scholarly journals Automated Estimation of Time and Cost for Determining Optimal Machining Plans

2012 ◽  
Author(s):  
Benjamin Van Blarigan ◽  
Matthew I. Campbell ◽  
Ata A. Eftekharian ◽  
Tolga Kurtoglu
Keyword(s):  
Evaluation Model ◽  
Time Estimation ◽  
Computational Time ◽  
Selection Strategy ◽  
Tool Selection ◽  
Individual Values ◽  
Machining Time ◽  
Process Plan ◽  
Preliminary Results ◽  
Automated Tool

In any manufacturing setting, producing a machined part is a complicated, multi-step process. It requires coordination between an engineer and a machinist to ensure all goals are met. A machinist creates a part based generally on personal experience and intuition, and while they know that this will result in the finished product, it is not guaranteed that the process plan chosen is the quickest or least expensive way to make the part. In the past year, we have been developing an automated tool that analyzes a solid model to determine its best process plan. The tool is essentially comprised of a reasoning engine that determines what processes are valid for particular sections of the part, and an evaluation engine that estimates the time and cost of the candidate processes. This paper presents the implemented evaluation engine, which assigns individual values of time and cost to machine operations. The evaluation starts with an automated tool selection strategy. The engineering model is able to determine the machining time of the tool(s) chosen. The method presented here takes a unique approach to machining time estimation that balances the trade-off between accuracy and computational time. Preliminary results suggest that the method is able to achieve accuracy near that of commercial CAM packages, with a much lower computational expense. The evaluation model takes into account non-productive manufacturing times (e.g. fixturing, inspection), and translates these to related costs. The method will be presented and discussed in this paper along with some preliminary results.

scholarly journals Ions motion optimization algorithm for multiobjective optimization problems

2021 ◽  
pp. 93-110 ◽  
Author(s):  
Hitarth Buch ◽  
Indrajit Trivedi
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Computational Time ◽  
Selection Strategy ◽  
Pareto Optimum ◽  
Optimal Solutions ◽  
Good Convergence ◽  
Motion Optimization ◽  
Multiobjective Approach ◽  
Benchmark Test Functions

This paper offers a novel multiobjective approach – Multiobjective Ions Motion Optimization (MOIMO) algorithm stimulated by the movements of ions in nature. The main inspiration behind this approach is the force of attraction and repulsion between anions and cations. A storage and leader selection strategy is combined with the single objective Ions Motion Optimization (IMO) approach to estimate the Pareto optimum front for multiobjective optimization. The proposed method is applied to 18 different benchmark test functions to confirm its efficiency in finding optimal solutions. The outcomes are compared with three novel and well-accepted techniques in the literature using five performance parameters quantitatively and obtained Pareto fronts qualitatively. The comparison proves that MOIMO can approximate Pareto optimal solutions with good convergence and coverage with minimum computational time.

Modeling Technique of Prosthetic Heart Valves

1984 ◽  
Vol 106 (1) ◽  
pp. 83-88 ◽  
Author(s):  
T. Kitamura ◽  
T. Kijima ◽  
H. Akashi
Keyword(s):  
Real Time ◽  
Heart Valves ◽  
Time Estimation ◽  
Modeling Technique ◽  
Computational Time ◽  
Prosthetic Heart Valves ◽  
The Real ◽  
Hemodynamic State ◽  
Real Time Estimation

This paper demonstrates a modeling technique of prosthetic heart valves. In the modeling, a pumping cycle is divided into four phases, in which the state of the valve and flow is different. The pressure-flow relation across the valve is formulated separately in each phase. This technique is developed to build a mathematical model used in the real time estimation of the hemodynamic state under artificial heart pumping. The model built by this technique is simple enough for saving the computational time in the real time estimation. The model is described by the first-order ordinary differential equation with 12 parameters. These parameters can be uniquely determined beforehand from in-vitro experimental data. It is shown that the model can adapt, with sufficient accuracy, to a change in the practical pumping condition and the viscosity of the fluid in their practical range, and is also demonstrated that the estimated backflow volume by model agrees closely with the actual one.

Parallel Turning Process Parameter Optimization Based on a Novel Heuristic Approach

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Lie Tang ◽  
Robert G. Landers ◽  
S. N. Balakrishnan
Keyword(s):  
Parameter Optimization ◽  
Search Algorithm ◽  
Process Parameter ◽  
Computational Time ◽  
Turning Process ◽  
Machining Time ◽  
Process Parameter Optimization ◽  
Turning Operations ◽  
Operation Optimization ◽  
Machining Operations

Process parameter optimization has been widely investigated in single-tool machining operations. However, for multitool machining operation optimization, the research reported in literature is scarce. In this paper, a novel heuristic algorithm based on particle swarm optimization (PSO) is proposed to optimize, in terms of minimum machining time, the process parameters for two-tool parallel turning operations with n features. Both single-pass and multipass operations are considered. The simulation results show that the performance of the proposed algorithm, in terms of total machining time and required computational time, is superior to an exhaustive search algorithm.

