Hasil untuk "Manufactures"

F. De Filippis, A. La Storia, G. Stellato et al.

Mozzarella (M), Grana Padano (GP) and Parmigiano Reggiano (PR) are three of the most important traditional Italian cheeses. In the three cheese manufactures the initial fermentation is carried out by adding natural whey cultures (NWCs) according to a back-slopping procedure. In this study, NWCs and the corresponding curds from M, GP and PR manufactures were analyzed by culture-independent pyrosequencing of the amplified V1–V3 regions of the 16S rRNA gene, in order to provide insights into the microbiota involved in the curd acidification. Moreover, culture-independent high-throughput sequencing of lacS gene amplicons was carried out to evaluate the biodiversity occurring within the S. thermophilus species. Beta diversity analysis showed a species-based differentiation between GP-PR and M manufactures indicating differences between the preparations. Nevertheless, all the samples shared a naturally-selected core microbiome, that is involved in the curd acidification. Type-level variability within S. thermophilus species was also found and twenty-eight lacS gene sequence types were identified. Although lacS gene did not prove variable enough within S. thermophilus species to be used for quantitative biotype monitoring, the possibility of using non rRNA targets for quantitative biotype identification in food was highlighted.

D. Larsson, C. de Pedro, N. Paxéus

Eli Berman, John Bound, Z. Griliches

M. Greenstone

Lindsey D. Cameron

This research explores how a new relation of production—the shift from human managers to algorithmic managers on digital platforms—manufactures workplace consent. While most research has argued that the task standardization and surveillance that accompany algorithmic management will give rise to the quintessential “bad job” (Kalleberg, Reskin, and Hudson, 2000; Kalleberg, 2011), I find that, surprisingly, many workers report liking and finding choice while working under algorithmic management. Drawing on a seven-year qualitative study of the largest sector in the gig economy, the ride-hailing industry, I describe how workers navigate being managed by an algorithm. I begin by showing how algorithms segment the work at multiple sites of human–algorithm interactions and how this configuration of the work process allows for more-frequent and narrow choice. I find that workers use two sets of tactics. In engagement tactics, individuals generally follow the algorithmic nudges and do not try to get around the system; in deviance tactics, individuals manipulate their input into the algorithmic management system. While the behaviors associated with these tactics are practical opposites, they both elicit consent, or active, enthusiastic participation by workers to align their efforts with managerial interests, and both contribute to workers seeing themselves as skillful agents. However, this choice-based consent can mask the more-structurally problematic elements of the work, contributing to the growing popularity of what I call the “good bad” job.

Wonjin Sim, Ji-Woo Lee, Eung-Sun Lee et al.

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.