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The optimum design of a cam mechanism is a very interesting problem in the contact mechanics today, due to the alternative industrial applications as a mechanism of precision. In this paper, a new evolutionary algorithm called modified adaptive differential evolution (MADE) is introduced for multi-objective optimization of a cam mechanism with offset translating roller follower. The optimization procedure is investigated for three objectives among them minimum congestion, maximum efficiency, and maximum strength resistance of the cam. To enhance the design quality of the mechanism in the optimization process, more geometric parameters and more design constraints are included in the problem formulation. In order to validate the developed algorithm, three engineering design problems are solved. The simulation results for the tested problems indicate the effectiveness and the robustness of the proposed algorithm compared to the various existed optimization methods. Finally, the optimal results obtained for the case study example provide very useful decisions for a cam mechanism synthesis. [ABSTRACT FROM AUTHOR]

In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimization problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumulative distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely 'closed' by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technological limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing operation; therefore, the economic objectives are minimized under imposed maximum probabilities of failure. In both situations, the efficiency and robustness of optimal solutions generated by the P-NSGA-II algorithm are analysed, discussed and compared with sequence of unconstrained minimization technique (SUMT) method. [ABSTRACT FROM AUTHOR]

The stochastic location-allocation p-hub median problems are related to long-term decisions made in risky situations. Due to the importance of this type of problems in real-world applications, the authors were motivated to propose an approach to obtain more reliable policies in stochastic environments considering the decision makers' preferences. Therefore, a systematic approach to make robust decisions for the single location-allocation p-hub median problem based on mean-variance theory and two-stage stochastic programming was developed. The approach involves three main phases, namely location modeling, risk modeling, and decision making, each including several steps. In the first phase, the pertinent location-allocation model of the problem is developed in the form of a two-stage stochastic model based on its deterministic version. A risk measure, based on total cost function and mean-variance theory, is derived in the second phase. Furthermore, two heterogeneous terms of the risk measure have been normalized and an innovative procedure has been proposed to significantly improve the calculation efficiency. In the third phase, the Pareto solution is obtained, the frontier curve is depicted to determine the decision maker's risk aversion coefficient, and a robust policy is obtained through optimization based on decision makers' preferences. Finally, a case study of an automobile part distribution system with stochastic demand is described to further illustrate our risk management and analysis approach. [ABSTRACT FROM AUTHOR]

Despite studies that consider service level rates of individual factories in a supply chain system, the interactions between connected factories and the impact of these interactions on the overall supply chain service level rate have been rarely studied. Moreover, due to uncertainties induced by various sources such as transportation delay and manufacturing processes variability, ensuring the robustness of system service level rate while considering these uncertainties in individual factories is a highly complex task. This paper studies uncertainty effect introduced by factory service level rates on the robustness of overall supply chain network performance, and presents a novel robust design optimization methodology to derive designs of factory service level rates in order to satisfy the service level rate requirement of the system and ensure its robustness. A case study with a multi-level multiple factories supply chain network is used to demonstrate the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Laser shock peening is an innovative surface treatment technique, which has been successfully applied to improve fatigue performance of metallic components. Laser shock peening improves the surface morphology and microstructure of the material. In this paper, three Nd3+:YAG laser process parameters (voltage, focus position and pulse duration) are varied in an experiment, in order to determine the optimal process parameters that could simultaneously meet the specifications for seven correlated responses of processed Nimonic 263 sheets. The modelling and optimisation of a process were performed using the advanced, problem-independent method. First, responses are expressed using Taguchi's quality loss function, followed by the application of multivariate statistical methods to uncorrelate and synthesise them into a single performance measure. Then, artificial neural networks are used to build the process model, and simulated annealing was utilised to find the optimal process parameters setting in a global continual space of solutions. Effectiveness of the proposed method in the development of a robust laser shock peening was proved in comparison to several commonly used approaches from the literature, resulting in the highest process performance measure, the most favourable response values and the corresponding process parameters optimum. Besides the improved surface characteristics, the optimised laser shock peening (LSP) showed an improvement in terms of microhardness and formation of favourable microstructural transformations. [ABSTRACT FROM AUTHOR]

