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This paper aims to present the design optimisation using genetic algorithm (GA) to achieve the highest strength to weight (S/W) ratio, for cold-formed steel residential roof truss.

The GA developed in this research simultaneously optimises roof pitch, truss configurations, joint coordinates and applied loading of typical dual-pitched symmetrical residential roof truss. The residential roof truss was considered with incremental uniform distributed loading, in both gravitational and uplift directions. The structural analyses of trusses were executed in this GA using finite element toolbox. The ultimate strength and serviceability of trusses were checked through the design formulation implemented in GA, according to the Australian standard, AS/NZS 4600 Cold-formed Steel Structures.

An optimum double-Fink roof truss which possess highest S/W ratio using GA was determined, with optimum roof pitch of 15°. The optimised roof truss is suitable for industrial application with its higher S/W ratio and cost-effectiveness. The combined methodology of multi-level optimisation and simultaneous optimisation developed in this research could determine optimum roof truss with consistent S/W ratio, although with huge GA search space.

The sizing of roof truss member is not optimised in this paper. Only single type of cold-formed steel section is used throughout the whole optimisation. The design of truss connection is not considered in this paper. The corresponding connection costs are not included in the proposed optimisation.

The optimum roof truss presented in this paper is suitable for industrial application with higher S/W ratio and lower cost, in either gravitational or uplift loading configurations.

This research demonstrates the approaches in combining multi-level optimisation and simultaneous optimisation to handle large number of variables and hence executed an efficient design optimisation. The GA designed in this research determines the optimum residential roof truss with highest S/W ratio, instead of lightest truss weight in previous studies.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

Layout optimization of structures aims to find the optimal topology and member sizes in an integrated manner. For this purpose, the most successful attempts have addressed the outstanding features of the genetic algorithms.

This paper utilizes a direct index coding (DIC) in a way that the optimization algorithm can simultaneously integrate topology and size in a minimal length chromosome in order to seek the true optimum in an efficient and reasonable manner. Proper genetic operators are adopted for this special kind of encoding together with some modifications in the topological mutation aiming to improve the convergence of the algorithm.

The present DIC, has the following features: enforcing one‐to‐one correspondence between discrete genotype space and the problems' phenotype space; avoiding any out‐of‐bound parameter addressing and limiting the GA search only to necessary genotypes; reduction in the size of genotype search space to increase the algorithm convergence and the possibility of leading to the global optimum; dealing with direct genetic operators so that the GA parameters can be purely controlled to tune the desired balance between convergence and escaping from local optima.

Employing direct index chromosome makes it possible to eliminate the additional topological bits in treated examples.

This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process.

Four components, namely, big data application, big data sensing and acquisition, big data processing and storage, model establishing, data mining and process optimization were presented to comprise the framework. Key technologies including the big data acquisition and integration, big data mining and knowledge sharing mechanism were developed for the big data analytics for AM.

The presented framework was demonstrated by an application scenario from a company of three-dimensional printing solutions. The results show that the proposed framework benefited customers, manufacturers, environment and even all aspects of manufacturing phase.

This study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering.

The proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process.

This paper introduces the concept of big data in the field of AM. The proposed framework can be used to make better decisions based on the big data during manufacturing process.

The purpose of this study is to investigate the optimization of design and energy management in a parallel hybrid-electric powertrain to replace the conventional engine of an existing tactical unmanned aerial vehicle (UAV) equipped with a Wankel engine with a pre-defined flight mission. The proposed powertrain can work in four different operating modes: electric, thermal, power-assist and charging.

The power request at propeller axis of each flight segment is used as input for an in-house model that calculates the overall fuel consumption throughout the mission (Mfuel) and the maximum payload weight (Wpay) by means of an average-point analysis. These outputs depend on the energy management strategy that is expressed by the power-split ratio between engine and electric phase (Uphase) of each mission phase, according to which the components of the hybrid system are sized. The in-house model is integrated into an optimization framework to find the optimal set of Uphase and battery size that minimizes Mfuel and maximizes Wpay.

It was found a 3.24% saving of the fuel mass burned throughout the mission (or, alternative an improvement of endurance by 4.3%) with about the same maximum-payload mass (+0.2%) of the original configuration, or a smaller fuel saving with +11% more payload. The fuel saving of 3.24% corresponds to −3.25% in total emissions of CO₂ and a 2.34% reduction of the cost-per-mission.

This study demonstrates that environmental advantages, even if limited, can be already obtained from optimal design and management of the hybrid power system with today technologies while waiting for further benefits from the introduction of advanced technologies for batteries and electric machines.

The main novelties are the design of the powertrain on the basis of the energy management and the application of scalability and hybridization to Wankel engines.

This paper addressed some critical issues in the development of hybrid electric powertrains for aircraft and propose a design methodology based on multi-objective optimization algorithms and mission-based simulations.

