Hasil untuk "Manufactures"

M. Vaezi, H. Seitz, Shoufeng Yang

B. Conner, G. Manogharan, Ashley Nicole Martof et al.

K. Cua, K. McKone, R. Schroeder

H. Brussel, J. Wyns, P. Valckenaers et al.

A. Serajuddin, A. Serajuddin

T. Dunne, Mark J. Roberts, L. Samuelson

W. Hopp, M. Spearman

R. Frosch, N. E. Gallopoulos

Y. Yusuf, M. Sarhadi, A. Gunasekaran

R. Wise, Peter Baumgartner

Jingchao Jiang, X. Xu, J. Stringer

Additive manufacturing (AM) has developed rapidly since its inception in the 1980s. AM is perceived as an environmentally friendly and sustainable technology and has already gained a lot of attention globally. The potential freedom of design offered by AM is, however, often limited when printing complex geometries due to an inability to support the stresses inherent within the manufacturing process. Additional support structures are often needed, which leads to material, time and energy waste. Research in support structures is, therefore, of great importance for the future and further improvement of additive manufacturing. This paper aims to review the varied research that has been performed in the area of support structures. Fifty-seven publications regarding support structure optimization are selected and categorized into six groups for discussion. A framework is established in which future research into support structures can be pursued and standardized. By providing a comprehensive review and discussion on support structures, AM can be further improved and developed in terms of support waste in the future, thus, making AM a more sustainable technology.

F. Calignano, D. Manfredi, E. Ambrosio et al.

J. Arinez, Q. Chang, R. Gao et al.

Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

Shan Ren, Yingfeng Zhang, Yang Liu et al.

Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and ...

A. Panesar, M. Abdi, D. Hickman et al.

A number of strategies that enable lattice structures to be derived from Topology Optimisation (TO) results suitable for Additive Manufacturing (AM) are presented. The proposed strategies are evaluated for mechanical performance and assessed for AM specific design related manufacturing considerations. From a manufacturing stand-point, support structure requirement decreases with increased extent of latticing, whereas the design-to-manufacture discrepancies and the processing efforts, both in terms of memory requirements and time, increase. Results from Finite Element (FE) analysis for the two loading scenarios considered: intended loading, and variability in loading, provide insight into the solution optimality and robustness of the design strategies. Lattice strategies that capitalised on TO results were found to be considerably (∼40-50%) superior in terms of specific stiffness when compared to the structures where this was not the case. The Graded strategy was found to be the most desirable from both the design and manufacturing perspective. The presented pros-and-cons for the various proposed design strategies aim to provide insight into their suitability in meeting the challenges faced by the AM design community.

F. Tao, Qinglin Qi

Recently, along with the wide application of new generation information technologies (New IT) in manufacturing, many countries issued their national advanced manufacturing development strategies, such as Industrial Internet, Industry 4.0, and Made in China 2025. One common aim of these strategies is to achieve smart manufacturing, which demands the interoperation, integration, and fusion of the physical world and the cyber world of manufacturing. As well, New IT [such as Internet of Things (IoT), cloud computing, big data, mobile Internet, and cyber-physical systems (CPS)] have played pivotal roles in promoting smart manufacturing. Data generated in the physical world can be sensed and transfered to the cyber world through IoT and the Internet, and be processed and analyzed by cloud computing, big data technologies to adjust the physical world. The physical world and the cyber world of manufacturing are integrated based on CPS. On the other hand, servitization has become a prominent trend in the manufacturing. Embracing the concept of “Manufacturing-as-a-Service,” manufacturing is provided as service for users. Because of the characteristics of interoperability and platform independence, services pave the way for large-scale smart applications and manufacturing collaboration. Combining New IT and services, this paper proposes a framework—New IT driven service-oriented smart manufacturing (SoSM). SoSM aims at facilitating the visions of smart manufacturing by making full use of New IT and services. Complementary to the framework of SoSM, the New IT driven typical characteristics of SoSM are also investigated and discussed, respectively.

Wenceslao Piedra-Cascón, Vinayak R. Krishnamurthy, W. Att et al.

OBJECTIVE To review the elements of the vat-polymerization workflow, including the 3D printing parameters, support structures, slicing, and post-processing procedures, as well as how these elements affect the characteristics of the manufactured dental devices. DATA Collection of published articles related to vat-polymerization technologies including manufacturing workflow description, and printing parameters definition and evaluation of its influence on the mechanical properties of vat-polymerized dental devices was performed. SOURCES Three search engines were selected namely Medline/PubMed, EBSCO, and Cochrane. A manual search was also conducted. STUDY SELECTION The selection of the optimal printing and supporting parameters, slicing, and post-processing procedures based on dental application is in continuous improvement. As well as their influence on the characteristics of the additively manufactured (AM) devices such as surface roughness, printing accuracy, and mechanical properties of the dental device. RESULTS The accuracy and properties of the AM dental devices are influenced by the manufacturing trinomial namely technology, printer, and material selected. The printing parameters, printing structures, slicing methods, and the post-processing techniques significantly influence on the surface roughness, printing accuracy, and mechanical properties of the manufactured dental device; however, the optimization of each one may vary depending on the clinical application of the additively manufactured device. CONCLUSIONS The printing parameters, supporting structures, slicing, and post-processing procedures have been identified, but further studies are required to determine the optimal manufacturing protocol and enhance the properties of the AM polymer dental devices. CLINICAL SIGNIFICANCE The understanding of the factors involved in the additive manufacturing workflow leads to a printing success and better outcome of the additively manufactured dental device.

Sheng Chen, Hong Zhang

Sachin S. Kamble, A. Gunasekaran, Abhijeet Ghadge et al.

Abstract The smart manufacturing systems (SMS) offer several advantages compared to the traditional manufacturing systems and are increasingly being adopted by manufacturing organizations as a strategy to improve their performance. Developing an SMS is expensive and complicated, integrating together various technologies such as automation, data exchanges, cyber-physical systems (CPS), artificial intelligence, internet of things (IoT), and semi-autonomous industrial systems. The Small, Medium and Micro Enterprises (SMMEs) have limited resources and therefore, would like to see the benefits from investments before allowing adopting SMS. This study uses a combination of exploratory and empirical research design to identify and validate the performance measures relevant to the evaluation of SMS investments in auto-component manufacturing SMMEs based in India. The study found that an Industry 4.0 enabled SMS offer more competitive benefits compared to a traditional manufacturing system. The planned investments in SMS can be evaluated on ten performance dimensions namely, cost, quality, flexibility, time, integration, optimized productivity, real-time diagnosis & prognosis, computing, social and ecological sustainability. Proposed novel Smart Manufacturing Performance Measurement System (SMPMS) framework is expected to guide the practitioners in SMMEs to evaluate their SMS investments.

Sachin Kumar, T. Gopi, N. Harikeerthana et al.

For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.