Ultra-high precision grinding of BK7 glass

With the increase in the application of ultra-precision manufactured parts and the absence of much participation of researchers in ultra-high precision grinding of optical glasses which has a high rate of demand in the industries, it becomes imperative to garner a full understanding of the production of these precision optics using the above-listed technology. Single point inclined axes grinding configuration and Box-Behnken experimental design was developed and applied to the ultra-high precision grinding of BK7 glass. A high sampling acoustic emission monitoring system was implemented to monitor the process. The research tends to monitor the ultra-high precision grinding of BK7 glass using acoustic emission which has proven to be an effective sensing technique to monitor grinding processes. Response surface methodology was adopted to analyze the effect of the interaction between the machining parameters: feed, speed, depth of cut and the generated surface roughness. Furthermore, back propagation Artificial Neural Network was also implemented through careful feature extraction and selection process. The proposed models are aimed at creating a database guide to the ultra-high precision grinding of precision optics

Neural networks approach towards determining Flax-Biocomposites composition and processing parameters

This research introduces neural networks (NN) as a novel approach towards aiding biocomposite materials processing. At its core, the aim of the research was to investigate NN usage as a tool for advancing the field of biocomposites. Empirical data was generated for compression-molded flax fiber and High Density Polyethylene (HDPE) matrix based biocomposite materials. In an attempt to create the NN model, tensile strength, impact strength, hardness, bending strength, and density were provided to the NN as inputs. These inputs were processed through multiple layers of the NN, and contributed to the prediction of the composition (fiber loading percentage) and operating parameter (pressure in MPa) as output. In précis, NN’s use was investigated to predict composition and operational parameter for biocomposites production when the desired mechanical properties of the biocomposites were available. Flax (Linum usitatissimum) fiber biocomposite boards were manufactured using chemically pretreated flax fiber and high density polyethylene (HDPE). After extensive preprocessing (combing and size reduction to 2 mm particles) and pretreatment regimen - flax fiber was mixed with HDPE and extruded using a laboratory scale single screw extruder. Extrudates generated from the extruder were again ground to 2 mm particles. Ground extrudates from different sample sets were exposed to a compression molding unit. The mold was put under two sets of pressures, (variable operating parameters) for all individual fiber loading. These boards were used to determine the mechanical properties tensile force, impact force, hardness, bending, and density. For verification and analysis of the mechanical properties, Microsoft Office Excel and a statistical software package SAS were used. After verification five different multilayer neural networks, i.e., cascade forward neural network, feedforward backpropagation neural network, neural unit (single layer, single neuron), feedforward time delay neural network and NARX, were trained and evaluated for performance. Ultimately, the feedforward backpropagation NN (FFBPNN) was selected as the most efficient. After rigorous testing, the FFBPNN trained by the TRAINSCG algorithm (Matlab ®) was selected to generate prediction results that were the most suitable, fast and accurate. Once the selection and training of the NN architecture was complete, biocomposite materials prediction was performed. From 9 separate input sets, NNs provided overall prediction error between 2 - and 4%. This was the same amount of error that was observed in the training of the neural network. It was concluded that the neural network approach for the experimental design and operational conditions were satisfied

An energy consumption estimation method for the tool setting process in CNC milling based on the modular arrangement of predetermined time standards

Modeling and estimating the energy consumption of computer numerical control (CNC) milling systems have been recognized as essential ways to realize lean energy consumption management and improve energy efficiency performance. As the preparatory phase, considerable time and energy are consumed in the tool setting process. However, research on the tool setting process mainly focuses on accuracy and operational efficiency, and the energy consumption is usually ignored or simplified. Accurately estimating the energy consumption of the tool setting process is thus indispensable for reducing the energy consumption of CNC milling systems and improving their energy efficiency. To bridge this gap, an energy consumption estimation method for the tool setting process in CNC milling based on the modular arrangement of predetermined time standards (MODAPTS) is presented. It includes three steps: (i) operations decomposition and determination of the MODAPTS codes for the tool setting process, (ii) power modeling of the basic action elements of the machine tool, and (iii) energy consumption modeling of the tool setting process. Finally, a case study was conducted to illustrate the practicability of the proposed method via energy consumption modeling of the tool setting process using an XH714D CNC machine center with a square workpiece, in which the estimation values of the operating time and the energy consumption for the tool setting process were 210.786 s and 140,681.68 J, respectively. The proposed method can increase the transparency of energy consumption and help establish labor-hour quotas and energy consumption allowances in the tool setting process

