IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 12, NO. 3, AUGUST 1995 233

The Use of Visual Modeling in Designing a Manufacturing Process for Advanced Composite Structures

Mark S. Alabastro, Gerd Beckmann, Gina Gifford, Anne P. Massey, and William A. Wallace, Member, IEEE

Abstract-Visual modeling is the depiction of abstractions of re- ality using visual representation of the phenomena being studied. These visual representations include graphs, maps, flowcharts, and pictures. Iconic programming includes the construction and implementation of models using this representation. The result can be readily exercised and assessed by experts, thereby helping to ensure the accuracy of the model and its usefulness in support- ing the relevant decision process.

This paper discusses visual modeling and demonstrates its usefulness in a case study of the design of a manufacturing process involving composite materials and associated manufacturing tech- nologies.

I. INTRODUCTION

HE design of a manufacturing process involving new T materials requires eliciting and then synthesizing tech- nical knowledge from a variety of sources: scientific and engineering literatures (Le., book knowledge), laboratory or pilot experimentation, and experts (i.e., those knowledge- able about the new materials and the present manufacturing process). The most difficult part of this modeling process is establishing a new common frame of reference or language to facilitate communications. Any methodology that can improve the communications will reduce the time needed for the modeling process. Simplifying the communications and using a common language for all three types of knowledge, (Le., book, experimental, and expert) will reduce the number of iterations needed for clarification, elaboration and correction in the process. It will also result in an integration of the three types of knowledge into a single knowledge base. One method of improving the communications and helping to insure in- tegration is the use of computer generated visuals. Visual modeling enables the construction of abstract representations using graphical descriptions of the phenomena being studied.

Manuscript received June 18, 1993. Review of this manuscript was pro- cessed by Editor D. Genvin. This work was supported in part by The US Army’s Manufacturing Methods Technology Project.

M. Alabastro is with the Computer Science, Rensselaer Polytechnic Insti- tute, Troy, NY 12180-3590 USA.

G. Beckmann was with the New York State Center for Advanced Tech- nology, Rensselaer Polytechnic Institute, Troy, NY 12180-3590 USA; he is presently head of Beckmann Engineering, Troy, NY 121 80.3590 USA.

G. Gifford is with the Arthur Andersen Consulting, New York, NY IO001 USA.

A. Massey is with the School of Management, Clarkson University, Potsdam, NY 13676 USA.

W. A. Wallace is with Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, NY 12180-3590 USA.

IEEE Log Number 9413795.

These “pictures” communicate the underlying knowledge in a form that can be understood by “experts” in various fields, e.g., design engineers, material scientists, and manufacturing personnel.

Recent advances in modeling technology provide the ca- pability to automate the process of visual modeling. Besides presenting iconic depictions, the technology can capture and process symbolic and numeric relationships. Most modeling packages now support graphical output, and many also support full animation to show the process flows, current work in process, queues, and even statistics on performance measures of interest throughout the system. This allows the modeler to study the process better and visually identify potential problems, a process critical to validating a model.

This paper will describe the use of visual modeling through- out the process of designing a manufacturing process for the fabrication of composite overwrapped gun tubes. We will demonstrate the usefulness of visual modeling, in various forms, from the earliest stages of model conceptualization to formalization and finally exercising. Visual modeling is used as a means of communication between the model builders and the “owners” of the manufacturing process. Visual modeling allows the owners of the process to be active participants in the development process. The following section presents the visual modeling approach. Section I11 describes the case setting and the fabrication of composite overwrapped gun tubes. Additionally, using the case for explanatory purposes, each component of the modeling process, Le., conceptualization, formalization, and exercising and learning, is described. The final section, Section IV, presents the conclusions and an assessment of visual modeling as an aid throughout the design process.

11. VISUAL MODELING

Modeling is the process of developing and providing an abstraction of reality, Le., model. The character and result of this process is dependent upon how one intends to use the model. If we wish to use the model to provide us with normative guidelines, i.e., what we ought to be doing, the model will be deductive in nature and may represent a very idealized view of what reality is actually like. Conversely, if we are concerned with describing reality, Le., developing a model of what is actually occurring, then we utilize various inductive techniques, the most prominent being statistical

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analysis. In recent years. due to the advent of advanced computer technology. we have been able to develop models that incorporate human judgment and experience. This for- malization is typically called a “knowledge representation” scheme. Such representations are in fact models that attempt to describe the thinking processes of humans about a particular problem [l].

