Hasil untuk "Manufacturing industries"

J. S. Hunter

R. Levin, A. Klevorick, R. Nelson et al.

Juan Gallegos Mardones, Maria J. Ibañez

In recent times, women have been increasing their participation in company management positions, occupying positions on the board of directors, and making strategic decisions with a view to the international expansion of the firms. However, the evidence of the participation of women in the processes of internationalization of companies is not conclusive or adequate for emerging economies, such as those in Latin America. Based on this premise, we conducted this research using data from 1,246 firms included in the Fifth Longitudinal Survey of Chilean Companies. These companies are categorized into four industries: 98 in extractive, 149 in manufacturing, 285 in Commercial, and 714 in services. A Tobit regression model to estimate the influence of the presence of women in senior management and gender diversity on boards on the export intensity of the company. The main findings show that women in the position of CEO and a higher percentage of women on boards of directors do not affect the export intensity of the company. They also show that the relationship between foreign ownership and export intensity was positive and, finally, that export intensity did not depend on gender diversity in high-level administrative positions. An important limitation of this research is not having a data panel for each company to estimate their evolution over time thus, as well as information related to compensation contracts for management teams. For future research, we are interested in studying the effect of ownership on the internationalization process.

Danny Hoang, Anandkumar Patel, Ruimen Chen et al.

Smart manufacturing can significantly improve efficiency and reduce energy consumption, yet the energy demands of AI models may offset these gains. This study utilizes in-situ sensing-based prediction of geometric quality in smart machining to compare the energy consumption, accuracy, and speed of common AI models. HyperDimensional Computing (HDC) is introduced as an alternative, achieving accuracy comparable to conventional models while drastically reducing energy consumption, 200$\times$ for training and 175 to 1000$\times$ for inference. Furthermore, HDC reduces training times by 200$\times$ and inference times by 300 to 600$\times$, showcasing its potential for energy-efficient smart manufacturing.

Andrew Razjigaev, Hans Lohr, Alejandro Vargas-Uscategui et al.

Industrial automation with six-axis robotic arms is critical for many manufacturing tasks, including welding and additive manufacturing applications; however, many of these operations are functionally redundant due to the symmetrical tool axis, which effectively makes the operation a five-axis task. Exploiting this redundancy is crucial for achieving the desired workspace and dexterity required for the feasibility and optimisation of toolpath planning. Inverse kinematics algorithms can solve this in a fast, reactive framework, but these techniques are underutilised over the more computationally expensive offline planning methods. We propose a novel algorithm to solve functionally redundant inverse kinematics for robotic manipulation utilising a task space decomposition approach, the damped least-squares method and Halley's method to achieve fast and robust solutions with reduced joint motion. We evaluate our methodology in the case of toolpath optimisation in a cold spray coating application on a non-planar surface. The functionally redundant inverse kinematics algorithm can quickly solve motion plans that minimise joint motion, expanding the feasible operating space of the complex toolpath. We validate our approach on an industrial ABB manipulator and cold-spray gun executing the computed toolpath.

Gauthier Roussilhe, Thibault Pirson, David Bol et al.

Growing attention is given to the environmental impacts of the digital sector, exacerbated by the increase of digital products and services in our globalized societies. The materiality of the digital sector is often presented through the environmental impacts of mining activities to point out that digitization does not mean dematerialization. Despite its importance, such a narrative is often restricted to a few minerals (e.g., cobalt, lithium) that have become the symbols of extractive industries. In this paper, we further explore the materiality of the digital sector with an approach based on the diversity of elements and their purity requirements in the semiconductor industry. Semiconductors are responsible for manufacturing the key building blocks of the digital sector, i.e., microchips. Given that the need for ultra-high purity materials is very specific to the semiconductor industry, a few companies around the world have been studied, revealing new critical actors in complex supply chains. This highlights strong dependencies towards other industrial sectors with mass production and the need for a deeper investigation of interactions with the chemical industry, complementary to the mining industry.

W. Keller

Dyi-Cheng Chen, Yu-Ting Chen

In recent years, additive manufacturing has been widely used in industrial, medical, and educational fields. Material extrusion is used in most industries to increase development efficiency and reduce costs. This study used the material extrusion to discuss the print quality of additive manufacturing and optimized the processing parameters based on material properties. Based on the literature, this study summarized the fishbone diagram influencing printing quality. The layer height, nozzle temperature, printing speed, infill pattern, and filling spacing were selected as the control factors of the Taguchi method. An orthogonal array L16 was used for parameter design. The optimal parameters were analyzed using the variance and the response surface method. The results of the study are as follows.

Bata Hena, Gabriel Ramos, Clemente Ibarra-Castanedo et al.

Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation solutions in manufacturing, serving safety-critical sectors such as automotive and aerospace industries. However, this method of component fabrication is very susceptible to generating manufacturing flaws, hence necessitating adequate non-destructive testing (NDT) to ascertain the fitness for use of such components. Machine learning has taken the center stage in recent years as a tool for developing automated solutions for detecting and classifying flaws in digital X-ray radiography. These machine learning-based solutions have increasingly been developed and deployed for component inspection, to keep pace with the high production throughput in manufacturing industries. This work focuses on the development of a defect grading algorithm that assesses detected flaws to ascertain if they constitute a defect that could render a component unfit for use. Guided by ASTM 2973-15; Standard Digital Reference Images for Inspection of Aluminum and Magnesium Die Castings, a grading pipeline utilizing K-D (k-dimensional) trees was developed to effectively structure detected flaws, enabling the system to make decisions based on acceptable grading terms. This solution is dynamic in terms of its conformity to different grading criteria and offers the possibility to achieve automated decision making (Accept/Reject) in digital X-ray radiography applications.

Victor Cezar Nepomuceno RIBEIRO, Geraldo BORTOLETTO JÚNIOR

ABSTRACT Brazil stands out as one of the largest manufacturers of MDF (medium density fiberboard) in the world. The industries are concentrated in the south and southeast of the country and are primarily based on the use of Pinus and Eucalyptus wood, which are available in extensive planted areas. In the northern region, there is only one MDF industrial plant. Despite an abundance of potential raw materials in this region, there is a lack of studies on native species wood and their industrial waste utilization for MDF production. The present study aimed to evaluate the properties of MDF manufactured from a mixture of cultivated paricá (Schizolobium amazonicum) wood and wood waste from native Amazonian species. The study assessed the isolated effects of different proportions of the raw materials and panel thicknesses on MDF properties. Panels were produced, and samples were obtained for testing. Using standard procedures, the following properties were determined: density, water absorption, thickness swelling, internal bonding, static bending, and resistance to screw withdrawal. The results revealed a significant impact of the analyzed variables on some physical and mechanical properties of MDF. With the exception of internal bonding, all other properties of the evaluated MDF panels met the specified regulatory requirements for use in furniture manufacturing. It is concluded that mixtures of the assessed raw materials have great potential for MDF production in the furniture industry. However, adjustments in the production process are recommended to improve the internal bonding property.

Shalbolova Urpash, Bissenov Kylyshbay, Makhanov Sagat

Diversification of the oil and gas complex of Kazakhstan is aimed not only at the development and development of oil and gas fields, but also at the further development of the manufacturing industry, in particular, at the construction of new and modernization of existing oil refining capacities. The article presents the results of analytical and research work to determine the effects of diversification on the national economy of Kazakhstan in the case of the construction of a new oil refinery and the modernization of existing production. At the stage of construction of oil refineries, indirect effects for the country's economy mainly appear, as a share of Kazakhstani content. In addition, the activities of the oil refining sector have a multiplier effect on the inter-industry balance in the structure of the national economy. The results of previously conducted studies are presented with an emphasis on multiplier effects, in which economic development takes place in other industries that are most interconnected with the petroleum products production sector. The purpose of the study is to reveal the economic effects for the economy of Kazakhstan when expanding production of oil refining products with added value.

Hertiana Ikasari

This study aims to analyze the export competitiveness and market position of the agricultural, manufacturing, and mining sectors in 10 main export destination countries (China, the United States, Japan, India, Singapore, Malaysia, South Korea, Thailand, the Netherlands, and the Philippines). This study uses secondary data sourced from UN COMTRADE for the period of 2013 – 2018. The data is categorized using a 2-digit Harmonized System (HS) classification. This study uses Revealed Comparative Advantage (RCA) and Export Product Dynamics (EPD) analysis tools. The RCA estimation results show the export competitiveness of Indonesia’s agricultural, industrial, and mining products is still weak and strong in several major export destination countries. Meanwhile, the EPD estimation shows the Indonesia’s exports of agricultural, industry, and mining commodities mostly got rising star positions in some countries but losing opportunity positions in some other countries. The following suggestions proposed are based on the research. In general, the implications of policy to improve the competitiveness of export products in the manufacturing, agriculture, and mining industries are infrastructure improvement, expansion of the export market, improvement in the quality of human resources, and employment, increasing access to finance, increasing the quality and quantity of production in processing, agriculture and mining industries, and maintaining political

David Wichner, Jeffrey Wishart, Jason Sergent et al.

Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV industry based on robust taxonomy development and validation criteria and provides rationale for such an approach. Keywords: Safety Management System (SMS), Automated Driving System (ADS), ADS-Equipped Vehicle, Autonomous Vehicles (AV)

Umut Kose

Plastic scintillator detectors with three-dimensional granularity and sub-nanosecond time resolution offer simultaneous particle tracking, identification, and calorimetry. However, scaling to larger volumes and finer segmentation poses significant challenges in manufacturing and assembly due to high costs, extensive time, and precision requirements. To address this, the 3DET R\&D collaboration has developed an innovative additive manufacturing approach, allowing for the monolithic fabrication of three-dimensional granular scintillators without the need for additional processing steps. A prototype, featuring a 5 $\times$ 5 $\times$ 5 matrix of optically isolated scintillating voxels integrated with wavelength shifting fibers, was manufactured and tested using cosmic rays and CERN test beams, demonstrating comparable light yield and reduced crosstalk compared to traditional methods. The developed additive manufacturing technique offers a viable, time-efficient, and cost-effective solution for producing next-generation scintillator detectors, maintaining high performance regardless of size and geometric complexity.

