Application of response surface methodology for predicting bend force during air bending process in interstitial free steel sheet

In this work, bend force has been modeled using response surface methodology for air bending operation of interstitial free steel sheet. The process parameters considered in this investigation are punch travel (d), strain hardening exponent (n), punch radius (r), punch velocity (v), and width of the sheet (w). The experimental plan was based on the central composite design. The model results are in satisfactory agreement with the experimental results. In addition, an analysis for the effect of the individual input bending parameters on the response has been carried out and presented in this study. This study shows that the punch travel is the dominant factor determining the bend force followed by punch velocity and punch radius. The interaction effects of punch travel and punch radius are considerably significant.     

A novel robot arm selection methodology based on axiomatic design principles

Many companies intend to utilize the robotic systems to improve the performance of their manufacturing systems. Since the robotic systems are complex, it is required to determine the most suitable robot arm at the beginning of the complete design process. However, due to the increase in the number of robot arm alternatives and existence of the multiple and conflicting criteria, it has become hard to the decision makers to select the appropriate robot arm for a production system. Although the traditional multiple criteria of decision-making techniques were heavily employed in the past for this problem, they were based on subjective judgments for both the alternatives and the criteria. Therefore, a methodology based on Axiomatic Design principles is proposed to help the decision maker decide the most appropriate robot arm on a scientific, systematic, and objective basis. Moreover, the proposed methodology is extended into a decision support system (DSS) through MATLAB software, to evaluate more alternatives rapidly. Both the proposed methodology and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were applied to solve a small problem, so that the techniques will be compared and the utility of the proposed approach will be revealed. Besides, the proposed DSS was applied to a real robot arm selection problem of a food manufacturing system to show its performance in evaluating several alternatives and choosing the most suitable one in an objective and quick manner.     

Robust weighting applied to optimization of AISI H13 hardened-steel turning process with ceramic wiper tool: A diversity-based approach

A mixed ceramic is a type of cutting-tool material widely employed for machining hardened steels. The usage of a mixed ceramic along with a wiper geometry can help double the feed rate, thereby increasing productivity while keeping the surface roughness (Ra) as low as possible. Analyses of manufacturing processes, such as a machining process, show that the various possible controlled parameters can be modeled by multiobjective mathematical models to ensure their optimization. Hence, the aim of this study was optimize a hard turning process using a robust weighting based on diversity to choose the final Pareto optimal solution of the multiobjective problem. The responses of the material removal rate (MRR), Ra parameter, and cutting force (Fc) were modeled by using the response surface methodology; in this methodology, decision variables, such as the cutting speed (Vc), feed rate (f), and depth of cut (d), are employed. The diversity index, as a decision-making criterion, proved to be useful in mapping the regions of minimum variance within the Pareto optimal responses obtained in the optimization process. Hence, the study demonstrates that the weights used in the multiobjective optimization process influence the prediction variance of the obtained response.     

Estimating the effect of process parameters on MRR, TWR and radial overcut of EDMed AISI D2 tool steel by RSM and GRA coupled with PCA

This paper investigates an optimisation design of the various machining parameters for the electrical discharge machining (EDM) processes on AISI D2 tool steel using a hybrid optimisation method. A new combination of response surface methodology (RSM) and grey relational analysis coupled with principal component analysis (PCA) has been proposed to evaluate and estimate the effect of machining parameters on the responses. The major responses selected for this analysis are material removal rate, tool wear rate and radial overcut or gap, and the corresponding machining parameters considered for this study were pulse current (Ip), pulse duration (Ton), duty cycle (Tau) and discharge voltage (V). Thirty experiments were conducted on AISI D2 steel workpiece materials based on a face-centred central composite design. The experimental results obtained were used in grey relational analysis, and the weights of the responses were determined by the PCA and further evaluated using RSM. The results indicate that the grey relational grade (GRG) was significantly affected by the machining parameters considered and some of their interactions. The $R^2$ value for the GRG model was found to be 0.83, and the optimal parameter setting was determined for the grey relational grades. The analysis of variance results reveal that Tau is the most influencing parameter having 28.57 of percentage contribution followed by Ip, V and Ton with 11.52, 5.89 and 5.83 %, respectively. The interaction of the parameters contributes 31.19 % of percentage contribution. These results provide useful information on how to control the machining parameters and thereby responses and ensure high productivity and accuracy of the EDMed component. This method is simple with easy operability, and the results have also been verified by running confirmation tests.     

