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This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

The effects of rotor blade design variables and their mutual interactions on aerodynamic efficiency of helicopters are investigated. The aerodynamic efficiency is defined based on figure of merit (FM) and lift-to-drag responses developed for hover and forward flight, respectively.

The approach is to couple a general flight dynamic simulation code, previously validated in the time domain, with design of experiment (DOE) required for the response surface development. DOE includes I-optimality criteria to preselect the data and improve data acquisition process. Desirability approach is also implemented for a better understanding of the optimum rotor blade planform in both hover and forward flight.

The resulting system provides a systematic manner to examine the rotor blade design variables and their interactions, thus reducing the time and cost of designing rotor blades. The obtained results show that the blade taper ratio of 0.3, the point of taper initiation of about 0.64 R within a SC1095R8 airfoil satisfy the maximum FM of 0.73 and the maximum lift-to-drag ratio of about 5.5 in hover and forward flight.

The work shows the practical possibility to implement the proposed optimization process that can be used for the advanced rotor blade design.

The work presents the rapid and reliable optimization process efficiently used for designing advanced rotor blades in hover and forward flight.

The aim of this paper is to obtain the analytical solution for the optimal design of reinforced concrete sections under ultimate design. The equilibrium equations of the section under bending moment and axial force in rupture are derived. The ultimate conditions are considered either in the steel or in the concrete according to the concrete design codes. The definition of the strains and stresses in the materials is based on the use of Heaviside functions. With this definition the equilibrium equations are described by unique equations. The optimization can then be developed with any design variables in the geometric definition, as area of the reinforcement and location. The optimization is developed with yielding of tensile steel and crushing of concrete. Although this is the current situation in reinforced concrete design, future developments of the model can include other steel and concrete conditions. Cost optimization and variable materials strength ratio are possible applications of the model. The interest of the model is the use of closed form unique equilibrium equations in the optimization of reinforced concrete sections. Numerical examples of the optimization of a rectangular section with minimum reinforcing steel area and economic bending moment are presented. The originality of the paper is the use of Heaviside functions in the definition of the ultimate strains in the reinforced concrete section. Unique equations for the objective function and restrictions are derived. The paper is useful for the design of reinforced concrete. The equations derived can be implemented into computer programs.

Analytical target cascading (ATC) is a hierarchical multi‐level design methodology. According to the state‐of‐the‐art, it is confirmed that for problems with unattainable targets, strict design consistency cannot be achieved with finite weighting factors. This paper aims to address these issues.

A new formulation is proposed to improve the ATC convergence. The weighted sum of deviation metric is transformed into a multi‐objective formulation. An original optimization problem with a single global optimal solution is used as a benchmark.

It is found that carrying out an industrial application to design optimally a tram traction system demonstrates the efficiency of the proposed solution.

This paper is of value in showing how to improve the convergence of a multi‐level optimization algorithm by best management of the consistency constraints.

The purpose of this paper is to apply some surrogate-assisted optimization techniques in order to improve the performances of a five-phase permanent magnet machine in the context of a complex model requiring computation time.

An optimal control of four independent currents is proposed in order to minimize the total losses with the respect of functioning constraints. Moreover, some geometrical parameters are added to the optimization process allowing a co-design between control and dimensioning.

The optimization results prove the remarkable effect of using the freedom degree offered by a five-phase structure on iron and magnets losses. The performances of the five-phase machine with concentrated windings are notably improved at high speed (16,000 rpm).

The effectiveness of the method allows solving the challenge which consists in taking into account inside the control strategy the eddy-current losses in magnets and iron. In fact, magnet losses are a critical point to protect the machine from demagnetization in flux-weakening region.

Companies are increasingly bringing personnel together into teams from different countries, physically and/or electronically, to develop products for multiple or worldwide markets. Called global new product teams (GNPTs), these groups face significant challenges, including cultural diversity. Differing cultural values can lead to conflict, misunderstanding, and inefficient work styles on the one hand, and strong idea generation and creative problem solving on the other. A study was conducted to identify team compositions that would optimize the effects of national culture so that product development outcomes are favorable. This began by developing a theoretical framework describing the impact of national culture on product development tasks. The framework was then translated into several mathematical models using analytical derivations and comparative statics. The models identify the levels and variances of culture values that maximize product development success by simultaneously considering four relevant dimensions of GNPT performance. Next, the utility of these models was tested by means of numerical simulations for a range of team scenarios. Concludes by drawing implications of the findings for managers and researchers.