Fabian Gumpert, Annika Janßen, Robin Basu et al.

For the manufacturing of thin films of solution-processable organic semiconductors, e.g. for organic photovoltaics (OPV), meniscus guided-coating techniques are the method of choice for large-scale industrial applications. However, the process requires an in-depth understanding of the respective fluid dynamics to control the resulting film thickness. In this article, we derive an analytical expression to describe the layer thickness of coatings manufactured with a trapezoidal-shaped applicator as a function of various fluid and process parameters. The analytical calculations are compared with results from computational fluid dynamics (CFD) simulations and experimental data for an industrially relevant OPV active material system. The analytical calculations are compared with results from computational fluid dynamics (CFD) simulations and experimental data for an industrially relevant OPV active material system. The good agreement of all three approaches demonstrates the potential of the analytical and simulative methods to reduce time- and resource-consuming experiments to a minimum. Furthermore, our theoretical model can be used to enhance the homogeneity of large-area coatings by means of an acceleration profile of the applicator that can compensate the liquid loss during the coating process. The respective analytical expression is validated by simulated and experimentally obtained data for long-distance coatings. Finally, this approach is used to fabricate a large-area OPV module with new world record efficiency.

Younes Chahid, Carolyn Atkins, Greg Lister et al.

Despite the established role of additive manufacturing (AM) in aerospace and medical fields, its adoption in astronomy remains low. Encouraging AM integration in a risk-averse community necessitates documentation and dissemination of previous case studies. The objective of this study is to create the first review of AM in astronomy hardware, answering: where is AM currently being used in astronomy, what is the status of its adoption, and what challenges are preventing its widespread use? The review starts with an introduction to astronomical instruments size/cost challenges, alongside the role of manufacturing innovation. This is followed by highlighting the benefits/challenges of AM and used materials/processes in both space-based and ground-based applications. The review case studies include mirrors, optomechanical structures, compliant mechanisms, brackets and tooling applications that are either in research phase or are implemented.

Chuan Luo, Federico Ferrari, James K. Guest

Tow steering technologies, such as Automated fiber placement, enable the fabrication of composite laminates with curvilinear fiber, tow, or tape paths. Designers may therefore tailor tow orientations locally according to the expected local stress state within a structure, such that strong and stiff orientations of the tow are (for example) optimized to provide maximal mechanical benefit. Tow path optimization can be an effective tool in automating this design process, yet has a tendency to create complex designs that may be challenging to manufacture. In the context of tow steering, these complexities can manifest in defects such as tow wrinkling, gaps, overlaps. In this work, we implement manufacturing constraints within the tow path optimization formulation to restrict the minimum tow turning radius and the maximum density of gaps between and overlaps of tows. This is achieved by bounding the local value of the curl and divergence of the vector field associated with the tow orientations. The resulting local constraints are effectively enforced in the optimization framework through the Augmented Lagrangian method. The resulting optimization methodology is demonstrated by designing 2D and 3D structures with optimized tow orientation paths that maximize stiffness (minimize compliance) considering various levels of manufacturing restrictions. The optimized tow paths are shown to be structurally efficient and to respect imposed manufacturing constraints. As expected, the more geometrical complexity that can be achieved by the feedstock tow and placement technology, the higher the stiffness of the resulting optimized design.

Cuneyt Eroglu, Christian Hofer

A. Levinson

Shams Ul Arfeen Laghari, Selvakumar Manickam, Ayman Khallel Al-Ani et al.

Industry 4.0, as a driving force, is making massive achievements, notably in the manufacturing sector, where all key components engaged in the production processes are being digitally interconnected. However, when combined with enhanced automation and robotics, machine learning, artificial intelligence, big data, cloud computing, and the Internet of Things (IoT), this open network interconnectivity renders industrial systems more vulnerable to cyberattacks. Cyberattacks may have a variety of different impacts and goals, but they always have negative repercussions for manufacturers. These repercussions include financial losses, disruption of supply chains, loss of reputation and competitiveness, and theft of corporate secrets. Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) is a legacy Machine-to-Machine (M2M) communication protocol used profoundly in the semiconductor and other manufacturing industries. SECS/GEM is mainly designed to be utilized in a trusted, controlled, and regulated factory environment separated from external networks. Industry 4.0 has revolutionized the manufacturing industry and has brought SECS/GEM back to the limelight, as SECS/GEM is completely devoid of security features. This research proposes ES-SECS/GEM, an Efficient Security mechanism that provides authentication, integrity, and protection against cyberattacks. The proposed mechanism is compared to other security mechanisms in terms of processing time, control overhead, and resilience against cyber-attacks. The ES-SECS/GEM demonstrated promising results, suggesting that it allowed SECS/GEM devices to only connect with authorized industrial equipment, maintained message integrity, discarded forged messages, and prevented cyberattacks on SECS/GEM communications. In terms of processing time and control, ES-SECS/GEM likewise outperformed other mechanisms and incurred the lowest values for these metrics.