Navigation-oriented design for in-pipe robot in recursively divided sampling space with rapidly exploring random tree

Gas pipelines are subject to periodic inspection and maintenance for safety and longevity. Many robotic inspection systems have been developed for in-pipe applications, but systematic geometric design methodology that is suitable for in-pipe navigation has not been well studied so far due to difficulties in predicting the capability of maneuvering through the obstacles inside of pipelines such as bend, miter, and T-branch joint. The geometric design of the robot is critical to the performance of such in-pipe robots because the actuation and the measurement are constrained by the shape and the size of the robot. In this paper, we propose a design methodology that finds the maximum value of geometric design parameters of the robot with recursive evaluation of the parameter values in the design parameter space. The role of the design space division is to reduce the search region and to increase the number of parametric samples to near optimal values. As a parameter evaluation method, we adapt Rapidly exploring random tree (RRT) because it is known to be suitable for solving narrow passage problems for high-dimensional systems. Our design method makes it possible to find an optimal parameter set without computing complex cost functions. The design result of the in-pipe robot is 8 % larger than that of a heuristic geometry-based approach in three-parameter design problem.

     

Thermal-error modeling for complex physical systems: the-state-of-arts review

Flexibility and responsiveness to customer demands are very important for success. Generally additional time is needed for setup caused by poor design of equipment. At this point the terms continuous process improvement and SMED (single minute exchange of dies) as an approach of lean manufacturing come into play. A lean manufacturing system is part of corporate culture, like tools and approaches. In this research work, the process capability analysis technique is implemented by using MINITAB14 software to investigate the relation between SMED methodology and equipment design. The index C_pk has been used in this application study to provide a quantitative measurement of the equipment design by applying the SMED methodology in automobile manufacturing. The results of this research study indicated that SMED in other words “quick changeover” is still a suitable method not only for manufacturing improvement but also for equipment/die design development.     

Effect of ultrasonic irradiations on gas–liquid mass transfer coefficient (kLa); Experiments and modelling

Sonochemical reactors have proven to be very useful for intensification of various reaction systems. However, there is a lack of understanding in mass transfer mechanism under ultrasonication due to dependency of mass transfer on various parameters. The present work aims at investigating the effect of ultrasonic intensity on volumetric gas–liquid mass transfer coefficient, k_La, as a function of gas flow rate and temperature. Response surface methodology (RSM) coupled with central composite design (CCD) was used for design, statistical analysis and evaluation of the interaction between operational parameters. The maximum value of k_La was found to be 0.0128 s⁻¹ in an optimum range of ultrasonic intensity between 320 and 360 W, though gas flow rate was the most influential parameter for k_La. In the next part of the study, a model was developed based on ANFIS to map the input variables to the outputs. Satisfactory agreement was observed between the ANFIS predictions and experimental data.     

GRAI-IDEF0-Merise (GIM): Integrated methodology to analyse and design manufacturing systems

This paper presents an integrated methodology for the analysis and design of manufacturing systems (MSs). The need and criteria for the selection of the components (GRAI, Merise and IDEF0) are presented. Second, elements of the integrated methodology viz. graphical tools, structured approach, reference models, coherence tools and translation rules are presented. Emphasis is given to the need for new tools: coherence tools, translation rules and reference models, in order to ensure integration.     

Study on tool wear and surface roughness in machining of particulate aluminum metal matrix composite-response surface methodology approach

Metal matrix composites (MMC) have become a leading material among composite materials, and in particular, particle reinforced aluminum MMCs have received considerable attention due to their excellent engineering properties. These materials are known as the difficult-to-machine materials because of the hardness and abrasive nature of reinforcement element-like silicon carbide particles (SiC_p). In this study, an attempt has been made to model the machinability evaluation through the response surface methodology in machining of homogenized 20% SiC_p LM25 Al MMC manufactured through stir cast route. The combined effects of four machining parameters including cutting speed (s), feed rate (f), depth of cut (d), and machining time (t) on the basis of two performance characteristics of flank wear (VB_max) and surface roughness (Ra) were investigated. The contour plots were generated to study the effect of process parameters as well as their interactions. The process parameters are optimized using desirability-based approach response surface methodology.     

