Selecting an optimal tool sequence for 2.5D pocket machining while considering tool holder collisions

Milling using a sequence of tools has become very attractive with the advent of rapid tool change mechanisms in modern CNC machines. However, the commercial CAM systems used to generate G&M code rely on experienced process planners to select a good tool sequence. When a tool sequence is selected and tool paths are generated, NC-verify systems are used to check the tool paths for tool holder collisions. If tool holder collisions are detected, the part has to be re-planned ab-initio. In this paper, we describe a method to select an optimal tool sequence by formulating the problem under certain assumptions as the shortest path solution to a single source directed acyclic graph. Also described is a method to incorporate tool holder solution in the context of selecting an optimal tool sequence. Examples have been worked out to illustrate the workings of the algorithm. © Springer Science+Business Media, LLC 2006

Tool sequence selection for 2.5-D pockets with uneven stock

This paper describes on algorithm to select the cheapest tool sequence for machining 2.5-D pockets using the milling process when the stock is uneven (non cylindrical). Uneven stock is generated when multiple setups are used to machine a prismatic part. Even though the pockets have flat bottom faces, the amount of material to be removed will vary along the depth of the pocket. This research has developed algorithms for finding accessible areas for tools, and pocket decomposition when the stock is un-even. Finally, it is shown that tool sequence selection problem can be formulated as the shortest path problem in a single-source, single-sink directed acyclic graph

On setup level tool sequence selection for 2.5-D pocket machining

This paper describes algorithms for efficiently machining an entire setup. Previously, the author developed a graph based algorithm to find the optimal tool sequence for machining a single 2.5-axis pocket. This paper extends this algorithm for finding an efficient tool sequence to machine an entire setup. A setup consists of a set of features with precedence constraints, that are machined when the stock is clamped in a particular orientation. The precedence constraints between the features primarily result from nesting of some features within others. Four extensions to the basic graph algorithm are investigated in this research. The first method finds optimal tool sequences on a feature by feature basis. This is a local optimization method that does not consider inter feature tool-path interactions. The second method uses a composite graph for finding an efficient tool sequence for the entire setup. The constrained graph and subgraph approaches have been developed for situations where different features in the setup have distinct critical tools. It is found that the first two methods can produce erroneous results which can lead to machine crashes and incomplete machining. Illustrative examples have been generated for each method

Applications of genetic algorithms in process-planning: Tool sequence selection for 2.5D pocket machining

Rapid tool change mechanisms in modern CNC machines have enabled the use of multiple tools (sequence of tools) to machine a given pocket. Larger diameter tools that have higher material removal rates are used to clear large open spaces, smaller tools are used for clean up. The challenge lies in selecting that particular combination of tools that minimizes total cost. Previously, we developed algorithms based on network optimization to find the best tool sequence given a list of cutters, cutting parameters and pocket geometry. The formulation was based on certain assumptions that did not account for tool holder geometry. It also required the evaluation of all possible tool-pair combinations for a given tool set. This can get time consuming if the tool set is large. In this paper, we present a genetic algorithm based method to select optimal tool sequences. The algorithm was implemented and bench marked against the graph algorithm. We have found that the GA based method is able to find a near optimal tool sequence without evaluating up to 30% of all possible tool-pairs. Copyright © 2006 by ASME

Finding the maximal turning state of an arbitrary mesh

This paper presents a new algorithm for computing the maximal turning state of an arbitrary mesh. This is accomplished by intersecting the mesh with a plane at even intervals along the mesh, and computing the farthest point in that plane from an axis of rotation. The algorithm will run in O(|E|*n) time and θ(n) space, where E is the list of the edges on the mesh and n is the number of intervals used. Because of this, a very high degree of accuracy can be obtained with very little computational cost. The developed algorithm has been implemented and tested on some meshed models. Copyright © 2007 by ASME

Real time machinability analysis of free form surfaces on the GPU

In this paper a new hardware accelerated method is presented to evaluate the machinability of free-form surfaces. This method works on tessellated models that are commonly used by CAD systems to render three-dimensional shaded images of solid models. Modern Graphics Processing Units (GPUs) can be programmed in hardware to accelerate specialized rendering techniques. In this research, we have developed new algorithms that utilize the programmability of GPUs to evaluate machinability of free-form surfaces. The method runs in real time on fairly inexpensive hardware ( \u3c $600), and performs well regardless of the surface type. The complexity of the method is dictated by the size of the projected view of the model. The proposed method can be used as a plug-in in a CAD system to evaluate manufacturability of a part at early design stages. The efficiency and the speed of the proposed method are demonstrated on some complex objects. Copyright © 2007 by ASME

