Automated Decomposition of Complex Parts for Manufacturing With Advanced Joining Processes

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Brandon Massoni ◽  
Matthew I. Campbell
Keyword(s):  
Artificial Intelligence ◽  
Minimum Cost ◽  
Manufacturing Cost ◽  
Optimization Approach ◽  
Tree Search ◽  
Exploration And Exploitation ◽  
Design Engineers ◽  
Complex Part ◽  
Rotary Friction Welding ◽  
Complex Parts

Abstract Advanced joining processes can be used to build-up complex parts from stock shapes, thereby reducing waste material. For high-cost metals, this can significantly reduce the manufacturing cost. Nevertheless, determining how to divide a complex part into subparts requires experience and currently takes hours for an engineer to evaluate alternative options. To tackle this issue, we present an artificial intelligence (AI) tree search to automatically decompose parts for advanced joining and generate minimum cost manufacturing plans. The AI makes use of a multi-fidelity optimization approach to balance exploration and exploitation. This approach is shown to provide good manufacturing feedback in less than 30 minutes and produce results that are competitive against experienced design engineers. Although the manufacturing plan models presented were developed specifically for linear and rotary friction welding, the primary algorithms are applicable to other advanced joining operations as well.

scholarly journals Applying Multiobjective Cost and Weight Optimization to the Initial Design of Turbine Disks

2007 ◽  
Vol 129 (12) ◽  
pp. 1303-1310 ◽  
Author(s):  
A. R. Rao ◽  
J. P. Scanlan ◽  
A. J. Keane
Keyword(s):  
Fatigue Life ◽  
Minimum Weight ◽  
Minimum Cost ◽  
Aircraft Engine ◽  
Integrated System ◽  
Structural Performance ◽  
Manufacturing Cost ◽  
Optimization Approach ◽  
Element Analysis ◽  
Conflicting Objectives

Aerospace design optimization typically explores the effects of structural performance and aerodynamics on the geometry of a component. This paper presents a methodology to incorporate manufacturing cost and fatigue life models within an integrated system to simultaneously trade off the conflicting objectives of minimum weight and manufacturing cost while satisfying constraints placed by structural performance and fatigue. A case study involving the design of a high pressure turbine disk from an aircraft engine is presented. Manufacturing cost and fatigue life models are developed in DECISIONPRO™, a generic modeling tool, whereas finite element analysis is carried out in the Rolls-Royce PLC proprietary solver SC03. A multiobjective optimization approach based on the nondominated sorting genetic algorithm (NSGA) is used to evaluate the Pareto front for minimum cost and volume designs. A sequential workflow of the different models embedded within a scripting environment developed in MATLAB™ is used for automating the entire process.

Dynamic Optimization of Steel Catenary Risers Based on Reliability Using Metamodel

2010 ◽  
Author(s):  
Wenqing Zheng ◽  
Hezhen Yang
Keyword(s):  
Dynamic Optimization ◽  
Computational Cost ◽  
Minimum Cost ◽  
Probabilistic Constraints ◽  
Optimization Approach ◽  
Element Analysis ◽  
Steel Catenary Riser ◽  
Reliability Based Design ◽  
Minimum Cost Design ◽  
Steel Catenary Risers

Reliability based design optimization (RBDO) of a steel catenary riser (SCR) using metamodel is investigated. The purpose of the optimization is to find the minimum-cost design subjecting to probabilistic constraints. To reduce the computational cost of the traditional double-loop RBDO, a single-loop RBDO approach is employed. The performance function is approximated by using metamodel to avoid time consuming finite element analysis during the dynamic optimization. The metamodel is constructed though design of experiments (DOE) sampling. In addition, the reliability assessment is carried out by Monte Carlo simulations. The result shows that the RBDO of SCR is a more rational optimization approach compared with traditional deterministic optimization, and using metamodel technique during the dynamic optimization process can significantly decrease the computational expense without sacrificing accuracy.

scholarly journals Penerapan Algoritma Dynamic Programing pada Pergerakan Lawan dalam Permainan Police and Thief

2019 ◽  
Vol 2 (2) ◽  
pp. 114
Author(s):  
Insidini Fawwaz ◽  
Agus Winarta
Keyword(s):  
Artificial Intelligence ◽  
Dynamic Programming ◽  
Search Algorithm ◽  
Dynamic Programming Algorithm ◽  
Minimum Cost ◽  
Programming Algorithm ◽  
Shortest Route ◽  
Basic Meaning ◽  
Dynamic Programing ◽  
Approximate Distance

<p class="8AbstrakBahasaIndonesia"><em>Games have the basic meaning of games, games in this case refer to the notion of intellectual agility. In its application, a Game certainly requires an AI (Artificial Intelligence), and the AI used in the construction of this police and thief game is the dynamic programming algorithm. This algorithm is a search algorithm to find the shortest route with the minimum cost, algorithm dynamic programming searches for the shortest route by adding the actual distance to the approximate distance so that it makes it optimum and complete. Police and thief is a game about a character who will try to run from </em><em>police.</em><em> The genre of this game is arcade, built with microsoft visual studio 2008, the AI used is the </em><em>Dynamic Programming</em> <em>algorithm which is used to search the path to attack players. The results of this test are police in this game managed to find the closest path determined by the </em><em>Dynamic Programming</em> <em>algorithm to attack players</em></p>

scholarly journals Reducing the Frame Vibration of Delta Robot in Pick and Place Application: An Acceleration Profile Optimization Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hongtai Cheng ◽  
Wei Li
Keyword(s):  
Surrogate Model ◽  
High Speed ◽  
Gaussian Process Regression ◽  
Frame Structure ◽  
Manufacturing Cost ◽  
Optimization Approach ◽  
Pick And Place ◽  
Delta Robot ◽  
Profile Optimization ◽  
Acceleration Profile