Estimating the effect of process parameters on surface integrity of EDMed AISI D2 tool steel by response surface methodology coupled with grey relational analysis

In this investigation, a hybrid optimization approach is used for the estimation of minimal surface integrity of surface created in electrical discharge machining (EDM). A new combination, response surface methodology coupled with the grey relational analysis method has been proposed and used to optimize the machining parameters of EDM. The significant input parameters such as pulse current (Ip), pulse duration (Ton), duty cycle (Tau) and discharge voltage (V ) are considered, and white layer thickness, surface roughness, and surface crack density have been considered as responses for this study. Thirty experiments were conducted on American Iron and Steel Institute (AISI) D2 steel work piece materials based on central composite design. The optimum conditions of the machining parameters were obtained from the grey relational grade. Analysis of variance is used to find the percentage contribution of the input parameters and found that Tau was the most influencing parameter followed by Ton and Ip in EDM of D2 steel. The $R^2$ value for the grey relational grade model was 0.918. These results provide useful information about how to control the responses and ensure the high-quality surfaces-quality surfaces. This method is simple with easy operability. The assessment outcome provides a scientific reference to obtain the minimal condition of surface integrity, and they were found to be a pulse current of 1 A, a pulse duration of 50 μs, a duty cycle of 80 %, and discharge voltage 40 V.     

Determining the optimum process parameter for grinding operations using robust process

We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine optimum parameters for minimum surface roughness (Ra) and vibration (Vb) in external cylindrical grinding. First, an experiment was conducted in a CNC cylindrical grinding machine. The TM using L ₂₇ orthogonal array was applied to the design of the experiment. The three input parameters were workpiece revolution, feed rate and depth of cut; the outputs were vibrations and surface roughness. Second, to minimize wheel vibration and surface roughness, two optimized models were developed using computer-aided single-objective optimization. The experimental and statistical results revealed that the most significant grinding parameter for surface roughness and vibration is workpiece revolution followed by the depth of cut. The predicted values and measured values were fairly close, which indicates (R _Ra ² =94.99 and R _Vb ² =92.73) that the developed models can be effectively used to predict surface roughness and vibration in the grinding. The established model for determination of optimal operating conditions shows that a hybrid approach can lead to success of a robust process.     

Increasing the sine-cosine transformation accuracy in signal approximation and interpolation

Sine-cosine transformations on the interval ?0.5T ≤ t ≤ 0.5T, which are equivalent to a cosine transformation on the interval 0 ≤ t ≤ T, are considered. Relationships for the error variance of signal reconstruction are obtained. Depending on the a priori information on the signal, the relationships make it possible to choose for its representation the most suitable orthogonal expansion. A stationary random signal model is used for comparing the expansion with a conventional sine-cosine transformation.     

Near-grain-boundary characterization by atomic force microscopy

Characterization of near-grain boundary is carried out by atomic force microscopy (AFM). It has been observed to be the most suitable technique owing to its capability to investigate the surface at high resolution. Commercial purity-grade nickel processed under different conditions, viz., (i) cold-rolled and annealed and (ii) thermally etched condition without cold rolling, is considered in the present study. AFM crystallographic data match well with the standard data. Hence, it establishes two grain-boundary relations viz., plane matching and coincidence site lattice (CSL Σ=9) relation for the two different sample conditions.     

Computer-Aided Fixture Design Verification. Part 2. Tolerance Analysis

Tolerance analysis is the most important issue in computer-aided fixture design (CAFD), and it is an important part of computer-aided fixture design verification (CAFDV). This study presents a new approach for fixture tolerance analysis that is more generalised and can be used to assign locator tolerances based on machining surface tolerance requirements. The tolerance analysis is also generalised to handle any type of fixture design, workpiece, datum feature, and machining feature tolerance. Locator tolerance assignment distributes tolerances to locators based on a sensitivity analysis.     

<Emphasis Type="Bold">Two-Phase Parameter Design for the Optimisation of the Electrical-Discharge Machining Process Using a Taguchi Dynamic Experiment</Emphasis>

The paper presents the application of a Taguchi dynamic experiment in developing a robust high-speed and high-quality electrical-discharge machining (EDM) process. In this study, a two-phase parameter design strategy coupled with a double-signal ideal function methodology is proposed. In the first phase, the ideal function of the EDM process is designed as a linear relationship between the main input signal (machining time) and the first output (material removal weight). This model seeks to develop a robust machining process that leads to a high material removal rate. In the second phase, the ideal function is particularly designed as a linear relationship between the adjustment signal (electrode dimension) and the second output (product dimension). The purpose is to adjust machined product dimension of the EDM through optimised process parameters obtained in the first phase, to the desired dimension to provide an allowance for subsequent fine-polishing. Experimental results showed that using a Taguchi dynamic experiment coupled with the proposed two-phase design strategy is simple, effective, and efficient for developing a robust high-speed and high-quality EDM machining process. Optimisation of multiple quality characteristics in the EDM process has been achieved to meet the customers requirements.     