Designing a new manufacturing process means investigating a process that has yet to be realized. Therefore, statistical methodologies cannot be employed to any great degree. Also, the nature of a manufacturing process as a complex system is such that it cannot be described as deductive. i.e.. based solely upon scientific theories. Therefore. the modeler has to elicit and formalize the judgment and experience of “experts” in constructing a “model.” The process of modeling can be described as consisting of three components: 1 ) conceptu- alization: identifying, defining and formulating the problem: 2) formalization: The translation of that conceptualization into a formal structure, e.g., a mathematical model. and 3) exercising and learning: The process of interacting with the model to first capture the desired degree of abstraction, and then to address the various scenarios based upon the problem. e.g. alternative manufacturing methods. When this modeling process requires communicating with either a team of mod- elers or people knowledgeable in the problem domain. visual representations are extremely helpful. For example, graphs and diagrams are excellent means of conveying relationship among variables: flow charts are a relied upon way of describing processes; and sketches of a physical setting can facilitate communications.

The most difficult and time consuming part of the modeling process is the elicitation and subsequent representation of human judgment and experience, i.e., expert knowledge. The modeler, during this process, must also reconcile opposing points of view when dealing with multiple system experts. Visual modeling is a knowledge acquisition approach that capitalizes on the capability to use pictures to facilitate the communications process. These “pictures.” e.g., graphs, icons, and figures, are used to elicit the experts’ representation of reality. The experts representation of reality is in fact. their individual “mental model” of the task or situation.

A. Meritcil and Concrptual Models

Humans constantly maintain mental models of the world around them. A mental model can be defined as an “internal mental replica that has the same ‘relation-structure’ as the phe- nomena it represents” [ 2 ] . Psychologists have long debated on how these models are stored structurally in the mind. Theory suggests. rather intuitively, that one way of storing them is through visual imagery [2]. In light of this, visual imagery can be considered a necessary component of cognition as complex systems, e.g., a manufacturing process. are conceptualized and stored as pictorial mental models. For example, if a production manager was asked to describe the production floor, he or she may verbally describe the layout of the facility as they mentally walk through their internal “picture” of what the facility looks like.

Each person‘s mental model of a system is unique. since mental models are tailored to the individual’s perception and interpretation of the system within their world. This model includes aspects of the world which are important for the individual to emphasize [3] . This emphasis will be determined by the types of decisions to be made and goals to be achieved through the model. In fact, an individual’s mental model will rarely include more detail or information than that which is necessary for it to be useful [2].

A conceptual model has been defined as an instructional representation that aids a learner in producing a mental rep- resentation [4]. Conceptual models can be communicated verbally. textually, or visually. The ultimate goal of a concep- tual model is to stabilize a mental representation and improve one’s interaction with the system it represents. More generally, conceptual models can be thought of as formalizations of men- tal model\. Thus. a conceptual model can be developed as an instructional device, memory aid or communication tool. For example, a blueprint may be considered the conceptualization of the mental model the production manager has of the layout of the production facility. The blueprint, in final form, would not contain erroneous information that the production manager may have in his internal representation.

B. Visiuil Modeling

A visual model can be defined as a model which relies on nontextual, nonverbal elements to communicate state or process descriptions of a system. Modeling the concept “stop” can be achieved by verbal communication, textual communica- tion or vihual communication. The directive “Stop!” is purely verbal. A stop sign is partially textual, but relies on visual constructs (octagonal shape and red color) to communicate the concept. A red light is purely a visual communication tool.

Conceptual models can be visual in nature. When repre- sented in visual form they enhance the clarity of commu- nication between a model designer and expert. Each per- son uses their mental models to verbally communicate their understanding of an object. This verbal communication is limiting when the object, system, or task is complex. By introducing a conceptual model through representations like pictures. diagrams, maps, or flow charts those communicating can interact with a final goal of reaching a common and appropriate mental model of the system. Stakeholders in the process can negotiute with each other and communicate with the model builder. During the conceptualization phase of modeling. the designer and the system expert communicate to each other their mental models of the system. These preconceived models will differ when the system designer is not the expert. The difference is dependent upon the system designer’s familiarity with the problem domain. The model designer approaches the expert with some vague mental imagery about the system. This imagery can come from studying objective sources such as books or documents, or by visualizing the system as a case of a class of systems, e.g., a manufacturing system. The system designer may assume a general system model as a prototype for initial discussion. The system expert’s model emphasizes those constructs which are

ALABASTRO et ul ’ USE OF VISUAL MODELIhG IN DESIGNING A PROCESS FOR ADVANCED COMPOSITE STRUCTURES 235

influential to their day-to-day decision making processes. In the expert’s model, the components of a problem are largely in the form of impressions of the system based on experience. The expert tends to classify, broadly categorize or generalize system constructs as they relate to a decision problem [ 5 ] . The system expert also has access to detail about the system which can be used to enhance the system model. This detail may not be emphasized in the expert’s day-to-day decision making model. However, when conceptualizing a model which proposes a change to the system, the designer must have the system expert reintroduce these formerly extraneous constructs to their mental model. The impact these constructs may have on the new system needs to be explored and documented.