Delta robot is typically mounted on a frame and performs high speed pick and place tasks from top to bottom. Because of its outstanding accelerating capability and higher center of mass, the Delta robot can generate significant frame vibration. Existing trajectory smoothing methods mainly focus on vibration reduction for the robot instead of the frame, and modifying the frame structure increases the manufacturing cost. In this paper, an acceleration profile optimization approach is proposed to reduce the Delta robot-frame vibration. The profile is determined by the maximum jerk, acceleration, and velocity. The pick and place motion (PPM) and resulting frame vibration are analyzed in frequency domain. Quantitative analysis shows that frame vibration can be reduced by altering those dynamic motion parameters. Because the analytic model is derived based on several simplifications, it cannot be directly applied. A surrogate model-based optimization method is proposed to solve the practical issues. By directly executing the PPM with different parameters and measuring the vibration, a model is derived using Gaussian Process Regression (GPR). In order to reduce the frame vibration without sacrificing robot efficiency, those two goals are fused together according to their priorities. Based on the surrogate model, a single objective optimization problem is formulated and solved by Genetic Algorithm (GA). Experimental results show effectiveness of the proposed method. Behavior of the optimal parameters also verifies the robot-frame vibration mechanism.

scholarly journals Dynamic and Optimized Prototype Clustering for Relational Data based on Multiple Prototypes

2019 ◽  
Vol 8 (3) ◽  
pp. 5630-5634
Keyword(s):  
Artificial Intelligence ◽  
Data Clustering ◽  
Synthetic Data ◽  
Medical Data ◽  
Relational Data ◽  
Data Sets ◽  
Optimization Approach ◽  
Complex Task ◽  
Data Points ◽  
Clustering Approach

In artificial intelligence related applications such as bio-medical, bio-informatics, data clustering is an important and complex task with different situations. Prototype based clustering is the reasonable and simplicity to describe and evaluate data which can be treated as non-vertical representation of relational data. Because of Barycentric space present in prototype clustering, maintain and update the structure of the cluster with different data points is still challenging task for different data points in bio-medical relational data. So that in this paper we propose and introduce A Novel Optimized Evidential C-Medoids (NOEC) which is relates to family o prototype based clustering approach for update and proximity of medical relational data. We use Ant Colony Optimization approach to enable the services of similarity with different features for relational update cluster medical data. Perform our approach on different bio-medical related synthetic data sets. Experimental results of proposed approach give better and efficient results with comparison of different parameters in terms of accuracy and time with processing of medical relational data sets.

scholarly journals Consistency Techniques for Flow-Based Projection-Safe Global Cost Functions in Weighted Constraint Satisfaction

2012 ◽  
Vol 43 ◽  
pp. 257-292 ◽  
Author(s):  
J.H.M. Lee ◽  
K. L. Leung
Keyword(s):  
Constraint Satisfaction ◽  
Minimum Cost ◽  
Constraint Satisfaction Problems ◽  
Combinatorial Problems ◽  
Cost Functions ◽  
Global Constraints ◽  
Optimization Approach ◽  
Weak Version ◽  
Global Cost ◽  
Weighted Constraint

Many combinatorial problems deal with preferences and violations, the goal of which is to find solutions with the minimum cost. Weighted constraint satisfaction is a framework for modeling such problems, which consists of a set of cost functions to measure the degree of violation or preferences of different combinations of variable assignments. Typical solution methods for weighted constraint satisfaction problems (WCSPs) are based on branch-and-bound search, which are made practical through the use of powerful consistency techniques such as AC*, FDAC*, EDAC* to deduce hidden cost information and value pruning during search. These techniques, however, are designed to be efficient only on binary and ternary cost functions which are represented in table form. In tackling many real-life problems, high arity (or global) cost functions are required. We investigate efficient representation scheme and algorithms to bring the benefits of the consistency techniques to also high arity cost functions, which are often derived from hard global constraints from classical constraint satisfaction. The literature suggests some global cost functions can be represented as flow networks, and the minimum cost flow algorithm can be used to compute the minimum costs of such networks in polynomial time. We show that naive adoption of this flow-based algorithmic method for global cost functions can result in a stronger form of null-inverse consistency. We further show how the method can be modified to handle cost projections and extensions to maintain generalized versions of AC* and FDAC* for cost functions with more than two variables. Similar generalization for the stronger EDAC* is less straightforward. We reveal the oscillation problem when enforcing EDAC* on cost functions sharing more than one variable. To avoid oscillation, we propose a weak version of EDAC* and generalize it to weak EDGAC* for non-binary cost functions. Using various benchmarks involving the soft variants of hard global constraints ALLDIFFERENT, GCC, SAME, and REGULAR, empirical results demonstrate that our proposal gives improvements of up to an order of magnitude when compared with the traditional constraint optimization approach, both in terms of time and pruning.

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