A material selection methodology and expert system for sustainable product design

Material selection is one of the main phases of product design process that has great impact on the manufacturing of sustainable products. One of the best approaches of material selection for sustainable products is life cycle engineering (LCE). But LCE is a costly and cumbersome task and it is not economic to perform this task for a large number of proposed materials in order to choose the most suitable one for a sustainable product. Instead, it is more reasonable to make a preliminary filtering on the proposed materials and obtain a shorter list of candidate materials and then perform LCE on alternatives which are obtained from preliminary filtering. Since environmental friendliness of materials is a critical sustainability issue, so it is a good criterion for preliminary filtering of alternatives. In this paper, a new methodology is proposed to support preliminary filtering of alternatives from environmental viewpoint. The methodology uses the knowledge of experts in the field of eco-design. The knowledge is translated to decision making rules and a decision tree is developed to guide the choice. In order to use the capabilities of frame-based systems, an object-oriented approach for representation of knowledge is also proposed. Moreover, a prototype hybrid expert system based on the proposed methodology called material selection expert system for sustainable product design is developed to support the task of preliminary filtering. Finally, a case study from tire manufacturing industries is presented to show the validity of the proposed system. The results show that the system can determine the appropriate candidate materials and hence improve the possibility of manufacturing of more sustainable products. Eliminating alternatives that do not have the necessary conditions for sustainable product leads to a large saving in time and cost of the LCE evaluation process     

Novel space-based design methodology for preliminary engineering design

This work is motivated from set-based concurrent engineering (SBCE) paradigm. In contrast to the traditional design practice, SBCE considers a broader range of design possibilities (i.e., design space) from the outset, explicitly communicates and reasons about sets of design alternatives, and gradually narrows the sets to eliminate inferior alternatives until a final solution remains. Thus, the key to success of SBCE is the proper implementation of the space representation method to define the possible design region, the space mapping method to obtain the performance space achievable by the initial design space, and the space narrowing method to eliminate infeasible subspaces from the initial design space. This paper proposes a novel space-based design methodology for preliminary engineering design. The main characteristic features of our approach are to incorporate the designer’s preference structure with degrees of desirability in specifying both design space and performance space, and to find a ranged set of design solutions that satisfy changing sets of performance requirements through set-to-set space mapping from design space to performance space and space narrowing to eliminate infeasible design subspaces. Central to the proposed design methodology is the integration of meta-modeling techniques, modified fuzzy arithmetic, design of experiment (D_oE), robust design techniques, and uncertainty analysis. A preliminary parametric design example of a vehicle side impact beam is demonstrated to show the effectiveness of the proposed methodology and its availability in the large-scale multi-objective design synthesis problem.     

RTDS implementation of an improved sliding mode based inverter controller for PV system

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (R_t), maximum percentage overshoot (MPOS), settling time (T_s), and steady state error (E_ss) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems.     

A hierarchical fuzzy axiomatic design methodology for ergonomic compatibility evaluation of advanced manufacturing technology

Advanced manufacturing technology (AMT) is a relevant resource that has been extensively used in modern industries around the world with the aim of being competitive and maintaining high levels of quality and performance. There is a wide variety of tools and models available in the literature to support AMT selection and evaluation processes. Usually, they consist of analyses of tangible aspects, such as cost, time, speed, precision, among others; however, some other important aspects are commonly neglected, that is, the case of human factors and ergonomic characteristics. This paper presents a new methodology for the evaluation of ergonomic compatibility of AMT. This methodology may be considered as a decision aid; thus, decision makers might perform their duties in a more complete manner taking into account ergonomic attributes. Fuzzy axiomatic design applications are state of the art methods for decision making, and this paper contributes with a unique application for ergonomic compatibility evaluation for AMT. The first part of the paper presents the findings of an extensive literature review about important ergonomic attributes of AMT. Then, those attributes were originally structured following a multi-attribute axiomatic design approach for AMT ergonomic evaluation under a fuzzy environment. Also, a unique ergonomic compatibility survey was proposed for data collection and an original procedure was developed for AMT evaluation, a numerical example is provided. The ergonomic compatibility concept was tested and validated using the Cronbach's alpha test (α?≥?0.7), finding that the instrument is suitable for the measurement of the proposed construct.     

Multi-objective optimization of combustion,performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (P_max)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO _x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, P_max), performance (BTE) and emission (CO, NO _x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, P_max, CO, NO _x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.