Academic literature on the topic 'Environmentally Sustainable Manufacturing not elsewhere classified'

Gaining grounds as a potential heat transfer fluid due to its superior thermal and tribological properties, Nanofluid Minimum Quantity Lubrication (NMQL) has been classified as an environmentally friendly technique and has already been successfully applied in several machining processes. This paper presents a review of the role of NMQL for different machining processes. The mechanisms of the MQL technique are thoroughly explained for achieving optimal performance based on parameters like nozzle feed position, angle of elevation, distance from the nozzle tip to cutting zone, flow rate, and air pressure. NMQL is shown to enhance cooling performance and lubrication, as well as the tribological properties of the fluid and cutting performance. With government legislative and public opinion pushing manufacturing companies towards sustainable production techniques and practices, the implementation of MQL-nanofluid can slowly prevent the adverse effects that conventional cutting fluids contribute.

1. INTRODUCTION In New York City a decline in manufacturing has propelled social and economic changes that have transformed certain districts [1,2]. Unused building stock there has been the basis for adaptive reuse yielding new housing for families of varying compositions. The constant pressure of the need for affordable housing has resulted in the conversion of existing abandoned industrial structures, providing a green, environmentally friendly alternative to new construction [3,4,5]. Adaptive reuse provides an opportunity to bring a building up to current codes, to make the layout and building systems more appropriate and efficient, and to help revitalize neighborhoods. The nineteenth through the middle of the twentieth centuries were characterized by urban environments which provided manufacturing jobs and the municipal services and education that supported them [6]. American cities such as Detroit, Cleveland, and Pittsburgh became boom-towns as people followed employment opportunities and moved to these locations throughout this period [7,8,9]. In the decades after World War II, the creation of highways and freeways–including the interstate highway system that stretched east to west and north to south–led to suburbanization, exemplified by Long Island's mushrooming Levit-town and many more like it [5,10]. These were the Baby Boom years. The suburban sprawl ultimately resulted in the creation of mega cities like New York City. Families typically consisted of a father, mother, and at least two children [16]. This trend was supported by strong manufacturing industries and plentiful space that allowed much of the population to fulfill the American dream of home ownership [2,11]. As labor cost increased due to stricter labor laws, unions, increasing land cost, and higher taxes, many manufacturers began a search for less costly environments, moving first to locations in the less expensive suburbs and then to the South [4,8]. Eventually, American factories moved overseas to places such as China, other Asian countries, and South America. This became known as out sourcing manufacturing [6,7,12]. With the subsequent boom town collapse that began in the 1980s and continued through the new millennium, old U.S. industrial cities faced declining populations, and Detroit, Cleveland, Pittsburgh, and their like were soon deserted by those who could no longer find employment there [14,40]. City populations decreased by as much as 50% and in some places even more steeply [13]. According to the U.S. Census (figure 1) [13,14], among American cities only New York City's and Los Angeles's populations have grown since the 1980s. Migration for employment opportunities became common and members per household, and households of one or two became not uncommon [15,16]. Typical housing no longer required a big space for shelter and a lawn or garden, and many people looked for smaller units [11,16]. Smaller working spaces made micro-scale businesses possible. New York City is an example of this change. Left with abandoned super block manufacturing buildings such as the Brooklyn Navy Yard and Brooklyn Army Terminal and retired infrastructure, New York City has looked for ways to repurpose these structures [10,17]. Super block, old manufacturing buildings and factories still stand, but in New York and elsewhere some have become mixed-use spaces. The goal of this paper is to examine how New York City served the public by providing working and living space through the conversion of existing super block buildings and creating new public spaces out of under-used or abandoned infrastructure. Comparative case studies are conducted focusing on the micro-scale movement and renewed use of old infrastructure. It considers a future model for sub-divided building spaces and repurposed structures providing shared, public venues as it analyzes this movement structurally and the changes it has wrought on local communities.

