A case study on Application of FUZZY logic in Electrical Discharge Machining(EDM)

Electrical Discharge Machining (EDM) is one of the most accurate manufacturing processes available for creating complex or simple shapes and geometries within parts and assemblies. EDM works by eroding material in the path of electrical discharges that form an arc between an electrode tool and the work piece. EDM manufacturing is quite affordable and a very desirable manufacturing process when low counts or high accuracy is required. Turn around time can be fast and depends on manufacturer back log. The EDM system consists of a shaped tool or wire electrode, and the part. The part is connected to a power supply. Sometimes to create a potential difference between the work piece and tool, the work piece is immersed in a dielectric (electrically non-conducting) fluid which is circulated to flush away debris

Research reports: 1991 NASA/ASEE Summer Faculty Fellowship Program

The basic objectives of the programs, which are in the 28th year of operation nationally, are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA Centers. The faculty fellows spent 10 weeks at MSFC engaged in a research project compatible with their interests and background and worked in collaboration with a NASA/MSFC colleague. This is a compilation of their research reports for summer 1991

Diamond turning of contact lens polymers

Contact lens production requires high accuracy and good surface integrity. Surface roughness is generally used to measure the index quality of a turning process. It has been an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. In this study, an ONSI-56 (Onsifocon A) contact lens buttons were used to investigate the triboelectric phenomena and the effects of turning parameters on surface finish of the lens materials. ONSI-56 specimens are machined by Precitech Nanoform Ultra-grind 250 precision machine and the roughness values of the diamond turned surfaces are measured by Taylor Hopson PGI Profilometer. Electrostatics values were measured using electrostatic voltmeter. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness and electrostatic discharge (ESD) on the turned ONSI-56. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. The required data for predictive models were obtained by conducting a series of turning test and measuring the surface roughness and ESD data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for ONSI-56 are compared with each other using mean absolute percentage error (MAPE) for accuracy and computational cost

Utilizing Temporal Information in The EHR for Developing a Novel Continuous Prediction Model

Type 2 diabetes mellitus (T2DM) is a nation-wide prevalent chronic condition, which includes direct and indirect healthcare costs. T2DM, however, is a preventable chronic condition based on previous clinical research. Many prediction models were based on the risk factors identified by clinical trials. One of the major tasks of the T2DM prediction models is to estimate the risks for further testing by HbA1c or fasting plasma glucose to determine whether the patient has or does not have T2DM because nation-wide screening is not cost-effective. Those models had substantial limitations on data quality, such as missing values. In this dissertation, I tested the conventional models which were based on the most widely used risk factors to predict the possibility of developing T2DM. The AUC was an average of 0.5, which implies the conventional model cannot be used to screen for T2DM risks. Based on this result, I further implemented three types of temporal representations, including non-temporal representation, interval-temporal representation, and continuous-temporal representation for building the T2DM prediction model. According to the results, continuous-temporal representation had the best performance. Continuous-temporal representation was based on deep learning methods. The result implied that the deep learning method could overcome the data quality issue and could achieve better performance. This dissertation also contributes to a continuous risk output model based on the seq2seq model. This model can generate a monotonic increasing function for a given patient to predict the future probability of developing T2DM. The model is workable but still has many limitations to overcome. Finally, this dissertation demonstrates some risks factors which are underestimated and are worthy for further research to revise the current T2DM screening guideline. The results were still preliminary. I need to collaborate with an epidemiologist and other fields to verify the findings. In the future, the methods for building a T2DM prediction model can also be used for other prediction models of chronic conditions

Preparation, Proximate Composition and Culinary Properties of Yellow Alkaline Noodles from Wheat and Raw/Pregelatinized Gadung (Dioscorea Hispida Dennst) Composite Flours

The steady increase of wheat flour price and noodle consumptions has driven researchers to find substitutes for wheat flour in the noodle making process. In this work, yellow alkaline noodles were prepared from composite flours comprising wheat and raw/pregelatinized gadung (Dioscorea hispida Dennst) flours. The purpose of this work was to investigate the effect of composite flour compositions on the cooking properties (cooking yield, cooking loss and swelling index) of yellow alkaline noodle. In addition, the sensory test and nutrition content of the yellow alkaline noodle were also evaluated for further recommendation. The experimental results showed that a good quality yellow alkaline noodle can be prepared from composite flour containing 20% w/w raw gadung flour. The cooking yield, cooking loss and swelling index of this noodle were 10.32 g, 1.20 and 2.30, respectively. Another good quality yellow alkaline noodle can be made from composite flour containing 40% w/w pregelatinized gadung flour. This noodle had cooking yield 8.93 g, cooking loss 1.20, and swelling index of 1.88. The sensory evaluation suggested that although the color, aroma and firmness of the noodles were significantly different (p ≤ 0.05) from wheat flour noodle, but their flavor remained closely similar. The nutrition content of the noodles also satisfied the Indonesian National Standard for noodle. Therefore, it can be concluded that wheat and raw/pregelatinized gadung composite flours can be used to manufacture yellow alkaline noodle with good quality and suitable for functional food

An Experimental Study on Reducing the Formation of Dross when Cutting 1018 HR Steel Using a CNC Plasma Cutter