Scalable models were used for the main components of the powertrain, namely, the (two stroke diesel) engine, the (lithium) batteries and the (permanent magnet) motor. The optimization was performed with the NSGA-II genetic algorithm coupled with an in-house MATLAB tool. The input parameters were the size of engine, the hybridization degree and the specification of the battery (typology, nominal capacity, bus voltage, etc.). The outputs were electric endurance, additional volume, performance parameters and fuel consumption over a specified mission.

Electric endurance was below 30 min in the two test cases (unmanned aerial vehicles [UAVs]) but, thanks to the recharging of the batteries on-board, the total electric time was higher. Fuel consumption was very high for the largest UAV, while an improvement of 11 per cent with respect to a conventional configuration was obtained for the smallest one.

The research used a simplified approach for flight mechanics. Some components were not sized in the proposed test cases.

The results of the test cases stressed the importance of improving energy density and power density of the electric path.

The proposed methodology is aimed at minimizing the environmental impact of aircraft.

The proposed methodology was obtained from the automotive field with several original contributions to account for the aircraft application.

In this paper, the authors investigated a proposed radio-frequency identification (RFID)-based meat supply chain to monitor quality and safety of meat products we purchase from supermarkets. The supply chain consists of farms, abattoirs and retailers. The purpose of this paper is to determine a cost-effective trade-off decision obtained from a developed multi-criteria optimization model based on three objectives. These objectives include customer satisfaction in percentage of product quantity as requested by customers, product quality in numbers of meat products and the total implementation cost. Furthermore, this work was aimed at determining the number and locations of farms and abattoirs that should be established and quantities of products that need to be transported between entities of the proposed supply chain.

To this aim, a tri-criteria optimization model was developed. The considered criteria were used for minimizing the total implementation cost and maximizing customer satisfaction and product quality. In order to obtain Pareto solutions based on the developed model, four solution approaches were employed. Subsequently, a new decision-making algorithm was developed to select the superior solution approach in terms of values of the three criteria.

A case study was applied to examine the applicability of the developed model and the performance of the proposed solution approaches. The computational results proved the applicability of the developed model in obtaining a trade-off among the considered criteria and solving the RFID-based meat supply chain design problem.

The developed tri-criteria optimization model can be used by decision makers as an aid to design and optimize food supply chains.

This paper presents a development of first, a cost-effective optimization approach for a proposed RFID-based meat supply chain seeking a trade-off among three conflicting criteria; and second, a new decision-making algorithm which can be used for any multi-criteria problem to select the best Pareto solution.

The purpose of this paper is to propose an approach to determine the optimal parameters and tolerances in concurrent product and process design in the early design stages utilizing fuzzy goal programming. A wheelchair design is provided for illustration.

The product design is developed on the basis of both customer and functionality requirements. The critical product components are then determined. The design and analysis of experiments are performed by using simulation, and then the probability distributions are adopted to determine the values of desired responses under each combination of critical product parameters and tolerances. Regression nonlinear models are then developed and inserted as constraints in the complete optimization model. Preferences on product specifications and process settings, as well as process capability index ranges, are also set as model constraints. The combined objective functions are finally formulated to minimize the sum of positive and negative deviations from desired targets and maximize process capability. The optimization model is applied to determine the optimal wheelchair design.

The results showed that the proposed approach is effective in determining the optimal values of the design parameters and tolerances of the critical components of the wheelchair with their related process means and standard deviations that enhance desired multiple quality responses under uncertainty.

This work provides a general methodology that can be applied for concurrent optimization of product design and process design in a wide range of business applications. Moreover, the methodology is beneficial when uncertainty exists in quality responses and the parameters and tolerances of product design and its critical processes.

The fuzziness is rarely considered in research and development stage. This research considers membership functions for parameters and tolerances of a product and its related processes rather than crisp values. Moreover, presented optimization model considers multiple objective functions, sum of deviations and process capability. Finally, the indirect quality responses are calculated from the best-fit probability distributions rather than assuming a normal distribution.

Magnesium alloys are becoming prominent as an alternative to the permanent biomedical implants. In present work, electric discharge drilling (EDD) process has been investigated and optimized for ZM21 Mg alloy that can be used for producing perforated bone implants having geometrically precise micro holes.

Planning of experiments has been carried out in accordance to the Taguchi mixed L18 orthogonal array (OA). The hole overcut (HO), circularity at entrance (Cent) and circularity at exit (Cext) of drilled micro holes were measured as response characteristics during experimentation corresponding to different settings of EDD input parameters. For optimizing multiresponse characteristics, the hybrid approach of grey relational analysis, regression analysis and particle swarm optimization has been implemented.

It is found from hybrid approach that brass electrode along with Ip; 3 Amp, Ton; 50 µs and Toff; 52 µs outperformed over all other parametric settings against the collective result of response characteristics. The experimental values of response characteristics at suggested optimized setting are HO: 93.48 µm; Cent: 0.988 and Cext: 0.992, respectively.

The optimization of EDD process for developing perforated Mg alloy bone implants, using hybrid approach is still missing.