Conventional Optimization Techniques for Manufacturing Applications

2006 ◽  
pp. 27-40
Keyword(s):  
Optimization Techniques ◽  
Manufacturing Applications ◽  
Conventional Optimization

Optimization of Stirling Engine Performance Based on Multipoint Approximation Technique

1994 ◽  
Author(s):  
Vassili V. Toropov ◽  
Henrik Carlsen
Keyword(s):  
Computing Time ◽  
Engine Performance ◽  
Stirling Engine ◽  
Optimization Techniques ◽  
Approximation Technique ◽  
Working Cycle ◽  
Design Variables ◽  
Stirling Engines ◽  
Multipoint Approximation ◽  
Conventional Optimization

Abstract The ideal Stirling working cycle has the maximum obtainable efficiency defined by Carnot efficiency, and highly efficient Stirling engines can therefore be built, if designed properly. To analyse the power output and the efficiency of a Stirling engine, numerical simulation programs (NSP) have been developed, which solve the thermodynamic equations. In order to find optimum values of design variables, numerical optimization techniques can be used (Bartczak and Carlsen, 1991). To describe the engine realistically, it is necessary to consider several tens of design variables. As even a single call for NSP requires considerable computing time, it would be too time consuming to use conventional optimization techniques, which require a very large number of calls for NSP. Furthermore, objective and constraint functions of the optimization problem present some level of noise, i.e. can only be estimated with a finite accuracy. To cope with these problems, the multipoint explicit approximation technique is used.

scholarly journals Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 109 ◽  
Author(s):  
Angelos Angelopoulos ◽  
Emmanouel T. Michailidis ◽  
Nikolaos Nomikos ◽  
Panagiotis Trakadas ◽  
Antonis Hatziefremidis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Optimization Techniques ◽  
Human Machine Interaction ◽  
Research Fields ◽  
Prediction And Prevention ◽  
Conventional Optimization ◽  
Manufacturing Data

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

Ultrasonic Machining Process Optimization by Cuckoo Search and Chicken Swarm Optimization Algorithms

2020 ◽  
Vol 11 (2) ◽  
pp. 1-26 ◽  
Author(s):  
Bappa Acherjee ◽  
Debanjan Maity ◽  
Arunanshu S Kuar
Keyword(s):  
Cuckoo Search ◽  
Optimization Techniques ◽  
Machining Process ◽  
Computational Time ◽  
Machining Parameters ◽  
Ultrasonic Machining ◽  
Swarm Optimization ◽  
Fireworks Algorithm ◽  
Conventional Optimization ◽  
Chicken Swarm Optimization

The ultrasonic machining (USM) process has been analyzed in the present study to obtain the desired process responses by optimizing machining parameters using cuckoo search (CS) and chicken swarm optimization (CSO), two powerful nature-inspired, population and swarm-intelligence-based metaheuristic algorithms. The CS and CSO algorithms have been compared with other non-conventional optimization techniques in terms of optimal results, convergence, accuracy, and computational time. It is found that CS and CSO algorithms predict superior single and multi-objective optimization results than gravitational search algorithms (GSAs), genetic algorithms (GAs), particle swarm optimization (PSO) algorithms, ant colony optimization (ACO) algorithms and artificial bee colony (ABC) algorithms, and gives exactly the same results as predicted by the fireworks algorithm (FWA). The CS algorithm outperforms all other algorithms namely CSO, FWA, GSA, GA, PSO, ACO, and ABC algorithms in terms of mean computational time, whereas, the CSO algorithm outperforms all other algorithms except for the CS and GSA algorithms.

Design for Manufacturing Using B-Spline Developable Surfaces

1998 ◽  
Vol 42 (03) ◽  
pp. 207-215 ◽  
Author(s):  
Julie S. Chalfant ◽  
Takashi Maekawa
Keyword(s):  
Heat Treatment ◽  
Design For Manufacturing ◽  
Optimization Techniques ◽  
Developable Surface ◽  
Planar Surface ◽  
Complex Objects ◽  
B Spline ◽  
Developable Surfaces ◽  
Manufacturing Applications ◽  
User Friendly

