Institute for Systems Research Technical Reports

In multi-stage molding process, the desired multi-material object is produced by carrying out multiple molding operations in a sequence, adding one material in the target object in each mold-stage.

We model multi-material objects as an assembly of single-material components. Each mold-stage can only add one type of material. Therefore, we need a sequence of mold-stages such that (1) each mold-stage only adds one single-material component either fully or partially, and (2) the molding sequence completely produces the desired object.

In order to find a feasible mold-stage sequence, our algorithm decomposes the multi-material object into a number of homogeneous components to find a feasible sequence of homogeneous components that can be added in sequence to produce the desired multi-material object.

Our algorithm starts with the final object assembly and considers removing one component either completely or partially from the object one-at-a-time such that it results in the previous state of the object assembly. If the component can be removed from the target object leaving the previous state of the object assembly a connected solid then we consider such decomposition a valid step in the stage sequence. This step is recursively repeated on new states of the object assembly, until the object assembly reaches a state where it only consists of one component.

When an object-decomposition has been found that leads to a feasible stage sequence, the gross mold for each stage is computed and decomposed into two or more pieces to facilitate the molding operation. We expect that our algorithm will provide the necessary foundations for automating the design of multi-stage molds and therefore will help in significantly reducing the mold design lead-time for multi-stage molds.

In this paper, we propose the idea of process planning for small batch manufacturing, i.e., we simultaneously consider multiple parts and exploit opportunities for sharing manufacturing resources such that the process plan will be optimized over the entire set of parts. In particular, we discuss a geometric algorithm for multiple part cutter selection in 2-1/2D milling operations.

We define the 2-1/2D milling operations as covering the target region without intersecting with the obstruction region. This definition allows us to handle the open edge problem. Based on this definition, we first discuss the lower and upper bond of cutter sizes that are feasible for given parts. Then we introduce the geometric algorithm to find the coverable area for a given cutter. Following that, we discuss the approach of considering cutter loading time and changing time in multiple cutter selection for multiple parts. We represent the cutter selection problem as shortest path problem and use Dijkstra's algorithm to solve it. By using this algorithm, a set of cutters is selected to achieve the optimum machining cost for multiple parts.

Our research illustrates the multiple parts process planning approach that is suitable for small batch manufacturing. At the same time, the algorithm given in this paper clarifies the 2-1/2D milling problem and can also help in cutter path planning problem.

In this paper, we give a geometric algorithm to find the optimal cutters for 2-1/2D milling operations. We define the 2-1/2D milling operations as covering the target region without intersecting with the obstruction region. This definition allows us to handle the open edge problem. Based on this definition, we introduced the offsetting and inverse-offsetting algorithm to find the coverable area for a given cutter. Following that, we represent the cutter selection problem as shortest path problem and discuss the lower and upper bond of cutter sizes that are feasible for given parts. The Dijkstra's algorithm is used to solve the problem and thus a set of cutters is selected in order to achieve the optimum machining cost.

We believe the selection of optimum cutter combination can not only save manufacturing time but also help automatic process planning.

1. We give a general definition of the problem as the task of covering a target region without interfering with anobstruction region. This definition encompasses the task of milling a general 2-D profile that includes bothopen and closed edges.

2. We discuss three alternative definitions of what it means for a cutter to be feasible, and explain which of thesedefinitions is most appropriate for the above problem.

3. We present a geometric algorithm for finding the maximal cutter for 2-D milling operations, and we show thatour algorithm is correct.

Our methodology has the following three benefits over the state-of-the-art. First, by using multi-piece molds we can create complex 3D objects that are impossible to create using traditional two piece molds. Second, we make use of sacrificial molds. Therefore, using multi-piece sacrificial molds, we can create parts that pose disassembly problems for permanent molds. Third, mold design steps are significantly automated in our methodology. Therefore, we can create the functional part from the CAD model of the part in a matter of hours and so our approach can be used in small batch manufacturing environments.

The basic idea behind our mold design algorithm is as follows. We first form the desired gross mold shape based on the feature-based description of the part geometry. If the desired gross mold shape is not manufacturable as a single piece, we decompose the gross mold shape into simpler shapes to make sure that each component is manufacturable using CNC machining. During the decomposition step, we account for tool accessibility to make sure that (1) each component is manufacturable, and (2) components can be assembled together to form the gross mold shape. Finally, we add assembly features to mold component shapes to facilitate easy assembly of mold components and eliminate unnecessary degree of freedoms from the final mold assembly.

Evaluating the manufacturability of a proposed design involves determining whether or not it is manufacturable with a given set of manufacturing operations - and if so, then finding the associated manufacturing efficiency. In this research, the design is represented as a solid model. The tolerance and surface finish information is represented as attributes of various faces of the solid model. Machining features are used to model the available machining operations Since there can be several different ways to manufacture a proposed design, this requires considering alternative ways to manufacture it, in order to determine which one best meets the design and manufacturing objectives.

The approach developed in this thesis is based on the systematic exploration of various machining plans. The first step is to identify all machining features which can potentially be used to machine the given design. Using these features, different machining plans are generated. Each time a new plan generated, it is examined to find whether it can produce the desired design tolerances. If a plan is found to be capable of meeting the tolerance specifications, then its rating is computed. If no machining plan can be found that is capable of producing the design, then the design cannot be machined using the given set of machining operations; otherwise, the manufacturability rating of the design is computed. Since various alternative ways of machining the part are considered in this approach, the conclusions about the manufacturability are more realistic compared to the approach where just one alternative is considered.

It is anticipated that this research will help in speeding up the evaluation of new product designs in order to decide how or whether to manufacture them. Such a capability will be useful in responding quickly to changing demands and opportunities in the marketplace.

In this paper, we survey of current state of the art in automated manufacturability analysis. We describe the two dominant approaches to automated manufacturability analysis and overview representative systems based on their application domain. Finally, we attempt to expose some of the existing research challenges and future directions.

In this paper, we provide a survey of current state of the art in automated manufacturability analysis. We present the historical context in which this area has emerged and outline characteristics to compare and classify various systems. We describe the two dominant approaches to automated manufacturability analysis and overview representative systems based on their application domain. We describe support tools that enhance the effectiveness of manufacturability analysis systems. Finally, we attempt to expose some of the existing research challenges and future directions.

To achieve any improvement in setup time, first we need to estimate the setup time accurately. In this paper we propose a methodology to estimate the setup time for machining prismatic parts in a three axis vertical machining center. We consider three major factors in estimating the number of setups, namely---the precedence constraints among machining operations, the feasibility of work holding using vise clamping, and the availability of datum faces for locating the workpiece.

Even after one has decided upon al abstract set of features to use for representing manufacturing operations, the set of feature instances used to represent a complex part is by no means unique. For a complex part, many (sometimes infinitely many) different manufacturing operations can potentially be used to manufacture various portions of the part - - and thus many different feature instances can be used to represent these portions of the part. Some of these feature instances will appear in useful manufacturing plans, and others will not. If the latter feature instances can be discarded at the outset, this will reduce the number of alternative manufacturing plans to be examined in order to find a useful one. Thus, what is required is a systematic means of specifying which feature instances are of interest.

This paper addresses the issue of alternatives by introducing the notion of primary feature instances, which we contend are sufficient to generate all manufacturing plans of interest. To substantiate our argument, we describe how various instances in the primary feature set can be used to produce the desired plans. Furthermore, we discuss how this formulation overcomes computational difficulties faced by previous work, and present some complexity results for this approach in the domain of machined parts.