Hasil untuk "Manufactures"

Masoud Deylami, Negar Izadipour, Adel Alaeddini

Manufacturability assessment is a critical step in bridging the persistent gap between design and production. While artificial intelligence (AI) has been widely applied to this task, most existing frameworks rely on geometry-driven methods that require extensive preprocessing, suffer from information loss, and offer limited interpretability. This study proposes a methodology that evaluates manufacturability directly from parametric design features, enabling explicit incorporation of dimensional tolerances without requiring computer-aided design (CAD) processing. The approach employs Kolmogorov-Arnold Networks (KANs) to learn functional relationships between design parameters, tolerances, and manufacturability outcomes. A synthetic dataset of 300,000 labeled designs is generated to evaluate performance across three representative scenarios: hole drilling, pocket milling, and combined drilling-milling, while accounting for machining constraints and design-for-manufacturing (DFM) rules. Benchmarking against fourteen machine learning (ML) and deep learning (DL) models shows that KAN achieves the highest performance in all scenarios, with AUC values of 0.9919 for drilling, 0.9841 for milling, and 0.9406 for the combined case. The proposed framework provides high interpretability through spline-based functional visualizations and latent-space projections, enabling identification of the design and tolerance parameters that most strongly influence manufacturability. An industrial case study further demonstrates how the framework enables iterative, parameter-level design modifications that transform a non-manufacturable component into a manufacturable one.

Junchen Li, Qiushuang Wang, Swee Leong Sing et al.

Conventional manufacturing of 430 ferritic stainless steel faces significant challenges, including severe work hardening, rapid tool wear, and limited strengthening via heat treatment. To overcome these limitations, this study employs laser powder bed fusion (LPBF) technology to fabricate high-density (99.93%) 430 stainless steel samples through systematic process parameter optimisation. The as-built samples exhibit a unique microstructure characterised by columnar ferrite grains with a <100 >  fibre texture along the building direction and a few oxides enriched in aluminium, oxygen, and nitrogen. In terms of mechanical properties, the as-built sample exhibits high yield strength of approximately 747.5 MPa, nearly double that of the conventionally hot rolled counterpart, while maintaining considerable ductility (∼29.2%). This significant strengthening is primarily attributed to the high-density dislocations generated during the LPBF forming process. This work demonstrates the potential of LPBF for producing high-performance ferritic stainless steels with enhanced mechanical properties.

Ilhan Aziz, Younes Chahid, Jennifer Keogh et al.

Additive manufacturing (AM; 3D Printing), a process which creates a part layer-by-layer, has the potential to improve upon conventional lightweight mirror manufacturing techniques, including subtractive (milling), formative (casting) and fabricative (bonding) manufacturing. Increased mass reduction whilst maintaining mechanical performance can be achieved through the creation of intricate lattice geometries, which are impossible to manufacture conventionally. Further, part consolidation can be introduced to reduce the number of interfaces and thereby points of failure. AM design optimisation using computational tools has been extensively covered in existing literature. However, additional research, specifically evaluation of the optical surface, is required to qualify these results before these advantages can be realised. This paper outlines the development & metrology of an AM mirror for a CubeSat platform with a targeted mass reduction of 60% compared to an equivalent solid body. This project aims to incorporate recent developments in AM mirror design, with a focus on manufacture, testing & evaluation. This is achieved through a simplified design process of a Cassegrain telescope primary mirror mounted within a 3U CubeSat chassis. The mirror geometry is annular with an external diameter of 84 mm and an internal diameter of 32 mm; the optical prescription is flat for ease of manufacture. Prototypes were printed in AlSi10Mg, a low-cost aluminium alloy commonly used in metal additive manufacturing. They were then machined and single-point diamond turned to achieve a reflective surface. Both quantitative & qualitative evaluations of the optical surface were conducted to assess the effect of hot isostatic pressing (HIP) on the optical surface quality. The results indicated that HIP reduced surface porosity; however, it also increased surface roughness and, consequently, optical scatter.