In deep drawing process, the blank holder plays a key role in adjustment of metal flow into the die cavity. Moreover, the quality of drawn parts is extremely affected by this flow. There are two methods of treating the blank holder in deep drawing and its simulation. One is blank holder force (BHF) and the other is blank holder gap (BHG), defined as the fixed distance between the blank holder and the die surface. In previous studies, a large number of experimental techniques have been used to study BHF; however, the amount of theoretical and numerical simulation work to study BHG is insufficient. In the present study, the concept of BHG profile, i.e., variation of BHG over punch stroke is introduced and it is shown that a properly selected BHG profile can improve the section thickness of formed part and result in the drawing of deeper parts. Here, two methods for the optimization of BHG profile are devised, i.e., the local optimization and the global optimization methods. In the first approach, the best BHG in each punch step is determined and finally, the local optimized BHG profile is achieved. In the second method, however, the empirical model for the prediction of final minimum section thickness in terms of BHG profile is obtained using design of experiments and neural networks. In the next stage, the proposed model is implanted into a simulated annealing optimization procedure to identify a proper BHG profile that can produce the desired blank thickness. Afterward, the BHG profile approach is applied to a variety of initial thicknesses, blank diameters, and materials in order to examine the robustness of method. In this paper, ABAQUS finite element package is used to gather finite element (FE) data and several experiments are performed to verify the FE results. [ABSTRACT FROM AUTHOR]

Quality costing has become a key pillar for monitoring process efficiency while documenting product integrity. A robust technique for profiling economically complex industrial processes is proposed in this work. The presentation is focused on the performance evaluation of a single product response where data is collected by group and subgroup replication in order to account for uncertainty that may intrude in any phase of production. Data transformed by Taguchi's original quality loss functions are subjected to non-parametric analysis customized to handle highly dense non-linear orthogonal arrays. The economic design outlined in this article is capacitated by the data collection efficiency of popular fractional factorial designs and the data compression capabilities of rank sums for repetitive non-regular sampling schemes. The master rank ordering concept is introduced to actuate the non-parametric analysis of the converted quality loss cost data. The distribution-free character of the developments facilitates screening for prominent effects where perplexing issues such as compliance to normality and sparsity assumptions are waived. The overall approach is shown to be easy to implement and convenient to use. The technique is illustrated on a real study where four controlling factors are adjusted through an L(3) orthogonal array in order to rectify the packaging check-weighing operation of a pharmaceutical enterprise. Our proposed economic data mining diagnosticated that the input weight, the feed rate and the internal conversion rate were significant at a level of 0.10 with a three-way composite P statistic of 2,203. The input weight was found to be the preponderant effect at a significance level of 0.10 accumulating a sum of squared rank sums of 797. The optimal operating settings were found to be 50 g, 30 m/s and 19 counts/s for the input weight, feed rate and the internal conversion rate, respectively. [ABSTRACT FROM AUTHOR]

The present work introduces an expert system that automatically selects and designs rolling sequences for the production of square and round wires. The design strategy is aimed at reducing the overall number of passes assuming a series of process constraints, e.g., available roll cage power and torque, rolls groove filling behaviors, etc. The method is carried out into two steps: first a genetic algorithm is used to select the proper rolling sequence allowing to achieve a desired finished product; then, an optimization roll pass design tool is utilized for proper design of roll passes. Indeed, an artificial neural network (ANN) is utilized to predict the main geometrical characteristics of the rolled semi-finished product and technological requirements. The ANN was trained with a non-linear finite element (FE) model. The proposed methodology was applied to some industrial cases to show the validity of the proposed approach in terms of reduction of number of passes and search robustness. [ABSTRACT FROM AUTHOR]

