Elsevier

Computer-Aided Design

Volume 35, Issue 9, August 2003, Pages 825-839
Computer-Aided Design

Algorithms for selecting cutters in multi-part milling problems

https://doi.org/10.1016/S0010-4485(02)00110-0Get rights and content

Abstract

This paper describes geometric algorithms for automatically selecting an optimal sequence of cutters for machining a set of 2.5-D parts. In milling operations, cutter size affects the machining time significantly. Meanwhile, if the batch size is small, it is also important to shorten the time spent on loading tools into the tool magazine and establishing z-length compensation values. Therefore, in small-batch manufacturing, if we can select a set of milling tools that will produce good machining time on more than one type of parts, then several unnecessary machine-tool reconfiguration operations can be eliminated. In selecting milling cutters we consider both the tool loading time and the machining time and generate solutions that allow us to minimize the total machining time. In this paper we first present algorithms for finding the area that can be cut by a given cutter. Then we describe a graph search formulation for the tool selection problem. Finally, the optimal sequence of cutters is selected by using Dijkstra's shortest path planning algorithm.

Introduction

Increasing emphasis on more personalized products and shrinking product lives is resulting in major changes in manufacturing practices [1]. Increasingly, the manufacturing industry is moving towards high part mixes, which makes it important to reduce setup and tooling operations. For example, if a machine-tool is not configured to accommodate more than one part within a part family, then large amount of time will repeatedly be spent on reconfiguring the machine-tool (i.e. loading new tools and fixtures into the machine-tool) each time a request is received for manufacturing a different part. Such reconfigurations are the major source of inefficiency in small batch manufacturing.

If the machine-tool were configured from the beginning to accommodate several different parts within the part family, much of the cost of reconfiguring the machine-tool could be avoided. This will require considering all of the parts that need to be produced during the given operational period, and selecting tools and machine-tool configurations that can work for multiple different parts.

Human process planners and machine operators are already trying to create multi-use setups and machine-tool configurations and to exploit every opportunity for reusing tools and fixtures that have already been loaded into machine-tools. Here are two examples:

  • In the sheet-metal industry, when given a new part, machine operators often analyze the previous machine-tool configurations to see how they can make use of portions of the existing configuration for the new part. In some cases, they even will intentionally plan configurations that will be useful for multiple parts.

  • For CNC machining operations, operators often try to use the tools that are already loaded into the tool magazine. When they need to produce several different types of parts, they try to select a set of tools that can be used to produce all parts, and load all tools into the tool magazine before starting the machining operation for the first part.

In the milling operation domain, it is well known that the size of the milling cutters significantly affects the machining time. Therefore, in order to perform milling operations efficiently, we need to select a set of milling cutters with optimal sizes. It is difficult for human process planners to select the optimal or near optimal set of milling cutters due to complex geometric interactions among tools size, part shapes, and tool trajectories. Furthermore, in small batch manufacturing, both tool loading time (i.e. the time spent on loading tools into the tool magazine) and machining time (i.e. the time spent on performing milling operations) are equally important.

Most existing cutter selection algorithms select milling cutters by minimizing the machining time and do not account for tool loading time. In most cases, the existing algorithms will recommend using a different set of cutters for each new type of part. Since most machine-tools can only hold a limited number of tools at one time, this means that we will need to reconfigure the machine-tool (i.e. we will need to change the set of tools in the tool magazine) before machining each new type of part. When the batch size is small, reconfiguring the machine-tool before machining each type of part may significantly reduce the throughput. However, if we can select a set of tools that can be used for more than one type of part, then several unnecessary machine-tool reconfiguration operations can be eliminated, thereby increasing the throughput.

This paper describes geometric algorithms for finding an optimal set of milling cutters for machining a given set of parts. In selecting milling cutters we consider both the tool loading time as well as machining time and generate solutions that allow us to minimize the total manufacturing time. Our tool selection algorithm improves upon the previous work in this area, in the following manner: (1) in selecting cutters it accounts for tool loading time, and (2) it can simultaneously consider multiple different parts and select the optimal set of cutters to minimize the total manufacturing time.

