China
Africa
Explore chapters and articles related to this topic
Documentation
Published in Bahram Nassersharif, Engineering Capstone Design, 2022
Project Specific Details and Analysis – Include project-specific details in this section, including data collection activity, engineering analysis, market analysis for a product, sponsor survey for process design specific to the team’s project. For example Product design teams should include market analysis, demand forecasting, cost versus price information, and surveys of potential users.Process design teams include flowcharts, process diagrams, floor plans of the current process, and time studies of the current operations.National student design competition teams should document work done to fulfill the design competition requirements that go beyond typical course requirements.
Biofuels Production Processes and Technologies
Published in M.R. Riazi, David Chiaramonti, Biofuels Production and Processing Technology, 2017
Franziska Müller-Langer, Marco Klemm, Jens Schneider, M.R. Riazi, David Chiaramonti
Schmitz et al. (2015) suggest the synthesis of OMEs directly from methanol and formaldehyde in a batch reactor. Economic advantages are expected for this approach due to a more simple process design. Besides, Zhang et al. (2016) modeled the production of OMEs from woody biomass via gasification and methanol synthesis as intermediate steps (Zhang et al. 2016).
Dynamic design method of digital twin process model driven by knowledge-evolution machining features
Published in International Journal of Production Research, 2022
Jinfeng Liu, Peng Zhao, Xuwen Jing, Xuwu Cao, Sushan Sheng, Honggen Zhou, Xiaojun Liu, Feng Feng
In general, the manufacturing activities are guided based on the constructed process plan, which is a pivotal link between design and manufacturing. Process design involves the selection of necessary manufacturing processes and the decision of their sequences to transform a designer’s idea into a physical component economically and competitively. Among the key components of marine diesel engines, a large number of machining parts have the complex machining processes and high quality of the machining face. And the real-time data such as equipment status, tool status and work-piece status, have an important impact on the processing quality. By combining DTPM, a large number of processing problems (e.g. processing quality prediction, equipment fault diagnosis, working hour’s statistics, etc.) are timely solved. In order to verify the effectiveness of the proposed method, the machining efficiency and quality are chosen as the research object, which is described as follows.
Big data analytics energy-saving strategies for air compressors in the semiconductor industry – an empirical study
Published in International Journal of Production Research, 2022
Kuo-Hao Chang, Yi-Jyun Sun, Chi-An Lai, Li-Der Chen, Chih-Hung Wang, Chung-Jung Chen, Chih-Ming Lin
Smart manufacturing is characterised by the utilisation of digital information technology and automated processes that can facilitate a high degree of adaptability and the implementation of rapid design changes on the manufacturing floor (Chen et al. 2017). The backbone of a smart factory is the cyber-physical system (CPS). CPSs bring together physical space and virtual cyberspace, enabling real-time information exchange and feedback loops between these two mediums; CPSs thus equip smart factories with self-sensing and self-correcting abilities (Monostori 2018). To achieve information exchange, smart devices such as real-time sensors and supervisory control and data acquisition systems (SCADA) are essential. The data collected by these smart devices can be further used for prediction and optimisation through big data analytics (Li et al. 2018). This, in turn, allows for decision-making of the process design that can improve the bottom line of manufacturing companies either through decreasing production costs or enhancing production efficiency. In fact, it has been shown that big data and business analytics (BDBA) can facilitate agile manufacturing and yield better competitive and business performance objectives (Gunasekaran et al. 2017).
An automatic machining process decision-making system based on knowledge graph
Published in International Journal of Computer Integrated Manufacturing, 2021
Liang Guo, Fu Yan, Yuqian Lu, Ming Zhou, Tao Yang
Process design is an important basic work of the modern manufacturing industry, which is the bridge between product design and product manufacturing(Hicks et al. 2002). The quality and efficiency of process design directly affect the allocation and optimization of manufacturing resources, product quality, production organization efficiency, product cost, and production cycle. With the advent of the information age, the development of modern products is facing unprecedented challenges. The current engineering design process shows an imbalance between the time required for non-creative activities and the time available for the exploration of innovative design spaces. The imbalance has been growing over time and becomes excessive for complex products developed in modern manufacturing models(La Rocca and van Tooren 2012). In addition, for modern products in the machinery field and other industries, there is enormous pressure from the market in terms of demands for shorter lead time and stricter quality requirement(Peng et al. 2019). In practice, this means that products of increasing complexity have to be developed in a shorter time frame. Traditional product design methods that use limited or manual review of process manuals, management models, and information processing methods can no longer meet the high-quality and high-efficiency development needs of modern enterprises. Therefore, how to implement intelligent process decisions for complex products in a shorter period of time is essential for achieving shorter cycle development of complex products in modern manufacturing models.