Seyed Ali Ghazi Asgar, Narasimha Reddy, Satish T. S. Bukkapatnam

While additive manufacturing has opened interesting avenues to reimagine manufacturing as a service (MaaS) platform, transmission of design files from client to manufacturer over networks opens up many cybersecurity challenges. Securing client's intellectual property (IP) especially from cyber-attacks emerges as a major challenge. Earlier works introduced streaming, instead of sharing process plan (G-code) files, as a possible solution. However, executing client's G-codes on manufacturer's machines exposes them to potential malicious G-codes. This paper proposes a viable approach when the client and manufacturer do not trust each other and both the client and manufacturer want to preserve their IP of designs and manufacturing process respectively. The proposed approach is based on segmenting and streaming design (STL) files and employing a novel machine-specific STL to G-code translator at the manufacturer's site in real-time for printing. This approach secures design and manufacturing process IPs as demonstrated in a real-world implementation.

Ziqian Wang, Yuhan Qian, Tengteng Sun et al.

Ti-modified AlCuMg alloys have demonstrated great potential in improving the printability and mechanical properties of conventional Al alloys processed by laser powder-bed fusion (LPBF). However, an effective material design method, which considers processing parameters and solidification microstructure, is currently absent. Accordingly, the present work proposed a comprehensive model with which the formation of hot-tearing cracks and lack-of-fusion pores could be more efficiently predicted. The hot-tearing factor was an assistant in the tailoring of crack-free Ti-modified AlCuMg alloys, while the lack-of-fusion factor suggested a volumetric energy density for better printability. This hot-tearing factor could also serve as the supplementary method for evaluating the crack susceptibility of AlCuMg alloys. The microstructure evolution and mechanical properties were also investigated to achieve an optimal processing window for these alloys, which could satisfy the dual requirements of higher mechanical properties and better formability. It was believed that this comprehensive model would facilitate the tailoring of crack-free AlCuMg alloys and the optimisation of processing parameters in a time-efficient manner with high precision.

Chayasmita Deka, Chayasmita Deka, Mrinal Kanti Dutta et al.

Amidst escalating challenges concerning extreme climatic events, the transition to low-carbon lifestyles has emerged as a significant policy priority. To that end, adoption of low-carbon technologies like electric vehicles (EVs) is critical. This study is a novel examination of the socio-psychological mechanisms shaping intentions to adopt EVs in Assam, a fast-developing region in northeast India, characterized by collectivist cultural norms. While existing research has primarily focused on economic, technical, and volitional factors such as perceived behavioral control, environmental awareness and attitudinal variables, this study examines the combined effect of norm and fear-based drivers of intention to adopt EVs. Utilizing the Norm Activation Model (NAM) and the Protection Motivation Theory (PMT), this study identifies subjective norms and perceived vulnerability as the most significant norm-based and fear-based predictor of intention respectively. Structural equation modeling reveals a parallel rather than sequential operation of norm and fear-based constructs, with mediated intention pathways featuring a complex interplay of affect-cognition mechanisms shaping intention. Unlike findings in Western contexts, personal moral norms have less direct impact in shaping intention in a collectivist setting where social validation and group norms weigh higher. Awareness and environmental concern is also found to be ineffective unless it is accompanied with fear cues indicating personal vulnerability and a belief in the possibility of its mitigation. The findings highlight the need for localized, tailored, affect-filled communication strategies over nation-wide financial incentives alone to accelerate EV adoption. The limitations and directions for further research on evolving EV ecosystems are discussed.

Yifei Li, Jeongwon Park, Guha Manogharan et al.

Recent technological innovations in the areas of additive manufacturing and collaborative robotics have paved the way toward realizing the concept of on-demand, personalized production on the shop floor. Additive manufacturing process can provide the capability of printing highly customized parts based on various customer requirements. Autonomous, mobile systems provide the flexibility to move custom parts around the shop floor to various manufacturing operations, as needed by product requirements. In this work, we proposed a mobile additive manufacturing robot framework for merging an additive manufacturing process system with an autonomous mobile base. Two case studies showcase the potential benefits of the proposed mobile additive manufacturing framework. The first case study overviews the effect that a mobile system can have on a fused deposition modeling process. The second case study showcases how integrating a mobile additive manufacturing machine can improve the throughput of the manufacturing system. The major findings of this study are that the proposed mobile robotic AM has increased throughput by taking advantage of the travel time between operations/processing sites. It is particularly suited to perform intermittent operations (e.g., preparing feedstock) during the travel time of the robotic AM. One major implication of this study is its application in manufacturing structural components (e.g., concrete construction, and feedstock preparation during reconnaissance missions) in remote or extreme terrains with on-site or on-demand feedstocks.

