design algorithm
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2022 ◽  
Vol 25 (6) ◽  
pp. 708-719
D. A. Ishenin ◽  
A. S. Govorkov

The study aimed to develop an algorithm for computer-aided design (CAD) of working operations. A processing route for machining components was developed based on the criteria of production manufacturability, industrial data and a digital model of the product. The process of machining a workpiece was analysed using a method of theoretical separation. The machining process of a frame workpiece was used as a model. The identified formal parameters formed a basis for developing a CAD algorithm and a model of manufacturing route associated with the mechanical processing of a work-piece applying a condition-action rule, as well as mathematical logic. The research afforded a scheme for selecting process operations, given the manufacturability parameters of a product design. The concept of CAD algorithm was developed to design a production process of engineering products with given manufacturability parameters, including industrial data. The principle of forming a route and selecting a machining process was proposed. Several criteria of production manufacturability (labour intensity, consumption of materials, production costs) were selected to evaluate mechanical processing. A CAD algorithm for designing technological operations considering the parameters of manufacturability was developed. The algorithm was tested by manufacturing a frame workpiece. The developed algorithm can be used for reducing labour costs and development time, at the same time as improving the quality of production processes. The formalisation of process design is a crucial stage in digitalisation and automation of all production processes.

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Hongxiao Wang ◽  
Qiang Li ◽  
Sang-Bing Tsai

With the rapid economic development and urbanization process accelerating, motor vehicle ownership in large cities is increasing year by year; urban traffic congestion, parking difficulties, and other problems are becoming increasingly serious; in ordinary daily life, continuous risk of disturbance, having a flexible transportation system network is more able to alleviate daily congestion in the city, and the main thing about flexible transportation network is its algorithm. It is worth noting that congestion in many cities is generally reflected in the main roads, while many secondary roads and branch roads are underutilized, and the limited road resources in cities are not fully utilized. As an economic and effective road traffic management measure, one-way traffic can balance the spatial and temporal distribution of traffic pressure within the road network, make full use of the existing urban road network capacity, and solve the traffic congestion problem. Therefore, it is of great theoretical and practical significance to develop a reasonable and scientific one-way traffic scheme according to the characteristics of traffic operation in different regions. Based on the fixed demand model, the influence of traffic demand changes is further considered, the lower-level model is designed as an elastic demand traffic distribution model, the excess demand method is used to transform the elastic demand problem into an equivalent fixed demand problem based on the extended network, and the artificial bee colony algorithm based on risk perturbation is designed to solve the two-level planning model. The case study gives a one-way traffic organization optimization scheme that integrates three factors, namely, the average load degree overload limit of arterial roads, the detour coefficient, and the number of on-street parking spaces on feeder roads, and performs sensitivity analysis on the demand scaling factor.

2022 ◽  
pp. 18-30

Purpose. Creation of design algorithm of continuous action mixing complexes that will allow defining parameters of the equipment proceeding from requirements to quality, productivity and the set compounding of mixture.Methodology. The method of discrete elements, classical mechanics positions, theory of solids contact interaction, method of mathematical modeling are used in the work.Findings. The paper proposes a generalized algorithm for designing a continuous mixing complex for bulk materials. The procedure for designing a centrifugal mixer, the flow shapers, plate feeders and conical-cylindrical hoppers are presented. Calculations of design and technological parameters are carried out on the basis of information about the physical and mechanical properties of bulk components particles, requirements for equipment performance and the mixture homogeneity. The results of calculations of the mixing complex for the three-component mixture used for the production of polyethylene film are presented. To test the proposed algorithm, a mathematical model based on the discrete elements method is created. The mixing process is modeled and the coefficients of inhomogeneity of each of the components in the finished mixture are determined. The obtained results confirmed that the proposed algorithm allows to determine the parameters of the mixing complex, which ensure compliance with the specified requirements for the quality and the equipment performance.Originality. Mathematical models of bulk motion dynamics in mixing complexes are improved, which include bunker devices, plate feeders, flow shapers and continuous centrifugal mixer, taking into account the bulk motion discrete nature.Practical value. The obtained results allow calculating the design and technological parameters of the equipment that is a part of the continuous mixing complex according to the set productivity, recipe and requirements to the mixture homogeneity.