Currently our algorithm is restricted to 2.5-D milling operations. In particular, we consider the problem of selecting a sequence of cylindrical cutters to cut all of the points in a 2.5-D target region without cutting any of the points in a 2.5-D obstruction region.

Section snippets

Multi-part process planning

Alva and Gupta [2] studied the problem of selecting shared bending punches. In sheet-metal bending, bends are formed using a combination of a punch and a die. These tools need to be able to withstand the bending forces, and their shapes should be such that there is no tool-part interference. The methodology for automatically synthesizing shapes of bending punches involves the following three steps:

  • 1.

    Extract constraints on punch parameters, by performing intersection checks between geometric

Background and basic definitions

The milling problem is the problem of taking one or more pieces of stock and using a sequence of one or more milling operations to remove portions of each piece of stock, in order to produce some desired set of parts. Each milling operation is performed using a milling cutter, and our research focuses on the geometric aspects of selecting those cutters. In previous work [12], we looked at the case where only one milling operation was to be used, and developed an algorithm for finding the

Algorithm for extracting target region and obstruction region

To select tools automatically, we will need to extract the target and obstruction region from the CAD model. To see how we extract the target region and obstruction region, consider example shown in Fig. 2, in which we have a 3D model of a rectangular part whose faces are parallel to the xy, yz, and xz planes, and we also have its initial stock which is allocated in the same manner. Therefore, by subtracting the final part from its stock, we get its delta-volume, which is a single feature as

Algorithms for finding coverable area for a given cutter

In order to solve the multi-part cutter selection problem, an important step is to find the coverable region and calculate the coverable area for each of the cutters C1,…,Cn. This section describes geometric algorithms for calculating the coverable area for a given feature and tool combination.

Algorithm for finding optimal sequence of cutters for multi-part

In cutter selection problems, we are given a set of parts associated with corresponding stocks, and a set of available cutters. We need to select a subset of the initial set of cutters such that by using the subset to perform machining operations, the given set of parts can be produced from the corresponding stocks in the shortest possible total machining time.

Recall from Section 3 that we are given a sequence of cutting tools (C1,C2,…,Cn), listed in decreasing order of cutter radii; we are

Implementation and examples

We have implemented our algorithm, using C++ and the ACIS® kernel. Following are two examples.

Summary

In order to stay competitive in today's market, companies need to eliminate as many sources of manufacturing inefficiency as possible. One such source of inefficiency comes from unnecessary machine-tool reconfiguration operations.

In this paper, we describe a way to select an optimal set of cutting-tool sizes such that the cutting tools can be used for multiple different parts, thereby eliminating unnecessary machine-tool reconfigurations. In particular, this paper describes the following new

Acknowledgements

This research has been supported by NSF grant DMI9896255, DMI0093142, NSF Equipment Grant EIA9986012, and ONR grant N000140010416. Opinions expressed in this paper are those of authors and do not necessarily reflect opinion of the sponsors.

Zhiyang Yao is a PhD student in Mechanical Engineering Department in University of Maryland, College Park. He received a BS in 1995 and an MS in 1998 in Mechanical Engineering from Tsinghua University, People's Republic of China. His research interests are Computer-Aided Design and Manufacturing, Concurrent Engineering, Geometric Reasoning. Particularly, he is working on constructing innovative process plan that can significantly reduce time-to-market and enable cost effective small batch

References (18)

There are more references available in the full text version of this article.

Cited by (37)

  • Optimization of cutter profile for achieving maximum stiffness in five-axis milling of deep and narrow channel parts

    2019, Journal of Manufacturing Processes
    Citation Excerpt :

    Chen et al. [6] proposed a method to calculate the maximum cylindrical cutter without any pocket interference, which was later extended by using the method of central axis transformation [7]. The geometries in the above research works [4–7] are mostly wide-open, while they usually concentrate on the relationship between a closed curve and a circle in a plane. However, when elevated to multi-axis machining of free-form surfaces, the interference relationship between the 3D cutter shape and a free-form surface becomes much more complicated.