A developable surface can be formed by bending or rolling a planar surface without stretching or tearing; in other words, it can be developed or unrolled isometrically onto a plane. Developable surfaces are widely used in the manufacture of items that use materials that are not amenable to stretching such as the formation of ducts, shoes, clothing and automobile parts including upholstery and body panels (Frey & Bindschadler 1993). Designing a ship hull entirely of developable surfaces would allow production of the hull using only rolling or bending. Heat treatment would only be required for removal of distortion, thus greatly reducing the labor required to form the hull. Although developable surfaces play an important role in various manufacturing applications, little attention has been paid to implementing developable surfaces from the onset of a design. This paper investigates novel, user friendly methods to design complex objects using B-spline developable surfaces based on optimization techniques. Illustrative examples show the substantial improvements this method achieves over previously developed methods.

Application of the corrected response surface methodology for the optimization of a permanent magnet synchronous machine

2018 ◽  
Vol 37 (2) ◽  
pp. 740-754 ◽  
Author(s):  
Stéphane Vivier
Keyword(s):  
Optimal Design ◽  
Permanent Magnet ◽  
Response Surface ◽  
Synchronous Machine ◽  
Optimization Techniques ◽  
Permanent Magnet Synchronous Machine ◽  
Optimization Approach ◽  
Content Type ◽  
Coarse Model ◽  
Conventional Optimization

PurposeThis paper aims to introduce an original application of the corrected response surface method (CRSM) in the context of the optimal design of a permanent magnet synchronous machine used as an integrated starter generator. This method makes it possible to carry out this design in a very efficient manner, in comparison with conventional optimization approaches. Design/methodology/approachThe search for optimal conditions is achieved by the joint use of two multi-physics models of the machine to be optimized. The former models most finely the physical functioning of the machine; it is called “fine model”. The second model describes the same physical phenomena as the fine model but must be much quicker to evaluate. Thus, to minimize its evaluation time, it is necessary to simplify it considerably. It is called “coarse model”. The lightness of the coarse model allows it to be used intensively by conventional optimization algorithms. On the other hand, the fine reference model makes it possible to recalibrate the results obtained from the coarse model at any instant, and mainly at the end of each classical optimization. The difference in definition between fine and coarse models implies that these two models do not give the same output values for the same input configuration. The approach described in this study proposes to correct the values of the coarse model outputs by constructing an adjustment (correcting) response surface. This gives the name to this method. It then becomes possible to have the entire load of the optimization carried over to the coarse model adjusted by the addition of this correction response surface. FindingsThe application of this method shows satisfactory results, in particular in comparison with those obtained with a traditional optimization approach based on a single (fine) model. It thus appears that the approach by CRSM makes it possible to converge much more quickly toward the optimal configurations. Also, the use of response surfaces for optimization makes it possible to capitalize the modeling data, thus making it possible to reuse them, if necessary, for subsequent optimal design studies. Numerous tests show that this approach is relatively robust to the variations of many important functioning parameters. Originality/valueThe CRSM technique is an indirect multi-model optimization method. This paper presents the application of this relatively undeveloped optimization approach, combining the features and benefits of (Indirect) efficient global optimization techniques and (multi-model) space mapping methods.

scholarly journals A Heterogeneous Parallel LU Factorization Algorithm Based on a Basic Column Block Uniform Allocation Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Rongteng Wu ◽  
Xiaohong Xie
Keyword(s):  
Heterogeneous Computing ◽  
Wait Time ◽  
Optimization Techniques ◽  
Programming Models ◽  
Lu Factorization ◽  
Allocation Strategy ◽  
Multiple Gpus ◽  
Factorization Algorithm ◽  
Heterogeneous Architectures ◽  
Conventional Optimization

Most supercomputers are shipped with both a CPU and a GPU. With the powerful parallel computing capability of GPUs, heterogeneous computing architecture produces new challenges for system software development and application design. Because of the significantly different architectures and programming models of CPUs and GPUs, conventional optimization techniques for CPUs may not work well in a heterogeneous multi-CPU and multi-GPU system. We present a heterogeneous parallel LU factorization algorithm for heterogeneous architectures. According to the different performances of the processors in the system, any given matrix is partitioned into different sizes of basic column blocks. Then, a static task allocation strategy is used to distribute the basic column blocks to corresponding processors uniformly. The idle time is minimized by optimized sizes and the number of basic column blocks. Right-looking ahead technology is also used in systems configured with one CPU core to one GPU to decrease the wait time. Experiments are conducted to test the performance of synchronization and load balancing, communication cost, and scalability of the heterogeneous parallel LU factorization in different systems and compare it with the related matrix algebra algorithm on a heterogeneous system configured with multiple GPUs and CPUs.