Discourse on green construction and sustainable development is currently a major topic in everywhere, including Indonesia. In every production activities, the manufacturing process always produces waste or residual production. In connection with the issue of sustainable development, certainly, the problem of waste utilization that frequently occur in the industrial sector should be minimized and handled so that no adverse impact to the environment. The interior design enterprise is one of the creative industries which is mostly using wood as a raw materials, waste generated from this manufacturing is not in small amount. The community service was collaborated with two interior design enterprises, namely CV Abstrak 3D and CV Garis Pratama, both located in Kendari and classified as a small enterprises. The main activity was to given assistance on the production process-based eco-design, which is a production process that applies the principles of environmentally friendly in every process. Environmentally friendly starting from the identification phase, production, waste processing, until the marketing phase. Through this community service, an increase in productivity as well as knowledge were achieved by both of partners. One of examples of productivity improvement is the ability of the enterprises to process production waste into some valuable goods Keywords: creative industry, interior design, furniture, eco-design, wood waste utilization

Due to increasing environmental regulation and customers’ demand for environmentally friendly products, organizations have been required to adopt sustainable manufacturing practices by implementing clean technology (Cleantec) to manufacture green products. By adopting environmental practices, organizations can also achieve qualitative and quantitative benefits that help them remain competitive in the market while meeting governmental environmental policies, such as lowering energy and the cost of materials. The significant number of articles addressing sustainability in manufacturing published in the past few years attests to the importance of the topic. However, not many studies have been developed to understand the decision-making process in sustainable manufacturing. Therefore, the objective of this paper is to conduct a systematic literature review on the application of multi-attribute decision-making (MADM) methods in sustainable manufacturing. A total of 158 papers, published between 2009 and 2018, met the criteria set in the research methodology. The 158 papers were then analyzed and classified into seven categories: (i) SM domain, (ii) activity within the organization, (iii) decision goals, (iv) decision-makers involved (group or individual), (v) uncertain data, (vi) SM aspects (social, environmental, and economic), and (vii) MADM methods. Among the results, we identified that AHP is the most applied MADM method and, regarding the activities of the organization, MADM methods have been the most frequently applied to strategy management and supply chain. We also identified a tendency to consider uncertain and imprecise data in the decision-making process. Another result is that all the three domains — economic, environmental and social — were considered in most of the papers, followed by the combination of the economic and environmental perspectives. In the conclusion, some recent trends and future research directions are highlighted.

An environmentally-based systems approach to sustainability analyses of organic fruit production systems in New Zealand. This research introduces an approach for the assessment of the sustainability of farming systems. It is based on the premises that sustainability has an environmental bottom line and that there is very limited substitutability between natural capital and other forms of capital. Sustainability assessment is undertaken through analyses of energy and material flows of the system and their impacts on the environment. The proposed sustainability assessment approach is based on two high level criteria for sustainability: efficient use of energy and non-degradation of the environment from energy and material use. Sustainability assessment of organic orchard systems in New Zealand was undertaken to demonstrate this approach. Five indicators which address the two criteria for the sustainability of the orchard systems are the energy ratio, the CO2 ratio, changes in the soil carbon level, nutrient balances, and the leaching of nitrogen. Organic kiwifruit and organic apple systems are modelled based on their key energy and material flows and their interactions with the natural environment. The energy and material flows are converted into appropriate energy and matter equivalents based on coefficients taken from the published literature. Sustainability indicators are estimated over one growing season using two computer modelling tools, Overseer® and Stella®, in a life cycle approach. Sustainability assessment of the organic orchard systems suggests that the approach is useful for evaluating energy use and key environmental impacts that occur in soil, water and atmosphere. The results indicate that the model organic orchard systems are sustainable in terms of energy use and are a net sink of CO2-equivalent emissions. The implication of this result is that organic orchard systems potentially could trade carbon credits under the Kyoto Protocol. The findings also suggest that the sustainability assessment approach is capable of identifying the trade-offs within the sustainability indicators associated with particular management practices. Further research to improve and validate the proposed approach is essential, before it can be practically used for decision making at the orchard level and for policy making at the national level.

One 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.