Multi-Part Setup Planning for Sheet Metal Bending Operations

1997 ◽  
Author(s):  
Satyandra K. Gupta ◽  
David Alan Bourne
Keyword(s):  
Process Planning ◽  
Planning Problem ◽  
Setup Planning ◽  
Planning Systems ◽  
Spatial Constraints ◽  
Part Family ◽  
Production Environments ◽  
Planning Algorithm ◽  
Planning Technique ◽  
Planning Problems

Abstract Most process planning systems solve process planning problem for individual parts. Quite often, many different parts can be produced on shared setups. However, plans generated by current process planning systems fail to exploit this commonality between setups and try to generate optimal setups for individual parts. In this paper, we present an algorithm for multi-part setup planning. This algorithm takes a family of parts and tries to find a composite setup plan that can work for every part in the part family. Our setup planning algorithm employs a new two-step approach to handle multi-part setup planning problems. First, we identify the setup constraints imposed by various bending operations in the part family. These setup constraints describe spatial constraints on sizes and locations of various tooling stages in the setup. After identifying setup constraints, we try to generate setup plans that can satisfy all setup constraints. Any setup plan that satisfies all setup constraints is capable of accommodating every part in the part family. Setup changes constitute a major portion of the production time in batch production environments. We believe that multipart setup planning technique can be used to significantly cut down the total number of setups and increase the overall throughput.

Sheet Metal Bending: Generating Shared Setups

1999 ◽  
Vol 121 (4) ◽  
pp. 689-694 ◽  
Author(s):  
S. K. Gupta ◽  
D. A. Bourne
Keyword(s):  
Sheet Metal ◽  
Process Planning ◽  
Planning Problem ◽  
Setup Planning ◽  
Planning Systems ◽  
Batch Production ◽  
Planning Techniques ◽  
Production Environments ◽  
Sheet Metal Bending ◽  
Different Parts

Contemporary process planners for sheet metal bending solve the process planning problem for individual parts. Quite often, many different parts can be produced on shared setups. However, plans generated by current process planning systems fail to exploit this commonality between setups and try to generate optimal setups for individual parts. In this paper, we present an algorithm for multi-part setup planning for sheet metal bending. This algorithm takes a set of parts and operation sequences for these parts, and tries to find a shared setup plan that can work for every part in the set. Setup changes constitute a major portion of the production time in batch production environments. Therefore, multi-part setup planning techniques can be used to significantly cut down the total number of setups and increase the overall through-put.

Software Development for Setup Planning of Rotational Part

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Sumit Dwivedi ◽  
Shahnawaz Alam
Keyword(s):  
Tolerance Analysis ◽  
Control Technique ◽  
Planning Problem ◽  
Setup Planning ◽  
Tolerance Chart ◽  
Efficiency And Effectiveness ◽  
Planning Algorithm ◽  
Rotational Part ◽  
Sound Foundation ◽  
Tolerance Control

An innovative approach was developed to solve the problem of setup planning, which is the most critical problem in process planning for discrete metal parts. Setup planning is the act of preparing detailed work instructions for setting up a part. The major objective of this research is to improve the performance of CAPP systems by developing a systematic approach to generate practical setup plans based on tolerance analysis. A comprehensive literature review on tolerance control in CAPP was conducted. It was found that tolerance chart analysis, a traditional tolerance control technique, is reactive in nature and can be greatly improved by solving the problem of setup planning. In order to develop a theoretically sound foundation for tolerance analysis-based setup planning, the problem of tolerance stack up in NC machining was analyzed in terms of manufacturing error analysis. Guidelines for setup planning were then developed based on the analysis. To systematically solve the setup planning problem, a graph theoretical setup planning algorithm for rotational parts was then developed for automated and integrated setup planning and fixture design. Its efficiency and effectiveness evaluated. The result is promising. The algorithms were then computerized. A setup planning program was developed under the Microsoft Windows environment using C.

scholarly journals Learning to Solve Planning Problems Efficiently by Means of Genetic Programming