  • An optimal approach to multiple tool selection and their numerical control path generation for aggressive rough machining of pockets with free-form boundaries

    2011, CAD Computer Aided Design
    Citation Excerpt :

    Yao et al. [14] proposed a geometric algorithm to determine the largest feasible cutter size for 2D milling operations. Moreover, Yao et al. [15] presented geometric algorithms for automatically selecting an optimal sequence of cutters for machining a set of 2 1/2-dimensional parts. Zhang and Li [16] tried to select multiple tools to achieve the optimal roughing of pockets with arbitrary shape in terms of the minimum machining time and the maximum material removal rate.

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

    2006, Robotics and Computer-Integrated Manufacturing
View all citing articles on Scopus

Zhiyang Yao is a PhD student in Mechanical Engineering Department in University of Maryland, College Park. He received a BS in 1995 and an MS in 1998 in Mechanical Engineering from Tsinghua University, People's Republic of China. His research interests are Computer-Aided Design and Manufacturing, Concurrent Engineering, Geometric Reasoning. Particularly, he is working on constructing innovative process plan that can significantly reduce time-to-market and enable cost effective small batch manufacturing. He has authored or co-authored 14 articles in journals, conference proceedings, and technical reports. He is a student member of ASME.

Satyandra K. Gupta is an Associate Professor in Mechanical Engineering Department and the Institute for Systems Research. He received a PhD in Mechanical Engineering from the University of Maryland at College Park in 1994. Prior to joining the University of Maryland, he was a Research Scientist in the Robotics Institute and an Adjunct Assistant Professor of Manufacturing in the Graduate School of Industrial Administration at Carnegie Mellon University. The objective of Dr Gupta's research is to develop new algorithms for creating next-generation computer-aided design and manufacturing (CAD/CAM) systems that can reduce time-to-market, enable cost effective small batch manufacturing, and facilitate manufacturing of geometrically complex heterogeneous objects. Dr. Gupta is a member of American Society of Mechanical Engineers (ASME) and Society of Manufacturing Engineers (SME). He has authored or co-authored more than eighty articles in journals, conference proceedings, and book chapters. He has organized several conference sessions in the area of computer-aided design and manufacturing. He has served as Program Co-Chair in 1998 ASME's Design for Manufacturing Conference, Papers Chair in 1999 ASME's Design for Manufacturing Conference, and Exhibit Chair in 2000 ASME's Design Engineering Technical Conferences. Dr Gupta has won many honors and awards for his research contribution to computer-aided design and manufacturing area. In particular, he has been awarded a Graduate School Fellowship and an Institute for Systems Research Graduate Fellowship during his PhD study at University of Maryland. Other awards received by Dr Gupta include Best Paper Award in ASME's International Conference on Computers in Engineering in 1994, Best Paper Award in ASME's Design for Manufacturing Conference in 1999, ONR's Young Investigator Award in 2000, SME's Robert W. Galvin Outstanding Young Manufacturing Engineer Award in, NSF's CAREER Award in 2001.

Dana S. Nau is a professor at the University of Maryland, with a joint appointment in the Department of Computer Science and the Institute for Systems Research, and affiliate appointments in the Institute for Advanced Computer Studies and the Department of Mechanical Engineering. His research interests include AI planning and searching, and computer-integrated design and manufacturing. He received his PhD from Duke University in 1979, where he was an NSF graduate fellow. He has more than 250 technical publications. He has received an NSF Presidential Young Investigator award, the ISR Outstanding Faculty award, and several ‘best paper’ awards. He is a Fellow of the American Association for Artificial Intelligence (AAAI).

View full text