Support structures are required in several additive manufacturing (AM) processes to sustain overhanging parts, in particular for the production of metal components. Supports are typically hollow or cellular structures to be removed after metallic AM, thus they represent a considerable waste in terms of material, energy and time employed for their construction and removal. This study presents a new approach to the design of support structures that optimise the part built orientation and the support cellular structure. This approach applies a new optimisation algorithm to use pure mathematical 3D implicit functions for the design and generation of the cellular support structures including graded supports. The implicit function approach for support structure design has been proved to be very versatile, as it allows geometries to be simply designed by pure mathematical expressions. This way, different cellular structures can be easily defined and optimised, in particular to have graded structures providing more robust support where the object's weight concentrate, and less support elsewhere. Evaluation of support optimisation for a complex shape geometry revealed that the new approach presented can achieve significant materials savings, thus increasing the sustainability and efficiency of metallic AM. [ABSTRACT FROM AUTHOR]

The present competitive market focuses on producing high-quality products with the lowest possible cost. To help accomplish this objective, various quality-enhanced tools have been put forward in recent years and, of these, process window approach (PWA) has emerged as perhaps the most viable and efficient technique for process quality improvement. The PWA is a powerful tool for quickly optimizing and confirming the quality and robustness of a process setup for any parameters. The work in this paper focuses on implementing a proposed PWA in order to optimize the sand-casting operation variables. In this research, the authors have kept their prime focus on minimizing the defects developed in the sand-casting process by PWA. Analysis of various critical process parameters and the interaction among them is carried out with the help of Taguchi method of experimental design. To optimize the results obtained and to make the analysis more precise and cost-effective, response surface methodology (RSM) is also incorporated. The optimized parameters obtained using the Taguchi method and RSM are then tested in an industrial case study. It is validated by proposed process window approach. [ABSTRACT FROM AUTHOR]

Thermal error compensation is becoming a cost-effective way to improve accuracy of machine tools especially with the increasing demand for machining accuracy in recent years. The compensation effectiveness mainly depends on the accuracy of the thermal error model. In order to explore the robustness, versatility, and prediction accuracy of the thermal error modeling, linear regression (LR) model, backpropagation (BP) network model, and radial basis function (RBF) network model are developed, analyzed, and compared. Experimental validation on a high precision four-axes machining center demonstrates that LR model has high prediction accuracy yet it has low robustness because the estimation of the regression coefficients and thermal key points are strongly correlated to the measurement data and their noise level. The neural network model is more adaptive to the case of different feed rates, rotational speeds, and ambient temperatures and has certain versatility on machine tools of the same type. The fitting accuracy and the prediction correctness of the BP network usually vary according to the hidden neurons, thresholds, and weights and cannot achieve the peak performance simultaneously. Experimental results show that the thermal error in Z axial direction could be reduced to as less as 25% of the original thermal error with compensation using the RBF model under the machining conditions of various feed rates and rotational speeds. It is also demonstrated that RBF model could improve the thermal precision in Z axle of the machine tool by about 65% under the distinct environmental temperature conditions. [ABSTRACT FROM AUTHOR]

In today's world of precision engineering, robustness and accuracy in the evaluation of the form tolerances are considered as competitive advantages for manufacturing enterprises. Amongst various methods for accurate and robust evaluation, which have been studied, nonlinear optimization methods based on operational research have proved to be successful as far as they can ensure unique and global convergence in practical applications. However, it is well known that ensuring the convergence is the most difficult thing to deal with for a nonlinear optimization technique because the performance is in general highly sensitive to parameter setting. Therefore, this paper introduces a robust linear programming formulation-based algorithm in which the performance is not dependent on the quality of parameters. Interestingly, in this algorithm, the data envelopment analysis technique is used to form a convex hull that decides the minimum enclosed zone in a robust manner. From the computational experiments, it is shown that the proposed algorithm can be a promising alternative to the traditional nonlinear optimization method for straightness and flatness evaluation. [ABSTRACT FROM AUTHOR]