Many manufacturers who cut metal use plasma arc cutting as part of their manufacturing process. Plasma cutters use electricity and pressurized gas to produce a temperature of up to 50,000 ºF at the cutting tip. These plasma cutters can rapidly cut through metals as much as 12 inches thick. The use of computer numerical controlled (CNC) plasma cutters allow manufacturers to rapidly cut even very intricate and detailed flat parts. This process is a tremendous improvement over traditional torch cutting, saw cutting, or other machining processes for producing near net shapes. It is faster and less expensive than most of the alternative processes available. There are several processing and quality factors that must be addressed when using a plasma cutter. The most common problem with plasma cutting is the formation of dross (resolidified metal) on the cut edge. The formation of dross on plasma-cut parts creates several problems in the manufacturing process. By carefully controlling the operating parameters, the formation of dross on the work piece can be minimized, which greatly increases the quality of the part and the efficiency of the production process. Efficient operation of a CNC plasma cutter to minimize the formation of dross requires controlling several variables in the process. These variables include: material type and thickness, arc current (amperage), cutting speed, cutting-gas pressure, cutting tip size, and the gap between the cutting tip and the work piece. Experience with plasma arc cutting and research on the subject reveals that the variables that most affect the formation of dross are arc current, cutting speed, material thickness, and nozzle size. A study involving these four variables will be performed to determine the optimum setup for the CNC plasma cutter to minimize the formation of dross.Michael E. DeVoreJetley, Sudershan(Bowling Green State University)Gordon MintyMarion D. SchaferJames E. SmallwoodTodd Waggoner(Bowling Green State University)Doctor of Clinical PsychologyDepartment of Technology ManagementCunningham Memorial library, Terre Haute, Indiana State University20110719-002DoctoralTitle from document title page. Document formatted into pages: contains 179 p.: ill. Includes abstract and appendix

Improving the Management of Large Colorectal Polyps

This thesis is focused on identifying current practices in the management of large non pedunculated colorectal polyps (LNPCPs) and the development of a structured management framework to improve outcomes. The methodology used includes a systematic review to ascertain current knowledge and retrospective quantitative analysis to identify current LNPCP management outcomes. The English Bowel Cancer Screening Programme (BCSP) which has a high volume of recorded LNPCP data was used to facilitate the latter process. In addition, qualitative analysis using consensus methodology to create best practice guidelines, key performance indicators (KPIs) to audit LNPCP outcomes and a complex polyp multidisciplinary team process was undertaken. The main outcomes of this thesis were: 1. Confirmation of variation in LNPCP management practices leading to variable outcomes 2. Formulation of evidence based and expert consensus LNPCP management guidelines 3. Identification of KPIs to allow audit of LNPCP management and outcomes 4. Identification of pertinent research questions to improve evidence LNPCP base 5. Development and pilot of regional complex polyp multidisciplinary team meetin

Advances in CAD/CAM/CAE Technologies

CAD/CAM/CAE technologies find more and more applications in today’s industries, e.g., in the automotive, aerospace, and naval sectors. These technologies increase the productivity of engineers and researchers to a great extent, while at the same time allowing their research activities to achieve higher levels of performance. A number of difficult-to-perform design and manufacturing processes can be simulated using more methodologies available, i.e., experimental work combined with statistical tools (regression analysis, analysis of variance, Taguchi methodology, deep learning), finite element analysis applied early enough at the design cycle, CAD-based tools for design optimizations, CAM-based tools for machining optimizations

Micro/Nano Manufacturing

Micro manufacturing involves dealing with the fabrication of structures in the size range of 0.1 to 1000 µm. The scope of nano manufacturing extends the size range of manufactured features to even smaller length scales—below 100 nm. A strict borderline between micro and nano manufacturing can hardly be drawn, such that both domains are treated as complementary and mutually beneficial within a closely interconnected scientific community. Both micro and nano manufacturing can be considered as important enablers for high-end products. This Special Issue of Applied Sciences is dedicated to recent advances in research and development within the field of micro and nano manufacturing. The included papers report recent findings and advances in manufacturing technologies for producing products with micro and nano scale features and structures as well as applications underpinned by the advances in these technologies

Case studies on productivity improvement and supplier selection

Two case studies have been reported (i) improvement of productivity in Electrical Discharge Machining (EDM) and (ii) Multi-Criteria Decision Making (MCDM) approach for supplier selection.Case study (i) highlights EDM of stainless steel in which best process environment (optimal) has been determined to satisfy productivity and quality requirements simultaneously. Material Removal Rate (MRR) during the process has been considered as productivity estimate with the aim to maximize it. Where as surface roughness i.e. (Ra value) of the machined surface has been chosen as surface quality estimate with the requirement to minimize it. These two contradicting requirements have been simultaneously satisfied by selecting an optimal process environment (optimal parameter setting). Desirability Function (DF) approach coupled with Taguchi method has been used to solve the problem.In case study (ii), usefulness of grey based MCDM approach method has been highlighted to solve multi-criteria decision making problem of supplier selection. The method has been found efficient to aggregate multiple attribute values into an equivalent single quality index (overall grey relation grade) which facilitates ranking/benchmarking as well as selection of the appropriate alternative supplie