Materials ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 153
Yi Huo ◽  
Yongtao Lyu ◽  
Sergei Bosiakov ◽  
Feng Han

With the change of people’s living habits, bone trauma has become a common clinical disease. A large number of bone joint replacements is performed every year around the world. Bone joint replacement is a major approach for restoring the functionalities of human joints caused by bone traumas or some chronic bone diseases. However, the current bone joint replacement products still cannot meet the increasing demands and there is still room to increase the performance of the current products. The structural design of the implant is crucial because the performance of the implant relies heavily on its geometry and microarchitecture. Bionic design learning from the natural structure is widely used. With the progress of technology, machine learning can be used to optimize the structure of bone implants, which may become the focus of research in the future. In addition, the optimization of the microstructure of bone implants also has an important impact on its performance. The widely used design algorithm for the optimization of bone joint replacements is reviewed in the present study. Regarding the manufacturing of the implant, the emerging additive manufacturing technique provides more room for the design of complex microstructures. The additive manufacturing technique has enabled the production of bone joint replacements with more complex internal structures, which makes the design process more convenient. Numerical modeling plays an important role in the evaluation of the performance of an implant. For example, theoretical and numerical analysis can be carried out by establishing a musculoskeletal model to prepare for the practical use of bone implants. Besides, the in vitro and in vivo testing can provide mechanical properties of bone implants that are more in line with the implant recipient’s situation. In the present study, the progress of the design, manufacture, and evaluation of the orthopedic implant, especially the joint replacement, is critically reviewed.

2021 ◽  
Vol 13 (3) ◽  
pp. 59-65
Daniela Ghelase ◽  
Luiza Daschievici ◽  

It is known that, from the point of view of the accuracy of a machine-tool, at its design, the dynamic behaviour of each element of the kinematic chains prevails. Worm-gear drives are widely used in the different machine-tools and robots. Therefore, it is important that during meshing, as far as possible, there are no vibrations, shocks, power losses, noise and low durability. These requirements can be met if, for example, the gear ratio is constant during meshing, without transmission errors, which means that the worm-gear drive should have a high accuracy. The accuracy improvement of the worm-gear drive has long been a focus of attention for machine-tools designers. Thus, this paper presents various approaches to solving such problems, based on modelling and simulation, such as: estimating the load share of worm-gear drives and to calculate the instantaneous tooth meshing stiffness and loaded transmission errors; the desired worm-gear drive design configuration by altering the optimum set of worm-gear drive design parameters which are suitable for the required performance by associating it with SVM (Support Vector Machine); optimization approach for design of worm-gear drive based on Genetic Algorithm; design optimization of worm-gear drive with reduced power loss; etc. The optimization of the worm-gear design is an important problem for the research because the design variables are correlated to each other. An optimal design algorithm developed by the authors of this paper, for worm-gear drive, is also presented.

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 33
Chin-Lin Pen ◽  
Wen-Jer Chang ◽  
Yann-Horng Lin

This paper develops a Takagi-Sugeno fuzzy observer gain design algorithm to estimate ship motion based on Automatic Identification System (AIS) data. Nowadays, AIS data is widely applied in the maritime field. To solve the problem of safety, it is necessary to accurately estimate the trajectory of ships. Firstly, a nonlinear ship dynamic system is considered to represent the dynamic behaviors of ships. In the literature, nonlinear observer design methods have been studied to estimate the ship path based on AIS data. However, the nonlinear observer design method is challenging to create directly since some dynamic ship systems are more complex. This paper represents nonlinear ship dynamic systems by the Takagi-Sugeno fuzzy model. Based on the Takagi-Sugeno fuzzy model, a fuzzy observer design method is developed to solve the problem of estimating using AIS data. Moreover, the observer gains of the fuzzy observer can be adjusted systemically by a novel algorithm. Via the proposed algorithm, a more suitable or better observer can be obtained to achieve the objectives of estimation. Corresponding to different AIS data, the better results can also be obtained individually. Finally, the simulation results are presented to show the effectiveness and applicability of the proposed fuzzy observer design method. Some comparisons with the previous nonlinear observer design method are also given in the simulations.

2021 ◽  
Pieter H Bos ◽  
Evelyne M. Houang ◽  
Fabio Ranalli ◽  
Abba E. Leffler ◽  
Nicholas A. Boyles ◽  

The lead optimization stage of a drug discovery program generally involves the design, synthesis and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations we have developed AutoDesigner, a de novo design algorithm. AutoDesigner employs a cloud-native, multi-stage search algorithm to carry out successive rounds of chemical space exploration and filtering. Millions to billions of virtual molecules are explored and optimized while adhering to a customizable set of project criteria such as physicochemical properties and potency. Additionally, the algorithm only requires a single ligand with measurable affinity and a putative binding model as a starting point, making it amenable to the early stages of a SBDD project where limited data is available. To assess the effectiveness of AutoDesigner, we applied it to the design of novel inhibitors of D-amino acid oxidase (DAO), a target for the treatment of schizophrenia. AutoDesigner was able to generate and efficiently explore over 1 billion molecules to successfully address a variety of project goals. The compounds generated by AutoDesigner that were synthesized and assayed (1) simultaneously met not only physicochemical criteria, clearance and central nervous system (CNS) penetration (Kp,uu) cutoffs, but also potency thresholds; (2) fully utilize structural data to discover and explore novel interactions and a previously unexplored subpocket in the DAO active site. The reported data demonstrate that AutoDesigner can play a key role in accelerating the discovery of novel, potent chemical matter within the constraints of a given drug discovery lead optimization campaign.