EVOLUTIONARY OPTIMIZATION (EvOpt): A BRIEF REVIEW AND ANALYSIS

2003 ◽  
Vol 03 (04) ◽  
pp. 311-330 ◽  
Author(s):  
RUHUL SARKER ◽  
JOARDER KAMRUZZAMAN ◽  
CHARLES NEWTON
Keyword(s):  
Evolutionary Algorithms ◽  
Operations Research ◽  
Transportation Problem ◽  
Optimization Techniques ◽  
The Other ◽  
Computational Techniques ◽  
Computational Results ◽  
Conventional Optimization ◽  
Mathematical Foundations ◽  
Real World Problems

Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques.

Optimization of multiple responses in the Taguchi method using fuzzy regression

2014 ◽  
Vol 28 (1) ◽  
pp. 99-107 ◽  
Author(s):  
Abbas Al-Refaie
Keyword(s):  
Signal To Noise Ratio ◽  
Optimization Techniques ◽  
Fuzzy Regression ◽  
Distinct Advantage ◽  
Response Repetition ◽  
Statistical Regression ◽  
Multiple Responses ◽  
Wide Range ◽  
Fuzzy Regression Model ◽  
Manufacturing Applications

AbstractIn reality, the behavior of processes is sometimes vague and the observed data is irregular. This research proposes an approach for optimizing fuzzy multiple responses using fuzzy regression. In this approach, each response repetition is transformed into a signal to noise ratio then modeled using statistical multiple regression. A trapezoidal fuzzy regression model is formulated for each response utilizing the statistical regression coefficients. The most desirable response values and the deviation function are determined for each response. Finally, four optimization models are formulated for the trapezoidal membership fuzzy number to obtain the optimal factor level at each number. Two case studies are adopted for illustration, where excluding response fuzziness will result in misleading optimal factor settings if solved by the traditional optimization techniques. In conclusion, the proposed approach based on fuzzy regression approach can successfully optimize fuzzy multiple responses in a wide range of manufacturing applications on the Taguchi's method. Moreover, compared to other approaches, such as data envelopment analysis and grey relational analysis, the proposed approach has the distinct advantage of being able to generate models using only a small number of experimental data sets and minimizing inherent variations.

scholarly journals Metaheuristics Optimizations for Speed Regulation in Self Driving Vehicles

2020 ◽  
Vol 02 (01) ◽  
pp. 43-52
Author(s):  
Prof. Sathish
Keyword(s):  
Absolute Error ◽  
Optimization Techniques ◽  
The Self ◽  
Heuristic Optimization ◽  
Movement Velocity ◽  
The Past ◽  
Speed Regulation ◽  
Integral Controller ◽  
Conventional Optimization ◽  
Fine Tune

The speed regulation becomes an important necessity in the self –driving vehicles that are engaged in various driving chores. It prevails as a prominent area of research from the past decades, proportional, integral and the derivative controllers play significant role in regulating the movement velocity of the vehicles as perfect adjustments of the parameters linked with the controller could afford to provide a proper speed regulation. But the attaining a perfect adjustments in the parameters are highly tedious. To attain a proper speed regulation in the self-driving vehicles, the paper attempts to utilize the metaheuristics algorithms for optimizing the parameters and minimizing the errors associated with its attributes. A regulating function to fine tune the proportional derivative and the integral controller parameters is formulated in the proffered method and the proper adjustment is achieved utilizing the heuristic optimization. Triple algorithms, genetic (Ge-Al), memetics (Me-Al) and adaptive direct search based on mesh (M-ADS) is used in the proffered method to carry out the optimizations. The results on applying the proposed optimization techniques proves to be more accurate compared to the conventional optimization techniques that were employed in adjusting the absolute error that is integral and the minimizing oscillatory performances and the performance index.

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