Zhichao Wang, Xiaoliang Yan, Shreyes Melkote et al.

Generative design (GD) methods aim to automatically generate a wide variety of designs that satisfy functional or aesthetic design requirements. However, research to date generally lacks considerations of manufacturability of the generated designs. To this end, we propose a novel GD approach by using deep neural networks to encode design for manufacturing (DFM) rules, thereby modifying part designs to make them manufacturable by a given manufacturing process. Specifically, a three-step approach is proposed: first, an instance segmentation method, Mask R-CNN, is used to decompose a part design into subregions. Second, a conditional generative adversarial neural network (cGAN), Pix2Pix, transforms unmanufacturable decomposed subregions into manufacturable subregions. The transformed subregions of designs are subsequently reintegrated into a unified manufacturable design. These three steps, Mask-RCNN, Pix2Pix, and reintegration, form the basis of the proposed Manufacturable conditional GAN (McGAN) framework. Experimental results show that McGAN can transform existing unmanufacturable designs to generate their corresponding manufacturable counterparts automatically that realize the specified manufacturing rules in an efficient and robust manner. The effectiveness of McGAN is demonstrated through two-dimensional design case studies of an injection molding process.

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.

V. M. Akhil, Samarth J. Mangalore, M. C. Chinmay et al.

ABSTRACT3D-printed ABS samples are treated with acetone and coated with copper oxide (CuO) for varying durations to understand how these treatment processes affect their mechanical and tribological properties. Tensile strength, flexural strength, coefficient of friction, and wear of ABS were analyzed under various pre-treatment and coating conditions and it is proved that acetone treatment and CuO coating can be effectively used to tune such properties to meet desired specifications. For 3D-printed ABS structures, CuO coating significantly enhances their tensile strength without acetone treatment. Although acetone treatment reduces their tensile strength as demonstrated, CuO coating can increase it again which means their tensile strength can be tuned by using the two processes. Furthermore, both acetone treatment and CuO coating improve their flexural strength. Additionally, acetone treatment and CuO coating both decrease their coefficient of friction. FESEM and EDS analyses were conducted on the structures to analyze and verify their wear properties.

David M. Wirth, Chi Chung Li, Jonathan K. Pokorski et al.

The field of additive manufacturing (AM) has advanced considerably over recent decades through the development of novel methods, materials, and systems. However, as the field approaches maturity, it is relevant to investigate the scaling frontiers and fundamental limits of AM in a generalized sense. Here we propose a simplified universal mathematical model that describes the essential process dynamics of many AM hardware platforms. We specifically examine the influence of several key parameters on total manufacturing time, comparing these with performance results obtained from real-world AM systems. We find a inverse-cubic dependency on minimal feature size and a linear dependency on overall structure size. These relationships imply how certain process features such as parallelization and process dimensionality can help move toward the fundamental limits. AM methods that are capable of varying the size of deposited voxels provide one possibility to overcome these limits in the future development of AM. We also propose a new framework for classifying manufacturing processes as "top-down" vs "bottom-up" paradigms, which differs from the conventional usage of such terms, and present considerations for how "bottom-up" manufacturing approaches may surpass the fundamental limits of "top-down" systems.

Lam Duc Nguyen, Arne Broering, Massimo Pizzol et al.

In recent years, industrial manufacturing has undergone massive technological changes that embrace digitalization and automation towards the vision of intelligent manufacturing plants. With the aim of maximizing efficiency and profitability in production, an important goal is to enable flexible manufacturing, both, for the customer (desiring more individualized products) and for the manufacturer (to adjust to market demands). Manufacturing-as-a-service can support this through manufacturing plants that are used by different tenants who utilize the machines in the plant, which are offered by different providers. To enable such pay-per-use business models, Distributed Ledger Technology (DLT) is a viable option to establish decentralized trust and traceability. Thus, in this paper, we study potential DLT technologies for an efficient and intelligent integration of DLT-based solutions in manufacturing environments. We propose a general framework to adapt DLT in manufacturing, then we introduce the use case of shared manufacturing, which we utilize to study the communication and computation efficiency of selected DLTs in resource-constrained wireless IoT networks.