Additive Manufacturing Research and Applications

This Special Issue book covers a wide scope in the research field of 3D-printing, including: the use of 3D printing in system design; AM with binding jetting; powder manufacturing technologies in 3D printing; fatigue performance of additively manufactured metals, such as the Ti-6Al-4V alloy; 3D-printing methods with metallic powder and a laser-based 3D printer; 3D-printed custom-made implants; laser-directed energy deposition (LDED) process of TiC-TMC coatings; Wire Arc Additive Manufacturing; cranial implant fabrication without supports in electron beam melting (EBM) additive manufacturing; the influence of material properties and characteristics in laser powder bed fusion; Design For Additive Manufacturing (DFAM); porosity evaluation of additively manufactured parts; fabrication of coatings by laser additive manufacturing; laser powder bed fusion additive manufacturing; plasma metal deposition (PMD); as-metal-arc (GMA) additive manufacturing process; and spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning

Synthesis of Biodiesel from rubber seed oil for internal compression ignition engine

ABSTRACT Biodiesel has been identified as a good complement and plausible replacement of fossil diesel because of the overwhelming characteristic properties similar to fossil diesel in addition to its good lubricity, biodegradability, non-toxicity and eco-friendliness when used in diesel engines. The production of biodiesel from edible vegetable oils competes with food sources, thereby resulting in high cost of food and biodiesel. Studies have shown that rubber seed contains 35 45 % oil, which portrays a better competitor to other non-edible oil bearing plants in biodiesel production. In this study, non-edible vegetable oils from underutilized Nigerian NIG800 clonal rubber seeds were extracted from 0.5 mm kernel particle size using n-hexane as solvent to obtain a yield of 43 wt.% over an extraction time of 1 h. The oil was characterized for fatty acids by using gas chromatography-mass spectrometry (GC-MS), and for structural properties by Fourier transform-infrared (FT-IR) and nuclear magnetic resonance (NMR) analyses. The optimization of the process conditions of the vegetable oil extraction was evaluated using response surface methodology (RSM) and artificial neural network (ANN) techniques both of which, were based on a statistically designed experimentation via the Box-Behnken design (BBD). A three-level, three-factor BBD was employed using rubber seed powder (X1), volume of n-hexane (X2) and extraction time (X3) as process variables. The RSM model predicted optimal oil yield of 42.98 wt. % at conditions of X1 (60 g), X2 (250 mL) and X3 (45 min) and experimentally validated as 42.64 wt. %. The ANN model predicted optimal oil yield of 43 wt. % at conditions of X1 (40 g), X2 (202 mL) and X3 (49.99 min) and validated as 42.96 wt. %. Both models were effective in describing the parametric effect of the considered operating variables on the extraction of oil from the rubber seeds. On further examinations of the potentials of the vegetable oil, the kinetics of thermo-oxidative degradation of the oil was investigated. The kinetics produced a first-order reaction, with activation energy of 13.07 kJ/mol within the temperature range of 100 250 oC. In a bid to attain enhanced yield of biodiesel produced via heterogeneous catalysis, coupled with the carbonaceous potentials of the pericarp and mesocarp of rubber seed shell casing as a suitable catalytic material, the rubber seed shells (RSS) were used to develop a heterogeneous catalyst. RSS was washed 3 4 times with hot distilled water, dried at 110 oC for 5 h, ground to powder, and calcined at 800 oC at a heating rate of 10 oC/min as a catalyst and analyzed for thermal, structural, and textural properties using thermogravimetric analyzer, x-ray diffractometer, and nitrogen adsorption/desorption analyzer, respectively. The catalyst was further analyzed for elemental compositions and surface morphology by x-ray fluorescence and scanning electron microscopy, respectively. The catalyst was then applied in biodiesel production from rubber seed oil. A central composite design (CCD) was employed together with RSM and ANN to obtain optimal conditions of the process variables consisting of reaction time, methanol/oil ratio, and catalyst loading on biodiesel yield. The optimum conditions obtained using RSM were as follows: reaction time (60 min), methanol/oil ratio (0.20 vol/vol), and catalyst loading (2.5 g) with biodiesel yield of 83.11% which was validated experimentally as 83.06 0.013%. Whereas, those obtained via ANN were reaction time (56.7 min), methanol/oil ratio (0.21 vol/vol), and catalyst loading (2.2 g) with a biodiesel yield of 85.07%, which was validated experimentally as 85.03 0.013%. The characterized biodiesel complied with ASTM D 6751 and EN 14214 biodiesel standards and was used in modern diesel test engine without technical modifications. Though the produced biodiesel has a lower energy content compared with conventional diesel fuel, in all the cases of blends considered, the optimal engine speed for higher performance and lower emissions was observed at 2500 rpm. In this study, the B20 blend has best engine performance with a lower emission profile, and was closely followed by B50 blend.EM201

