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Benefits and Challenges in Additive Manufacturing and Its Applications
Published in Sarbjeet Kaushal, Ishbir Singh, Satnam Singh, Ankit Gupta, Sustainable Advanced Manufacturing and Materials Processing, 2023
Rajwinder Singh, Mohammod Toseef, Jaswinder Kumar, Jashanpreet Singh
Since AM’s inception, researchers have investigated the sustainability implications of the technology in a variety of ways, from broad to restricted (Baumers et al., 2011; Faludi et al., 2015; Gebler et al., 2014; Kohtala & Hyysalo, 2015). This chapter will summarize the following aspects: How might AM allow more sustainable methods of production and consumption?It is the purpose of this chapter to begin to unravel the challenges that exist at the crossroads of these themes by asking: How might additive manufacturing allow more sustainable forms of production and consumption?This is a new research field where the consequences of AM for sustainability remain unknown. A deeper understanding of the consequences of AM for enhancing the sustainability of manufacturing systems may be gained by looking at the issue through the lens of industrial sustainability. “Goods (namely raw materials, different spare parts and products), services, and money” are all exchanged worldwide in complex and linked value chains. From digital production to peer-to-peer production, industrial systems include distributed manufacturing systems, including distributed production modes such as mass customization, customized fabrication, and mass production.
The Future of Unmanned Aircraft Systems
Published in R. Kurt Barnhart, Douglas M. Marshall, Eric J. Shappee, Introduction to Unmanned Aircraft Systems, 2021
The revolution in 3-D printing technology is allowing for more complex components to be produced “on-demand” using a variety of materials including polymers and metals. This is enabling the concept of “distributed manufacturing” where products and parts can be produced and assembled at multiple simultaneous locations (including by the customer), rather than being produced and assembled centrally then distributed through a traditional supply chain. The impact on traditional manufacturing is expected to be significant and revolutionary across multiple industries. As the cost of these 3-D printers continues to decline, it will likely become standard practice to produce and assemble the major components of a UAS at any location (i.e., home). This is closely related to another trend in manufacturing called “additive manufacturing.” Rather than beginning with a larger piece of material and removing what is not needed for a part, raw materials are added incrementally to build the part or component. There is very little, if any, waste.
Innovation and technology
Published in Thomas E. Johnsen, Mickey Howard, Joe Miemczyk, Purchasing and Supply Chain Management, 2018
Thomas E. Johnsen, Mickey Howard, Joe Miemczyk
Since the dawn of industrial revolution and the emergence of the factory system over 200 years ago, manufacturing has been portrayed as typically centralized, machine-based production operations with firms seeking economies-of-scale cost optimization (Srai et al., 2016). The rise of globalization over the past 30 years has seen the spread of international manufacturing sites serving regional and global markets where centralization is still characterized by long and unresponsive supply chains. The emergence of new technologies and approaches to production, however, is starting to challenge previously accepted notions of the supply chain as a basic building block in the manufacturing process. Distributed manufacturing (DM), for example, is a new approach in which breakthroughs in production and infrastructure technologies have enabled smaller and micro-scale manufacture much closer to the end user (Srai et al., 2016). These new forms of manufacturing are enabled by technologies such as sensors and process analytics providing enhanced production control as well as ICT, enabling closer customer involvement in the process, from product conception right through to final delivery. At the heart of this distributed, small-scale production approach is additive manufacturing and 3D printing technology. In the following paragraphs we will refer to this using the term ‘3D printing’.
Supply chain-oriented permutation flowshop scheduling considering flexible assembly and setup times
Published in International Journal of Production Research, 2023
Kuo-Ching Ying, Pourya Pourhejazy, Chen-Yang Cheng, Ren-Siou Syu
The recent advent of Industry 4.0 is revolutionising research in various fields; scheduling is no exception to this trend, particularly because it helps integrate the physical and decisional aspects of production planning, and facilitates autonomous manufacturing within decentralised supply chain systems (Rossit, Tohmé, and Frutos 2019). Distributed manufacturing systems (DMS) play a central role in the industry 4.0-based supply chains. DMS helps improve product quality, the company’s reputation, supply chain cost/time (Wang et al. 1997), and, overall, the competitiveness of the corporate (Renna 2013). Besides, distributed manufacturing has implications for sustainability, i.e. it reduces pollutions due to less global transports and helps the development of small and regional economies (Rauch, Dallasega, and Matt 2016). Given globalisation and rapid technological development, DMSs are decisive to control the manufacturing operations across supply chains and handle the complexities involved (Cunha, Putnik, and Ávila 2000). In this situation, scheduling decisions in distributed manufacturing environments are of high relevance and should receive more attention.
Distributed job-shop rescheduling problem considering reconfigurability of machines: a self-adaptive hybrid equilibrium optimiser
Published in International Journal of Production Research, 2022
M. Mahmoodjanloo, R. Tavakkoli-Moghaddam, A. Baboli, A. Bozorgi-Amiri
Due to the globalisation of the economy and rapidly changing market requirements, some companies have started to use the benefits of distributed manufacturing systems to have the opportunity of more adaptation by becoming closer to both customers and suppliers. Moreover, to increase the flexibility in manufacturing systems, RMTs have been developed to benefit from using several different machines that share many costly and common modules while being rarely used at the same time. Job scheduling in a distributed system that contains RMTs is very complex especially when the decision-making environment is dynamic. Today, interconnection in a network of geographically dispersed facilities thanks to the use of the new emerging technologies of Industry 4.0 can provide manufacturers to utilise real-time data to make efficient decisions in a dynamic environment. To the best of our knowledge, there is no study to tackle this problem. In this paper, we studied a distributed job-shop rescheduling problem, in which the facilities benefit from reconfigurable machines. Firstly, the problem was modelled and solved using mathematical programming. Then, regarding the high level of complexity, a self-adaptive version of a newly introduced meta-heuristic algorithm named Equilibrium Optimizer (EO) was developed to efficiently solve medium- and larger-sized problems in a reasonable time. And finally, a simulation-optimisation model was developed to evaluate the performance of the manufacturing system facing stochastically arriving jobs.
Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis
Published in International Journal of Production Research, 2020
Liane Mahlmann Kipper, Leonardo Bertolin Furstenau, Daniel Hoppe, Rejane Frozza, Sandra Iepsen
Distributed manufacturing is a new strategy that aims to decentralise the production of manufactured products (Rauch, Dallasega, and Matt 2017). Thus Rauch, Unterhofer, and Dallasega (2018) indicate that this strategy differs from the traditional one because the final product will be assembled or produced close to the customer, thereby increasing the efficiency with respect to the use of resources, improvement in product quality, reduction of production costs, and lower management risk. On the other hand, the communication between the factories will be a great challenge, which can be solved through the use of industry 4.0 technologies (Durão et al. 2017). Hence, it is possible to understand the co-occurrence between the clusters ‘distributed manufacturing’ and ‘OPC UA’.