Nedeljković Ivana, Rejman-Petrović Dragana

Background: In order to prevent the spread of the Covid-19 virus, a temporary interruption of teaching and educational activities in classrooms occurred. Most schools and faculties were forced to switch from traditional to online teaching. Purpose: This research aims to examine the key factors influencing students' intention to use e-learning, as well as predictors of student satisfaction with online teaching during the Covid-19 pandemic. Study design/methodology/approach: The analysis includes 312 students on the territory of the Republic of Serbia who use e-learning. Reliability analysis, confirmatory factor analysis and structural equations modeling are applied in the paper. Findings/conclusions: It is found that course design significantly affects perceived usefulness, perceived ease of use and quality of e-learning, and perceived usefulness and quality of e-learning are the main drivers of student satisfaction. Then, perceived usefulness, perceived ease of use and satisfaction with online teaching are important predictors of the attitude towards the use of e-learning, and attitude is an important driver of the intention to use e-learning. The results of the research and the implications derived from them can be helpful to educational institutions in creating, introducing and implementing e-learning, as well as increasing student satisfaction with online teaching during the pandemic. Limitations/future research: The limitation of the research stems from the selection of the sample (students). In addition, the research was conducted on the territory of Serbia, so the results cannot be generalized. Third, the possible bias of the respondents in giving answers can lead to wrong conclusions. The recommendation for future research is to examine the attitudes of professors who use e-learning, in addition to students. Another recommendation is to do a segment analysis (by gender, year of study) in order to develop specific strategies for each segment. Another suggestion is to compare students' opinions on e-learning and traditional ways of learning.

Knop Krzysztof

The article presents the result of multidimensional analysis of ‘Behaton’ type paving stones’ nonconformities for improving the production process by improving the quality of the final product. Statistical tools, including SPC tools and quality tools, both basic and new, were used to analyse nonconformities in the spatial-temporal system, i.e. according to the type of nonconformity and according to the examined months. The purpose of using the data analysis tools was to thoroughly analyse the cases of nonconformities of the tested product, obtain information on the structure of these nonconformities in the various terms, and information on the stability and predictability of the numerical structure of nonconformity over time. Potential causes influencing a large percentage of paving stone defects were identified, factors and variables influencing the most frequently occurring nonconformities were determined, and improvement actions were proposed. As a result of the multidimensional and multifaceted analyses of paving stone nonconformities, it was shown that in the structure of nonconformity there were cases that were unusual in terms of the number of occurrences, and the lack of stability in the number of nonconformities in terms of the examined months was proven. Three critical nonconformities of the tested product were identified: side surface defects, vertical edge defects, and scratches and cracks. It was determined that the most important factor causing a large percentage of nonconformity was the time of shaking and vibrating the concrete, which was significantly related to the technical condition of the machines, and the most important reason for a large percentage of paving stone nonconformity was the lack of efficient maintenance. Machine, method, and man turned out to be the most important categories of problem factors and specific remedial actions were proposed. A multidimensional look at the structure of paving stone nonconformity as well as the factor and causes causing them has brought a lot of valuable information for the management staff of the analysed company, thanks to which it is possible to improve the production process and improve the quality of the final product.

Kamran Hameed, Naveed Yazdani, Zamin Abbas et al.

The purpose of this study is two-folded: first, to explore the organizational commitment specifically focusing on where is employees’ commitment; towards the organization or towards their jobs? Secondly, their experiences are analyzed under the situation when their skills are not appreciated by their boss to whom they perceive as incompetent. There are six in-depth interviews were conducted of employees working in private organizations in Lahore Pakistan. Interpretative Phenomenological Analysis is used to analyze transcriptions, and data analysis is performed in Nvivo 11. The finding of this study has drawn the following themes: emotions/feelings, actions, and coping strategies that are linked with social exchange theory. The social exchange process propagates the individual's emotions are aligned with norms and values of the organization, and the nature of this association engages employees with an organization on moral grounds, and this association prolongs when in return organizations treat their employees fairly. Lastly, the themes are also connected with the survival perspective because most of the coping strategies are reflecting how employees are adapting their practices according to the stressful situation, and how they are building their capacity to transform themselves according to the situation.

Priyanka Mahesh, Akash Tiwari, Chenglu Jin et al.

