveals an excellent fit for the architectural requirements of Industry 4.0. In this work the subject of Industry 4.0 is analysed along literature to derive a collective understanding of expectations from a factory implementing Industry 4.0. Subsequently, IT-architectural requirements are derived that describe an architecture capable of satisfying these expectations. Then event-driven IT-architectures are analysed regarding their structural composition and capabilities. Finally, the fit of event-driven IT-architecture is evaluated against the architectural requirements of Industry 4.0, discussing congruence and divergence.

Md. Noor-A-Rahim, Jobish John, Fadhil Firyaguna et al.

Smart manufacturing is a vision and major driver for change in industrial environments. The goal of smart manufacturing is to optimize manufacturing processes through constantly monitoring and adapting processes towards more efficient and personalised manufacturing. This requires and relies on technologies for connected machines incorporating a variety of computation, sensing, actuation, and machine to machine communications modalities. As such, understanding the change towards smart manufacturing requires knowledge of the enabling technologies, their applications in real world scenarios and the communications protocols that they rely on. This paper presents an extensive review of wireless machine to machine communication protocols currently applied in manufacturing environments and provides a comprehensive review of the associated use cases whilst defining their expected impact on the future of smart manufacturing. Based on the review, we point out a number of open challenges and directions for future research.

Bo Dai, Fenfen Li

A centralized collaboration problem of customer-to-manufacturer (C2M) platform in e-commercial business is studied in this paper, where an e-commercial company and multiple manufacturers come to an agreement for constituting a collaborative alliance to satisfy the product orders of multiple customers. The problem has two important issues which are the optimal reallocation of customer orders among the manufacturers to maximize a total profit of the alliance and a fair allocation of the profit among the manufacturers so as to assure the stability of the alliance and the sustainability of C2M platform. A profit allocation mechanism is proposed which is based on core allocation concept and considers the contribution of each manufacturer in fulfilling orders. The effectiveness of the mechanism is evaluated with numerical experiments on sixty instances generated based on real data of Alibaba.

Mohammad Faruk Hossain, Che Thalbi bt Md. Ismail, Nazli Mahdzir

For Muslims, all worldly deeds are equal to worship if they are performed in the way given by Allah (SWT). Every aspect of human life is recorded in the Muslim’s Holy book Al-Quran. The Muslim way of eating or trading is determined and governed by Islamic law. Allah (SWT) has ordained for Muslims all the permissible and forbidden worldly matters. The main purpose of this article is to discuss the impact of corporate social responsibility with halal food and halal food business in Bangladesh. Halal food is one of the most important elements of daily life of Muslims. This article focuses on what kind of steps a halal food business needs to take to protect the religious beliefs and obligations of Muslims. So that the supply of Bangladeshi halal food in the international market and halal quality of international consumers can be ensured.

Lequn Chen, Xiling Yao, Peng Xu et al.

Surface monitoring is an essential part of quality assurance for additive manufacturing (AM). Surface defects need to be identified early in the AM process to avoid further deterioration of the part quality. In this paper, a rapid surface defect identification method for directed energy deposition (DED) is proposed. The main contribution of this work is the development of an in-situ point cloud processing with machine learning methods that enable automatic surface monitoring without sensor intermittence. An in-house software platform with a multi-nodal architecture is developed. In-situ point cloud processing steps, including filtering, segmentation, surface-to-point distance calculation, point clustering, and machine learning feature extraction, are performed by multiple subprocesses running simultaneously. The combined unsupervised and supervised machine learning techniques are applied to detect and classify surface defects. The proposed method is experimentally validated, and a surface defect identification accuracy of 93.15% is achieved.