Academic literature on the topic 'Environmentally Sustainable Manufacturing not elsewhere classified'
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Journal articles on the topic "Environmentally Sustainable Manufacturing not elsewhere classified"
Shah, Raj, Khosro A. Shirvani, Alexandra Przyborowski, Nikhil Pai, and Mohsen Mosleh. "Role of Nanofluid Minimum Quantity Lubrication (NMQL) in Machining Application." Lubricants 10, no. 10 (October 18, 2022): 266. http://dx.doi.org/10.3390/lubricants10100266.
Full textLee, Kyungsun, and Catherine Park. "THE SUSTAINABLE MICRO-SCALE MOVEMENT OF COMMUNITIES: CASE STUDIES OF SUBDIVIDED DEVELOPMENT AND ADAPTIVE REUSE OF SHARED SPACE IN NEW YORK CITY." Journal of Green Building 11, no. 1 (March 2016): 23–37. http://dx.doi.org/10.3992/jgb.11.1.23.1.
Full textAndrias, Asri, Siti Belinda Amri, and Aspin Aspin. "PENINGKATAN PRODUKTIVITAS KELOMPOK USAHA DESAIN INTERIOR MELALUI PROSES PRODUKSI BERBASIS ECO-DESIGN DI KOTA KENDARI." Edupreneur: Jurnal Pengabdian kepada Masyarakat bidang Kewirausahaan 1, no. 2 (June 2, 2018). http://dx.doi.org/10.36412/edupreneur.v1i2.354.
Full textPelissari, Renata, Sharfuddin Ahmed Khan, and Sarah Ben-Amor. "Application of Multi-Criteria Decision-Making Methods in Sustainable Manufacturing Management: A Systematic Literature Review and Analysis of the Prospects." International Journal of Information Technology & Decision Making, November 17, 2021, 1–23. http://dx.doi.org/10.1142/s0219622021300020.
Full textDissertations / Theses on the topic "Environmentally Sustainable Manufacturing not elsewhere classified"
Page, Girija. "An environmentally-based systems approach to sustainability analyses of organic fruit production systems in New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Sustainable Agricultural Systems at Massey University, Palmerston North, New Zealand." Massey University, 2009. http://hdl.handle.net/10179/825.
Full text(10695907), Wo Jae Lee. "AI-DRIVEN PREDICTIVE WELLNESS OF MECHANICAL SYSTEMS: ASSESSMENT OF TECHNICAL, ENVIRONMENTAL, AND ECONOMIC PERFORMANCE." Thesis, 2021.
Find full textOne way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.
Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.
Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.
However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.
A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.
To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.
(9726050), Onkar V. Sonur. "The Sustainable Manufacturing System Design Decomposition." Thesis, 2020.
Find full text(5931203), Shahab Shah. "Using the Collective System Design Approach to Facilitate a Sustainable Manufacturing System." Thesis, 2019.
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