Single- and multiple-trait index selection efficiencies in Fraser fir Christmas trees

1994 ◽  
Vol 24 (7) ◽  
pp. 1487-1494 ◽  
Author(s):  
R.J. Arnold ◽  
F.E. Bridgwater ◽  
J.B. Jett
Keyword(s):  
Genetic Variation ◽  
Selection Process ◽  
Selection Strategy ◽  
Index Selection ◽  
Family Selection ◽  
Multiple Trait ◽  
Individual Values ◽  
Source Selection ◽  
Stem Straightness ◽  
Initial Source

Selection methods for Abiesfraseri (Pursh) Poir. for Christmas tree wholesale value were evaluated based on parameters from the species' first genetic field test. For single-trait individual selection, combined individual plus family selection at half rotation age (4 years) on total height (HT4) gave the greatest estimated full rotation (8-year) retail value (VALUE) gain of 24.3%. The best 8-year trait, crown diameter, resulted in a gain of only 22.4%. Incorporation of family mean information together with individual values in the selection process was important in maximizing gains. Only 8-year stem straightness (STR8) had unfavorable genetic and phenotypic correlations with other traits. With multitrait combined optimum index selection, use of Kempthorne restrictions to prevent adverse change in this trait seriously limited gains in other 8-year traits. Severity of this limitation increased for younger age indices, and for those with fewer traits. Unrestricted combined optimum indices offered substantial VALUE gain advantages and only small decreases in STR8. Initial selection among seed sources also increased VALUE gain, despite decreasing the effective additive genetic variation. VALUE gains through initial source selection exceeded gain reductions from the genetic variation decreases. The optimum selection strategy, with 30.5% VALUE gain, involved initial source selection followed by unrestricted combined optimum index selection on HT4, and 4-year density. Though slightly below the maximum, this strategy would provide substantial economic and technical advantage in conducting field tests.

Deviplan: A Bid Preparation System for Mechanical SMEs

2000 ◽  
Author(s):  
Dimitris Kiritsis ◽  
Paul Xirouchakis
Keyword(s):  
Petri Net ◽  
Cost Estimation ◽  
Machine Tools ◽  
Software Tool ◽  
Machining Parameters ◽  
Machining Time ◽  
Process Plan ◽  
Machined Parts ◽  
Product Description ◽  
Bid Preparation

Abstract The problem under consideration is the cost estimation and consequent bid preparation for machined parts subcontracted to mechanical small and medium enterprises (SME). This activity, i.e. cost estimation and corresponding bid preparation, becomes more and more important due to the increasing and globalized competition in this market. There is, therefore, a clear need for precise and accurate cost estimation of machined parts in order for a small company to justify its prices. The proposed prototype software system is based (i) on a manufacturing feature based product description of the part to be machined and (ii) on a non-linear model of its process plan using Petri nets, taking into consideration processing alternatives and precedence constraints, which allows a heuristic based best search of the process plan and, consequently, the corresponding machining time and cost. Product description is done interactively through user friendly interfaces and the corresponding process planning model is constructed automatically in the form of a Petri net. Machine tools and their characteristics are selected from a customized database. Tools and machining parameters are selected through a link with the tool management software TOOL Light©. Minimum time or cost process plans and corresponding bids are reported using the Petri net model of the machined part under consideration and using machining heuristics. The type of parts that are considered in our application are rotational or prismatic parts that are used as components in complex machines like machine tools or automatic assembly machines.

Supervised Learning for Arrival Time Estimations in Restaurant Meal Delivery

2021 ◽  
Author(s):  
Florentin D. Hildebrandt ◽  
Marlin W. Ulmer
Keyword(s):  
Supervised Learning ◽  
Arrival Time ◽  
Computational Study ◽  
Time Estimation ◽  
Computational Time ◽  
Arrival Times ◽  
Meal Preparation ◽  
Online Simulation ◽  
Online Simulations ◽  
Meal Delivery

Restaurant meal delivery companies have begun to provide customers with meal arrival time estimations to inform the customers’ selection. Accurate estimations increase customer experience, whereas inaccurate estimations may lead to dissatisfaction. Estimating arrival times is a challenging prediction problem because of uncertainty in both delivery and meal preparation process. To account for both processes, we present an offline and online-offline estimation approaches. Our offline method uses supervised learning to map state features directly to expected arrival times. Our online-offline method pairs online simulations with an offline approximation of the delivery vehicles’ routing policy, again achieved via supervised learning. Our computational study shows that both methods perform comparably to a full near-optimal online simulation at a fraction of the computational time. We present an extensive analysis on how arrival time estimation changes the experience for customers, restaurants, and the platform. Our results indicate that accurate arrival times not only raise service perception but also improve the overall delivery system by guiding customer selections, effectively resulting in faster delivery and fresher food.

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