Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Computer aided parametric-planning (CAPP)

Today\u27s challenge of fast technological advances and global competition requires a shift in our planning paradigm. The same old way simply does not bring about the necessary results. Our paradigm must change to reflect this. Continuous improvement of the planning process is essential to achieve success. Research indicates that the key to project success is to invest quality time in systematic planning at an early stage. Yet, we have relied upon mostly unstructured and manual formation of plans. Existing scientific planning techniques (i.e. Critical Path Method) are scheduling tools for analysis rather than plan generation. They manipulate data provided by planners not the knowledge used in generating project plans. Unlike estimating, where past project data are frequently utilized in formation of quick estimates, planning data from previous jobs are rarely documented or used as a reference.;This study introduces a systematic planning model to guide the end user to conceptualize the planning process and prepare quick and reliable conceptual plans based on similar projects completed in the past. The study provides a framework to capture historical data, synthesize it, and identify the parameters, milestones, and major activities that affect timing, sequencing, and overall planned duration of a project. This framework will serve as the basis for an inference engine module that can be utilized in linking past project data to the present. The model is based on the parametric concept and referred to as the Computer Aided Parametric Planning or CAPP

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges

The chemical treatment of mine-influenced waters is a longstanding environmental challenge for many coal operators, particularly in Central Appalachia. Mining conditions in this region present several unique obstacles to meeting NPDES effluent limits. Outlets that discharge effluent are often located in remote areas with challenging terrain where conditions do not facilitate the implementation of large-scale commercial treatment systems. Furthermore, maintenance of these systems is often laborious, expensive, and time consuming. Many large mining complexes discharge water from numerous outlets, while using environmental technicians to assess the water quality and treatment process multiple times per day. Unfortunately, this treatment method when combined with the lower limits associated with increased regulatory scrutiny can lead to the discharge of non-compliant water off of the mine permit. As an alternative solution, this thesis describes the ongoing research and development of automated protocols for the treatment and monitoring of mine water discharges. In particular, the current work highlights machine learning algorithms as a potential solution for pH control.;In this research, a bench-scale treatment system was constructed. This system simulates a series of ponds such as those found in use by Central Appalachian coal companies to treat acid mine drainage. The bench-scale system was first characterized to determine the volumetric flow rates and resident time distributions at varying flow rates and reactor configurations. Next, data collection was conducted using the bench scale system to generate training data by introducing multilevel random perturbations to the alkaline and acidic water flow rates. A fuzzy controller was then implemented in this system to administer alkaline material with the goal of automating the chemical treatment process. Finally, the performance of machine learning algorithms in predicting future water quality was evaluated to identify the critical input variables required to build these algorithms. Results indicate the machine learning controllers are viable alternatives to the manual control used by many Appalachian coal producers

Development Of Fuzzy Logic Model For Turning Process Of Steel Alloy And Titanium Alloys

The study is about the application of fuzzy logic in representing the machinability data for the turning process. Machining is a very complex process with respect to the influences of the machining parameters such as cutting speed, feed rate, and depth of cut. In order to perform a good machining practice the proper selection of the machinability data, which includes the machining parameters and cutting tools is very important. Normally, the selection of the machinability data is done by the skilled machinist. The manufacturer may face trouble without the presence of the skilled machinists. Thus, there is a necessity to represent the knowledge of the skilled machinists into model, so that any normal machinists will be able to perform a good machining practice by retrieving the information which prescribed in the model. Consequently, fuzzy logic was chosen as a tool to describe the strategy and action of the skilled machinist. In this study, two types of fuzzy models for different workpiece material have been developed, and they are alloy steel and titanium alloys fuzzy models. Both fuzzy models serve the purpose of predicting the appropriate cutting speed and feed rate with respect to the corresponding input variables. Generally, the development of fuzzy model involves the design of three main elements, which are inputs membership functions, fuzzy rules (inference mechanism), and output membership functions. So far, there is no any clear procedure that can be used to develop these three elements. Thus, the strategy for generalizing the development of alloy steel fuzzy model has been suggested. This strategy is useful and less effort is required for developing a related new fuzzy models. The design of fuzzy rules is always the difficult part in developing the fuzzy model due to the tedious way of defining fuzzy rules with the conventional method. Therefore, a new method of developing fuzzy rules, namely fuzzy rule mapping has been introduced and implemented. Through fuzzy rule mapping method, the effort and the time required in developing the fuzzy rules has been reduced. This method has been applied in the developing the fuzzy model for alloy steel. All the predicted outputs (cutting speed and feed rate) from the alloy steel (with general strategy and fuzzy rule mapping) and titanium alloys fuzzy models were being compared with the data obtained from “Machining Data Handbook”, by Metcut Research Associate, and a good match have been obtained throughout the comparison. The average percentage errors for alloy steel fuzzy models with the implementation of general strategy and fuzzy rule mapping are about the ranges of 3.1% to 5.6% and 3.0% to 10.7%, respectively. On the other hand, the average percentage error for titanium alloys fuzzy model is about 1.8% to 5.1%. These results have showed that the machinability data information for the turning of alloy steel and titanium alloys can be represented by fuzzy model. Besides that, it has also proved the feasibility of using the suggested strategy and fuzzy rule mapping method