Proceeding Of Mechanical Engineering Research Day 2016 (MERD’16)

This Open Access e-Proceeding contains a compilation of 105 selected papers from the Mechanical Engineering Research Day 2016 (MERD’16) event, which is held in Kampus Teknologi, Universiti Teknikal Malaysia Melaka (UTeM) - Melaka, Malaysia, on 31 March 2016. The theme chosen for this event is ‘IDEA. INSPIRE. INNOVATE’. It was gratifying to all of us when the response for MERD’16 is overwhelming as the technical committees received more than 200 submissions from various areas of mechanical engineering. After a peer-review process, the editors have accepted 105 papers for the e-proceeding that cover 7 main themes. This open access e-Proceeding can be viewed or downloaded at www3.utem.edu.my/care/proceedings. We hope that these proceeding will serve as a valuable reference for researchers. With the large number of submissions from the researchers in other faculties, the event has achieved its main objective which is to bring together educators, researchers and practitioners to share their findings and perhaps sustaining the research culture in the university. The topics of MERD’16 are based on a combination of fundamental researches, advanced research methodologies and application technologies. As the editor-in-chief, we would like to express our gratitude to the editorial board and fellow review members for their tireless effort in compiling and reviewing the selected papers for this proceeding. We would also like to extend our great appreciation to the members of the Publication Committee and Secretariat for their excellent cooperation in preparing the proceeding of MERD’16

Neural Networks for cost estimating in project management

This thesis considers the application of neural networks in cost estimating in project management and whether they lead to more accurate estimates. It strikes two areas of research, namely neural networks and project management; an introductory chapter on both subjects is included. The statistical problem of parametric cost estimating is described and an explanation of the general principles is given. The Multi-Layer Perceptron with the Backpropagation learning algorithm is determined to be the most appropriate network and a selection of available software programs is reviewed. A Multi-Layer Perceptron neural model is used to determine one of the most important cost estimating relationships of the PRICE model. A comparison of the outputs of the neural network and the PRICE model shows that the Backpropagation algorithm is able to find the underlying estimating relationships used by PRJCE. To investigate whether other underlying functions can be found with artificial intelligence methods, other input parameters are selected and the costs generated by the PRICE model and by the neural network are compared with each other. Further experiments were undertaken in order to improve the performance of the neural network. The neural networks were applied to real data. and their output compared with the PRICE model. The processes of achieving better results are analogous to those used for the artificial data. A neural network was created which performs better than the PRICE model in terms of the accuracy of the estimates produced. The results are discussed and the collection of significant and accurate information and then deciding on which type of network is the best network to be used are identified as the major problems in the application of artificial intelligence for cost estimation in project management. The limitations and restrictions of the implementation of neural networks are examined and the scope and topics of further research are suggested