The Industry 4.0 concept promotes a digital manufacturing (DM) paradigm that can enhance quality and productivity, that reduces inventory and the lead-time for delivering custom, batch-of-one products based on achieving convergence of Additive, Subtractive, and Hybrid manufacturing machines, Automation and Robotic Systems, Sensors, Computing, and Communication Networks, Artificial Intelligence, and Big Data. A DM system consists of embedded electronics, sensors, actuators, control software, and inter-connectivity to enable the machines and the components within them to exchange data with other machines, components therein, the plant operators, the inventory managers, and customers. This paper presents the cybersecurity risks in the emerging DM context, assesses the impact on manufacturing, and identifies approaches to secure DM.

Erva Ulu, Nurcan Gecer Ulu, Walter Hsiao et al.

We introduce a method to analyze and modify a shape to make it manufacturable for a given additive manufacturing (AM) process. Different AM technologies, process parameters or materials introduce geometric constraints on what is manufacturable or not. Given an input 3D model and minimum printable feature size dictated by the manufacturing process characteristics and parameters, our algorithm generates a corrected geometry that is printable with the intended AM process. A key issue in model correction for manufacturability is the identification of critical features that are affected by the printing process. To address this challenge, we propose a topology aware approach to construct the allowable space for a print head to traverse during the 3D printing process. Combined with our build orientation optimization algorithm, the amount of modifications performed on the shape is kept at minimum while providing an accurate approximation of the as-manufactured part. We demonstrate our method on a variety of 3D models and validate it by 3D printing the results.

Ali Vatankhah Barenji, Hanyang Guo, Zonggui Tian et al.

Recently, there has been growing interest in the field of cloud manufacturing (CM) amongst researchers in the manufacturing community. Cloud manufacturing is a customer-driven manufacturing model that was inspired by cloud computing, and its major objective was to provide ubiquitous on-demand access to services. However, the current CM architecture suffers from problems that are associated with a centralized based industrial network framework and third part operation. In a nutshell, centralized networking has had issues with flexibility, efficiency, availability, and security. Therefore, this paper aims to tackle these problems by introducing an ongoing project to a decentralized network architecture for cloud manufacturing which is based on the blockchain technology. In essence, this research paper introduces the blockchain technology as a decentralized peer to peer network for multiple cloud manufacturing providers.

Serafín Colín-Urieta, Artemio Carrillo-Parra, José Guadalupe Rutiaga-Quiñones et al.

Ground contact speeds up timber decay because of the large number of microorganisms in soil. This study, we assessed the natural durability of seven tropical species using the European standard EN 807 (2001). We embedded samples of Dalbergia granadillo, Cordia elaeagnoides, Swietenia humillis, Tabebuia donell-smithii, Hura polyandra, Enterolobium cyclocarpum and Tabebuia rosea and temperate species Fagus sylvatica (as a control) in sandy, clay-sandy-loam and clay-loam for 8, 16, 24 and 32 weeks. We evaluated durability of the samples by determining the mass loss and modulus of elasticity (MOE) loss. The results varied significantly (p < 0.001) depending on timber species and soil type considered. The D. granadillo and C. elaeagnoides were the most durable, with mass losses of 4.5%, 6.5% and MOE losses of 4.5%, 20.5%, respectively. F. sylvatica, T. rosea and E. cyclocarpum samples were the least durable, with mass losses of 22.3-25% and MOE losses of 35.8-59.8%, respectively. Decay was most aggressive in sandy-clay-loam soil followed by the clay-loam soil and finally the sandy soil.

Meng-jiang Cen , Yuan Liu, Xiang Chen

This paper summarizes melting methods of titanium and titanium alloy, such as vacuum arc melting (VAR) and electron beam cold hearth melting (EBCHM), and the related inclusions formed when using these melting methods. Low-density inclusions are resulted from contamination of air, and high-density inclusions are caused by refractory elements. The formation process of inclusions was analysed. The removal mechanism of different kinds of inclusions was specified. Low-density inclusions are removed mainly by resolving. This is a comprehensive process containing reaction diffusion. The resolving rate of high-density inclusions is so low that these inclusions are mainly removed by sedimentation. The experiments and physical models of inclusions are detailed. In various melting methods, vacuum arc melting is prominent. However, this method cannot remove inclusions effectively, which usually results in repeat melting. Electron beam cold hearth melting has the best ability of removing inclusions. These results can provide instructions to researchers of titanium and titanium alloys.