A framework to manage uncertainties in cloud manufacturing environment

This research project aims to develop a framework to manage uncertainty in cloud manufacturing for small and medium enterprises (SMEs). The framework includes a cloud manufacturing taxonomy; guidance to deal with uncertainty in cloud manufacturing, by providing a process to identify uncertainties; a detailed step-by-step approach to managing the uncertainties; a list of uncertainties; and response strategies to security and privacy uncertainties in cloud manufacturing. Additionally, an online assessment tool has been developed to implement the uncertainty management framework into a real life context. To fulfil the aim and objectives of the research, a comprehensive literature review was performed in order to understand the research aspects. Next, an uncertainty management technique was applied to identify, assess, and control uncertainties in cloud manufacturing. Two well-known approaches were used in the evaluation of the uncertainties in this research: Simple Multi-Attribute Rating Technique (SMART) to prioritise uncertainties; and a fuzzy rule-based system to quantify security and privacy uncertainties. Finally, the framework was embedded into an online assessment tool and validated through expert opinion and case studies. Results from this research are useful for both academia and industry in understanding aspects of cloud manufacturing. The main contribution is a framework that offers new insights for decisions makers on how to deal with uncertainty at adoption and implementation stages of cloud manufacturing. The research also introduced a novel cloud manufacturing taxonomy, a list of uncertainty factors, an assessment process to prioritise uncertainties and quantify security and privacy related uncertainties, and a knowledge base for providing recommendations and solutions

Reusability in manufacturing, supported by value net and patterns approaches

The concept of manufacturing and the need or desire to create artefacts or products is very, very old, yet it is still an essential component of all modem economies. Indeed, manufacturing is one of the few ways that wealth is created. The creation or identification of good quality, sustainable product designs is fundamental to the success of any manufacturing enterprise. Increasingly, there is also a requirement for the manufacturing system which will be used to manufacture the product, to be designed (or redesigned) in parallel with the product design. Many different types of manufacturing knowledge and information will contribute to these designs. A key question therefore for manufacturing companies to address is how to make the very best use of their existing, valuable, knowledge resources. […] The research reported in this thesis examines ways of reusing existing manufacturing knowledge of many types, particularly in the area of manufacturing systems design. The successes and failures of reported reuse programmes are examined, and lessons learnt from their experiences. This research is therefore focused on identifying solutions that address both technical and non-technical requirements simultaneously, to determine ways to facilitate and increase the reuse of manufacturing knowledge in manufacturing system design. [Continues.

Global anthropogenic emissions of particulate matter including black carbon

This paper presents a comprehensive assessment of historical (1990–2010) global anthropogenic particulate matter (PM) emissions including the consistent and harmonized calculation of mass-based size distribution (PM1, PM2. 5, PM10), as well as primary carbonaceous aerosols including black carbon (BC) and organic carbon (OC). The estimates were developed with the integrated assessment model GAINS, where source- and region-specific technology characteristics are explicitly included. This assessment includes a number of previously unaccounted or often misallocated emission sources, i.e. kerosene lamps, gas flaring, diesel generators, refuse burning; some of them were reported in the past for selected regions or in the context of a particular pollutant or sector but not included as part of a total estimate. Spatially, emissions were calculated for 172 source regions (as well as international shipping), presented for 25 global regions, and allocated to 0.5°  ×  0.5° longitude–latitude grids. No independent estimates of emissions from forest fires and savannah burning are provided and neither windblown dust nor unpaved roads emissions are included. We estimate that global emissions of PM have not changed significantly between 1990 and 2010, showing a strong decoupling from the global increase in energy consumption and, consequently, CO2 emissions, but there are significantly different regional trends, with a particularly strong increase in East Asia and Africa and a strong decline in Europe, North America, and the Pacific region. This in turn resulted in important changes in the spatial pattern of PM burden, e.g. European, North American, and Pacific contributions to global emissions dropped from nearly 30 % in 1990 to well below 15 % in 2010, while Asia's contribution grew from just over 50 % to nearly two-thirds of the global total in 2010. For all PM species considered, Asian sources represented over 60 % of the global anthropogenic total, and residential combustion was the most important sector, contributing about 60 % for BC and OC, 45 % for PM2. 5, and less than 40 % for PM10, where large combustion sources and industrial processes are equally important. Global anthropogenic emissions of BC were estimated at about 6.6 and 7.2 Tg in 2000 and 2010, respectively, and represent about 15 % of PM2. 5 but for some sources reach nearly 50 %, i.e. for the transport sector. Our global BC numbers are higher than previously published owing primarily to the inclusion of new sources. This PM estimate fills the gap in emission data and emission source characterization required in air quality and climate modelling studies and health impact assessments at a regional and global level, as it includes both carbonaceous and non-carbonaceous constituents of primary particulate matter emissions. The developed emission dataset has been used in several regional and global atmospheric transport and climate model simulations within the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project and beyond, serves better parameterization of the global integrated assessment models with respect to representation of black carbon and organic carbon emissions, and built a basis for recently published global particulate number estimates