2021 ◽  
Vol 21 (12) ◽  
pp. 3789-3807
Dimitra M. Salmanidou ◽  
Joakim Beck ◽  
Peter Pazak ◽  
Serge Guillas

Abstract. The potential of a full-margin rupture along the Cascadia subduction zone poses a significant threat over a populous region of North America. Previous probabilistic tsunami hazard assessment studies produced hazard curves based on simulated predictions of tsunami waves, either at low resolution or at high resolution for a local area or under limited ranges of scenarios or at a high computational cost to generate hundreds of scenarios at high resolution. We use the graphics processing unit (GPU)-accelerated tsunami simulator VOLNA-OP2 with a detailed representation of topographic and bathymetric features. We replace the simulator by a Gaussian process emulator at each output location to overcome the large computational burden. The emulators are statistical approximations of the simulator's behaviour. We train the emulators on a set of input–output pairs and use them to generate approximate output values over a six-dimensional scenario parameter space, e.g. uplift/subsidence ratio and maximum uplift, that represent the seabed deformation. We implement an advanced sequential design algorithm for the optimal selection of only 60 simulations. The low cost of emulation provides for additional flexibility in the shape of the deformation, which we illustrate here considering two families – buried rupture and splay-faulting – of 2000 potential scenarios. This approach allows for the first emulation-accelerated computation of probabilistic tsunami hazard in the region of the city of Victoria, British Columbia.

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8285
José Miguel Fuster ◽  
Sergio Pérez-López ◽  
Pilar Candelas

In this work, we develop a new design method based on fast Fourier transform (FFT) for implementing zone plates (ZPs) with bifocal focusing profiles. We show that the FFT of the governing binary sequence provides a discrete sequence of the same length, which indicates the location of the main foci at the ZP focusing profile. Then, using reverse engineering and establishing a target focusing profile, we are capable of generating a binary sequence that provides a ZP with the desired focusing profile. We show that this design method, based on the inverse fast Fourier transform (IFFT), is very flexible and powerful and allows to tailor the design of bifocal ZPs to achieve focusing profiles with the desired foci locations and resolutions. The key advantage of our design algorithm, compared to other alternatives presented in previous works, is that our method provides bifocal focusing profiles with an absolute control of the foci locations. Moreover, although we analyze the performance of this novel design algorithm for underwater ultrasonics, it can also be successfully extended to different fields of physics, such as optics or microwaves, where ZPs are widely employed.

2021 ◽  
HaiYang Wang ◽  
Desheng Zhou ◽  
Jinze Xu ◽  
Shun Liu ◽  
Erhu Liu ◽  

Abstract Slickwater fracturing technology is one of the significant stimulation measures for the development of unconventional reservoirs. An effective proppant placement in hydraulic fractures is the key to increase the oil production of unconventional reservoirs. However, previous studies on optimizing proppant placement are mainly focused on CFD numerical simulation and related laboratory experiments, and an optimization design method that comprehensively consider multiple influencing factors has not been established. The objective of this study is to establish an optimal design algorithm for proppant placement based on the construction characteristics of slickwater fracturing combined with Back Propagation (BP) neural network. In this paper, a proppant placement simulation experimental device was built to analyze proppant placement form data. We established a BP neural network model that considers multiple influencing factors and used the proppant placement form data to train and calibrate the model, which the proppant placement form prediction model is finally obtained. Using the proppant placement form prediction model, we designed an algorithm that can quickly select the three groups of construction schemes with the best proppant-filling ratio based on the massive construction schemes. The results indicate that the prediction results of the algorithm for proppant placement form are consistent with the CFD simulation results and experimental results, and the numerical error of the balanced height and the distance between the front edge of the proppant sandbank and the fracture entrance is within 5%. After using this algorithm to optimize the design of the fracturing construction scheme for the C8 oil well in Changqing Oilfield, the stimulation performance of the C8 oil well after fracturing is 2.7 times that of the adjacent well. The optimal design algorithm for proppant placement established in this paper is an effective, accurate, and intelligent optimization algorithm. This algorithm will provide a novel method for hydraulic fracturing construction design in oilfields.

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