This paper presents a generic optimisation methodology for the selection of optimal process parameters in multiresponse processes that consists of two stages: The first stage is based on a quality loss function and multivariate statistical methods in order to adequately present responses, uncorrelate and synthesise them into a single process performance measure; the second stage uses artificial neural networks to build a process model and a simulated annealing (SA) algorithm to find the optimal process parameter conditions. The initial point of the SA algorithm is generated in such a way as to improve the convergence to the actual global optimum. The most important SA algorithm parameters are varied to assess the algorithm's robustness in terms of the effect of the algorithm setting on the quality of the final solution (optimal process parameters and the corresponding process performance measure). The results are benchmarked to genetic algorithm (GA) performance within the proposed methodology, and the quality of a final solution, the robustness of the optimisation algorithm, the speed of a convergence to the optimum and the computational time are evaluated. Four case studies are presented to illustrate the effectiveness of the proposed methodology in comparison to several commonly used approaches from the literature and also to the GA-based performance. [ABSTRACT FROM AUTHOR]

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environment. A problem in traditional CAPP system is that the multiple planning tasks are treated in a linear approach. This leads to an over constrained overall solution space and the final solution is normally far from optimal or even non-feasible. The operation-sequencing problem in process planning is considered to produce a part with the objective of minimizing the sum of machine, setup and tool change costs. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem more difficult to solve. In this paper, the feasible sequences of operations are generated based on the precedence cost matrix and reward-penalty matrix using simulated annealing technique (SAT), a meta-heuristic. A number of benchmark case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. The proposed SAT integrates robustness, convergence and trapping out of local minima. [ABSTRACT FROM AUTHOR]

Robust design (RD) is an effective quality improvement methodology in which the goal is to determine the settings of design parameters that minimize the effect of uncontrollable variation on the response of interest, thereby creating a “robust” system. Although a number of approaches to RD have been proposed in the literature, little attention has been given to the use of RD in situations involving the consideration of uncontrollable factors in constrained experiments. As an alternative approach, we propose a new RD model by incorporating the concept of D-optimality to overcome the limitations of standard experimental approaches, while simultaneously considering controllable and uncontrollable factors. Numerical examples are used to demonstrate our proposed approach, and the results determined from our proposed model are compared to that of the traditional RD approach. [ABSTRACT FROM AUTHOR]

The well-known quality improvement methodology, robust design, is a powerful and cost-effective technique for building quality into the design of products and processes. Although several approaches to robust design have been proposed in the literature, little attention has been given to the development of a flexible robust design model. Specifically, flexibility is needed in order to consider multiple quality characteristics simultaneously, just as customers do when judging products, and to capture design preferences with a reasonable degree of accuracy. Physical programming, a relatively new optimization technique, is an effective tool that can be used to transform design preferences into specific weighted objectives. In this paper, we extend the basic concept of physical programming to robust design by establishing the links of experimental design and response surface methodology to address designers’ preferences in a multiresponse robust design paradigm. A numerical example is used to show the proposed procedure and the results obtained are validated through a sensitivity study. [ABSTRACT FROM AUTHOR]

Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The grinding of silicon carbide is not an easy task due to its low fracture toughness, therefore making the material sensitive to cracking. The efficient grinding involves the optimal selection of operating parameters to maximize the material removal rate ( MRR) while maintaining the required surface finish and limiting surface damage. In this work, optimization based on the available model has been carried out to obtain optimum parameters for silicon carbide grinding via particle swarm optimization (PSO) based on the objective of maximizing MRR with reference to surface finish and damage. Based on statistical analysis for various constraint values of surface roughness and number of flaws, simulation results obtained for this machining process for PSO are comparatively better to genetic algorithm (GA) approach. In addition, the post-optimal robustness of PSO has also been studied. From simulation results together with the proposed robustness measurement method, it has been shown that PSO is a convergent stable algorithm. [ABSTRACT FROM AUTHOR]

The Taguchi method is a useful technique to improve the performance of products or processes at a lower cost and in less time. The traditional Taguchi method focused on a single characteristic to optimize the parameter conditions. In practice, most products and processes have more than one quality characteristic. Several approaches dealing with multiple static quality characteristic problems have been reported. However, little attention has been focused on optimizing the multiple dynamic quality characteristics. This research presents an approach to optimizing multiple dynamic problems based on quality loss. The objective is to minimize the total average quality loss for the multiple dynamic quality characteristics experiment. [ABSTRACT FROM AUTHOR]