Design and Management of Manufacturing Systems

Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

Development of Life Cycle Assessment Based Air Quality Modeling and Decision Support System for the Mining Industry

Air quality in mining region has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanism. Mining operations emit various types of pollutants which could violate the environmental guidelines. The development of an integrated approach is conceptualized in this thesis as life cycle assessment based air quality modeling system (LCAQMS) for the mining industry. LCAQMS consists of four primary models which are: (1) life cycle inventory model, (2) artificial neural network model, (3) mining-zone air dispersion model, and (4) decision analysis model. A graphical user interface (GUI) is built to integrate primary models to understand the pollutant’s fate from its generation (emission inventory) to its management (control decisions). The life cycle inventory (LCI) model is developed to determine emission inventory using inverse matrix method, and defined characterization methods are investigated to assess the environmental impact. Artificial neural network model is developed to analyze carbon footprints (CO2 equivalent) using backpropagation method. Mining-zone air dispersion model (MADM) is developed to generate the predicted concentration of air pollutants at various receptor levels by considering the deposition effect. The meteorological factors based on atmospheric stability conditions are determined by employing the Pasquill-Turner method (PTM). The decision analysis model comprises multi-criteria decision analysis (MCDA) method and air pollution control model (APCM) to provide air pollution control alternatives and optimize the cost-effective solutions, respectively. Monte Carlo simulation accomplishes the uncertainties in the system. Moreover, an environmental risk assessment (ERA) method is extended by integrating the APCM with a fuzzy set. The applicability of LCAQMS is explored through three different case studies of open-pit metal mining in North America. Inventory results first show the air emission load for each mining activity and allow to evaluate the emission impact by linking the inventory to each impact category. Then this study helps to quantify the carbon footprints for the gold and copper mines. Also, prediction of significant pollutants such as PM10, PM2.5, SO2, and NOx at ground level has been calculated. The results depict that dry deposition is a dominate physical removal mechanism in the mining area. The LCAQMS results are evaluated with the monitoring field values, particularly MADM results are statistically tested against California puff (CALPUFF) model. Additionally, atmospheric stability is examined by analyzing the relationship between modeled PM2.5 concentrations and mixing height based on seasonal variation and the diurnal cycle. In conclusion, LCAQMS can serve as a useful tool for the stakeholders to assess the impact, predict the air quality, and aid planners to minimize the pollutants at a marginal cost by suggesting control pollution techniques

Avian muscle development and growth mechanisms: association with muscle myopathies and meat quality Volume II

open2siGiven the significant interest in Volume I, it was decided to launch Volume II of the Research Topic “Avian Muscle Development and Growth Mechanisms: Association With Muscle Myopathies and Meat Quality.” The broiler industry is still facing an unsustainable occurrence of growth-related muscular abnormalities that mainly affect fast-growing genotypes selected for high growth rate and breast yield. From their onset, research interest in these issues continues as proven by the temporal trend of published papers during the past decade (Figure 1). Even if meat affected by white striping, wooden breast, and spaghetti meat abnormalities is not harmful for human nutrition, these conditions impair quality traits of both raw and processed meat products causing severe economic losses in the poultry industry worldwide (Petracci et al., 2019; Velleman, 2019). Since the Research Topic of “Avian Muscle Development and Growth Mechanisms: Association With Muscle Myopathies and Meat Quality” is quite diverse, contributions in this second volume reflect the broad scope of areas of investigation related to muscle growth and development with 11 original research papers and one mini-review from prominent scientists in the sector. We hope that this collection will instigate novel questions in the minds of our readers and will be helpful in facilitating the development of the field.openMassimiliano Petracci; Sandra G. VellemanMassimiliano Petracci; Sandra G. Vellema