Improved Binary Space Partition merging

This paper presents a new method for evaluating boolean set operations between Binary Space Partition (BSP) trees. Our algorithm has many desirable features, including both numerical robustness and O (n) output sensitive time complexity, while simultaneously admitting a straightforward implementation. To achieve these properties, we present two key algorithmic improvements. The first is a method for eliminating null regions within a BSP tree using linear programming. This replaces previous techniques based on polygon cutting and tree splitting. The second is an improved method for compressing BSP trees based on a similar approach within binary decision diagrams. The performance of the new method is analyzed both theoretically and experimentally. Given the importance of boolean set operations, our algorithms can be directly applied to many problems in graphics, CAD and computational geometry. © 2008 Elsevier Ltd. All rights reserved

Applications of genetic algorithms in process planning: Tool sequence selection for 2.5-axis pocket machining

Tool sequence selection is an important activity in process-planning for milling and has great bearing on the cost of machining. Currently, it is accomplished manually without consideration of cost factors a priori. Typically, a large tool is selected to quickly generate the rough shape and a smaller clearing tool is used to generate the net-shape. In this paper, we present a new systematic method to select the optimal sequence of tool(s), to machine a 2.5-axis pocket given pocket geometry, a database of cutting tools, cutting parameters, and tool holder geometry. Algorithms have been developed to calculate the geometric constructs such as accessible areas, and pocket decomposition, while considering tool holders. A Genetic Algorithm (GA) formulation is used to find the optimal tool sequence. Two types of selection mechanisms namely Elitist selection and Roulette method are tested. It is found that the Elitist method converges much faster than the Roulette method. The proposed method is compared to a shortest-path graph formulation that was developed previously by the authors. It is found that the GA formulation generates near optimal solutions while reducing computation by up to 30% as compared to the graph formulation. © 2008 Springer Science+Business Media, LLC

N-Rep: A neutral feature representation to support feature mapping and data exchange across applications

The proliferation of different feature based systems has made feature data exchange an important issue. Unlike geometry data exchange, where different representations use the same fundamental concepts; the most popular being B-Rep and CSG [Shah et al. 88], different feature representation schemes use different concepts to represent features corresponding to the application and domain. Therefore, feature data transfer between applications not only involves transfer of instance data but also transformation of feature concepts. This paper presents N-Rep, an application independent declarative language, for feature definition that includes topology, topological relationships, geometry, geometric relationships, parameters and parametric relationships. N-Rep has been designed to serve three roles, viz., (a) to generate feature recognition algorithms for recognizing features from geometry, (b) to generate feature producing procedures to be used in design by feature approaches, and (c) to serve as a neutral feature data exchange medium between representations

Data-parallel techniques for agent-based tissue modeling on graphics processing units

Agent-Based Modeling has been recently recognized as a method for in-silico multi-scale modeling of biological cell systems. Agent-Based Models (ABMs) allow results from experimental studies of individual cell behaviors to be scaled into the macro-behavior of interacting cells in complex cell systems or tissues. Current generation ABM simulation toolkits are designed to work on serial von-Neumann architectures, which have poor scalability. The best systems can barely handle tens of thousands of agents in real-time. Considering that there are models for which mega-scale populations have significantly different emergent behaviors than smaller population sizes, it is important to have the ability to model such large scale models in real-time. In this paper we present a new framework for simulating ABMs on programmable graphics processing units (GPUs). Novel algorithms and data-structures have been developed for agent-state representation, agent motion, and replication. As a test case, we have implemented an abstracted version of the Systematic Inflammatory Response System (SIRS) ABM. Compared to the original implementation on the NetLogo system, our implementation can handle an agent population that is over three orders of magnitude larger with close to 40 updates/sec. We believe that our system is the only one of its kind that is capable of efficiently handling realistic problem sizes in biological simulations. Copyright © 2008 by ASME