2001 ◽  
Vol 9 (4) ◽  
pp. 387-420 ◽  
Author(s):  
Ricardo Aler ◽  
Daniel Borrajo ◽  
Pedro Isasi
Keyword(s):  
Problem Solving ◽  
Genetic Programming ◽  
Genetic Material ◽  
Planning Problem ◽  
Planning Systems ◽  
Genetic Operator ◽  
Empirical Results ◽  
Whole Class ◽  
Planning Problems ◽  
Blocks World

Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator—Instance-Based Crossover—that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.

scholarly journals FLECS: Planning with a Flexible Commitment Strategy

1995 ◽  
Vol 3 ◽  
pp. 25-52 ◽  
Author(s):  
M. Veloso ◽  
P. Stone
Keyword(s):  
Planning Problem ◽  
Common Belief ◽  
Planning Strategy ◽  
Operator Ordering ◽  
Commitment Strategy ◽  
Efficient Planning ◽  
Planning Algorithm ◽  
The Common ◽  
Planning Strategies ◽  
Planning Problems

There has been evidence that least-commitment planners can efficiently handle planning problems that involve difficult goal interactions. This evidence has led to the common belief that delayed-commitment is the "best" possible planning strategy. However, we recently found evidence that eager-commitment planners can handle a variety of planning problems more efficiently, in particular those with difficult operator choices. Resigned to the futility of trying to find a universally successful planning strategy, we devised a planner that can be used to study which domains and problems are best for which planning strategies. In this article we introduce this new planning algorithm, FLECS, which uses a FLExible Commitment Strategy with respect to plan-step orderings. It is able to use any strategy from delayed-commitment to eager-commitment. The combination of delayed and eager operator-ordering commitments allows FLECS to take advantage of the benefits of explicitly using a simulated execution state and reasoning about planning constraints. FLECS can vary its commitment strategy across different problems and domains, and also during the course of a single planning problem. FLECS represents a novel contribution to planning in that it explicitly provides the choice of which commitment strategy to use while planning. FLECS provides a framework to investigate the mapping from planning domains and problems to efficient planning strategies.

Research on pull-type multi-AGV system dynamic path optimization based on time window

2021 ◽  
pp. 095440702098271
Author(s):  
Hongying Shan ◽  
Chuang Wang ◽  
Cungang Zou ◽  
Mengyao Qin
Keyword(s):  
Path Planning ◽  
Time Window ◽  
Planning Problem ◽  
Dijkstra Algorithm ◽  
Before And After ◽  
Common Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Set Up ◽  
Path Planning Algorithm

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Robotica ◽  
2021 ◽  
pp. 1-30
Author(s):  
Ümit Yerlikaya ◽  
R.Tuna Balkan
Keyword(s):  
Path Planning ◽  
Extreme Environment ◽  
Configuration Spaces ◽  
Planning Problem ◽  
Different Types ◽  
Planning Algorithm ◽  
Line Path ◽  
Path Planning Algorithm ◽  
Path Planning Problem ◽  
System Operating

Abstract Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.

scholarly journals HDDL: An Extension to PDDL for Expressing Hierarchical Planning Problems

2020 ◽  
Vol 34 (06) ◽  
pp. 9883-9891 ◽  
Author(s):  
Daniel Höller ◽  
Gregor Behnke ◽  
Pascal Bercher ◽  
Susanne Biundo ◽  
Humbert Fiorino ◽  
...  
Keyword(s):  
Planning System ◽  
Hierarchical Planning ◽  
Planning Systems ◽  
Description Language ◽  
Input Language ◽  
Common Input ◽  
Modeling Process ◽  
Planning Problems ◽  
Domain Independent ◽  
Tailored Advice

The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and – much more important – also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems.

Multi-Car Convex Feasible Set Algorithm in Trajectory Planning

2020 ◽  
Author(s):  
Jing Huang ◽  
Changliu Liu
Keyword(s):  
Convex Optimization ◽  
Quadratic Programming ◽  
Real Time ◽  
Trajectory Planning ◽  
Autonomous Driving ◽  
Curse Of Dimensionality ◽  
Planning Problem ◽  
Feasible Set ◽  
Planning Algorithm ◽  
Vehicle Trajectory

Abstract Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.

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