Fused deposition modelling (FDM) is the most economical additive manufacturing technique. The purpose of this paper is to describe a detailed review of this technique. Total 211 research papers published during the past 26 years, that is, from the year 1994 to 2019 are critically reviewed. Based on the literature review, research gaps are identified and the scope for future work is discussed.

Literature review in the domain of FDM is categorized into five sections – (i) process parameter optimization, (ii) environmental factors affecting the quality of printed parts, (iii) post-production finishing techniques to improve quality of parts, (iv) numerical simulation of process and (iv) recent advances in FDM. Summary of major research work in FDM is presented in tabular form.

Based on literature review, research gaps are identified and scope of future work in FDM along with roadmap is discussed.

In the present paper, literature related to chemical, electric and magnetic properties of FDM parts made up of various filament feedstock materials is not reviewed.

This is a comprehensive literature review in the domain of FDM focused on identifying the direction for future work to enhance the acceptability of FDM printed parts in industries.

In real-world cases, it is common to encounter mixed discrete-continuous problems where some or all of the variables may take only discrete values. To solve these non-linear optimization problems, the use of finite element methods is very time-consuming. The purpose of this study is to investigate the efficiency of the proposed hybrid algorithms for the mixed discrete-continuous optimization and compare it with the performance of genetic algorithms (GAs).

In this paper, the enhanced multipoint approximation method (MAM) is used to reduce the original nonlinear optimization problem to a sequence of approximations. Then, the sequential quadratic programing technique is applied to find the continuous solution. Following that, the implementation of discrete capability into the MAM is developed to solve the mixed discrete-continuous optimization problems.

The efficiency and rate of convergence of the developed hybrid algorithms outperforming GA are examined by six detailed case studies in the ten-bar planar truss problem, and the superiority of the Hooke–Jeeves assisted MAM algorithm over the other two hybrid algorithms and GAs is concluded.

The authors propose three efficient hybrid algorithms, the rounding-off, the coordinate search and the Hooke–Jeeves search-assisted MAMs, to solve nonlinear mixed discrete-continuous optimization problems. Implementations include the development of new procedures for sampling discrete points, the modification of the trust region adaptation strategy and strategies for solving mix optimization problems. To improve the efficiency and effectiveness of metamodel construction, regressors f defined in this paper can have the form in common with the empirical formulation of the problems in many engineering subjects.

This study responds to calls from industry and the literature to cope with the enormous challenges faced by companies operating in competitive business sectors. The main objective of this paper is to investigate how managers can optimise product quality and process efficiency of complex systems.

In this paper, a design of experiments (DoE) method was used to improve the development of complex products and manufacturing processes in the industry of automotive audio components. To identify the optimal combination that minimises quality problems occurring with subwoofer speakers in the marketing, this study proposed a full Factorial experiment 24 with three replications in a single block summarised in an analysis of the interaction among the factors.

The research findings revealed the factors and levels regarding both the product development and manufacturing processes that significantly impact the quality and reliability performance of the subwoofer speaker analysed. The findings from the article allowed the company to prioritise internal improvements to enhance product quality and process efficiency. Other automotive firms will benefit from the research findings obtained.

From a managerial perspective, this research presented the DoE methodology as a real opportunity to deal with the inherent complexity of the manufacturing process in the automotive audio components sector. This research assist managers with insights into how they can improve the quality performance in production systems and in the market.

This study is an original contribution to the advance of theory and empirical implementation of DoE in competitive industrial sectors.

The purpose of this paper is to develop an exponentially weighted moving average (EWMA) control chart based on the Wilcoxon rank-sum (WRS) statistic using repetitive sampling to improve the sensitivity of the EWMA control chart to process mean shifts regardless of the prior knowledge of the underlying process distribution.

The proposed chart is developed without any distributional assumption of the underlying quality process for monitoring the location parameter. The authors developed formulae as well as algorithms to facilitate the design and implementation of the proposed chart. The performance of the proposed chart is investigated in terms of the average run-length, standard deviation of the run-length (RL), average sample size and percentiles of the RL distribution. Numerical examples are given as illustration of the design and implementation of the proposed chart.

The proposed control chart presents very attractive RL properties and outperforms the existing nonparametric EWMA control chart based on the WRS in the detection of the mean process shifts in many situations. However, the performance of the proposed chart relatively deteriorates for small phase I sample sizes.

This study develops a new control chart for monitoring the process mean using a two-sample test regardless of the nature of the underlying process distribution. The proposed control chart does not require any assumption on the type (or nature) of the process distribution. It requires a small number of subgroups in order to reach stability in the phase II performance.