Visual aids can assist verbal communications during the conceptualization phase. When confined to verbal discussion and note-taking, the process of developing a mutually under- stood conceptual model is formidable. First, the discussion session is hard to constrain. The designer may not prompt the expert for required information, or the expert may give detail that they believe to be important but which is outside the scope of the model. Second, the resulting model may be biased. If the designer only prompts the expert for information which the designer deems important, it is biased in favor of the designer’s mental model. If the expert only gives information that the expert deems important, without respect to the problem at hand, the model becomes a replication of the expert’s day-to-day decision making model. This phenomena is called functional fixedness [ 61. Functional fixedness is a consequence of permanently arranging concepts or objects into functional classes and applying the scheme to all problems encountered. A model which classifies system entitles according to des- ignated function may be adequate for one problem definition. However, as the problem requirements change, the function of system entities should be reevaluated and the model updated.

Other problems with strict verbal communication exist. It may be difficult to assess the relative importance of the constructs discussed, to recall these constructs from notes during formalization, and to establish a starting point for future discussions. Most importantly, discussion and note-taking can be uninteresting and alienating to the expert. Without a clear picture of the objectives of the model, the expert my wonder why a question is being asked, where the answer would fit in, and what is being recorded in the designer’s notes.

Visual models of the system during the conceptualization phase significantly increase the speed and quality of model development. First, visual models of the system constrain discussions between expert and designer. An on-paper map, diagram or chart facilitates a “by exception” discussion. At- tention is focused on the visual, and constrained to the level of detail included in the picture. It is immediately apparent to the expert the type and detail of objects included in the conceptual model. This “by exception” discussion proceeds with the expert noting what is absent and important and what is present and Unimportant. This avoids discussion of what is unimportant in the model, or rehashing elements which are already adequately specified.

In addition, the expert can interact with the record of the discussion, Le., the visual. Thus the conceptual model is

“jointly owned.” This increases the expert’s stake in the quality and accuracy of the discussion and allows for uninhibited clarification and revision. After iterating the model with new information, starting points for future discussion are easily established. For the designer, the task of formalizing the model through records of discussion is eased. Since the data defining a system construct is recorded near its depiction on paper, organizing data which is scattered in notes is eliminated.

The availability of a computer environment designed to preserve the pictorial display of system constructs created during conceptualization substantially aids model formaliza- tion. Construct and content validity are improved since the modeling environment can “mimic” the pictorial display used during conceptualization. Referencing is simplified since a piece of information can be traced from code to picture to expert fairly easily. In addition, recurring types of objects and behaviors can be readily identified from the visual and capitalized on during “hard-coding’’ [7].

The final step in the modeling process is exercising and learning, where the formalized model is delivered to the end- user. The model is exercised by the end-user and modified dynamically to reflect new problem requirements or changes to the system. Here visual models are critical in explaining how the model performed and why it performed that way. Output from computer models where many decision criteria are considered can often be overwhelming to the end-user. Visual output in the form of bar-charts, graphs or animations of model performance convey critical information in a quick and comprehensive manner. This visual output can be used to aid the end-user in assessing the optimality of certain performance measures.

By maintaining an understood visual model of the system throughout conceptualization, formalization, and exercising and learning, the modeler provides an effective instrument for decision support. As the visual model converges to the user’s mental representation of the real system, the comprehensibility of the model increases. Additionally, the visual model can be used to validate the “correctness” of the expert’s mental model of the system. That is, while a mental model is always useable it is not always accurate. Therefore, during communication and model building the modeler must validate “correctness.” The modeler may resort to using multiple experts to discuss the resulting system representation. In understanding the model, the user is more apt to trust and exercise it in support of decision making [7].

C. Support for Visual Modeling: Iconic Programming

What enables a modeler to employ visual modeling effec- tively is the availability of technology that permits the use of pictures, i.e., “icons” to create a program. The technology permits interactive modeling with either other modelers or experts in the problem domain. This interactive capability facilitates evaluating different structures of the model using an iconic representation. Iconic programming is a paradigm that uses icons to create a program. Generally, object-oriented programming is used to define the icon, and may rely on this structure for the development of the basic structure of

236 IEEE TRANSACTIONS U\ tNGlNEERlNG MANAGEMENT. VOL. 12 , NO. 3, AUGUST 1995

the iconic language [8]. Object-oriented programming (OOP) takes advantage of object properties such as inheritance and class membership [9]. When an object is defined as an instance or a member of a class of objects, it inherits all or some of the characteristics of its parent or class. This makes programming easier by taking advantage of object repetition and duplication which arises naturally in many models [ IO] . The behaviors of a specific object could be described and invoked within the initial definition of an object. As a result of this, each object only performs activities that were predefined and no external manipulation of the object‘s behavior can occur. The result is the development of objects that behave specifically. for example. an object called lathe, could be developed that stimulates the activity of a lathe and is invoked by using a few parameters without specific knowledge of the internal code. Combined with iconic programming. a model can be formalized into the OOP structure by using graphs. maps, pictures or other visual representations as well as icons.

111. A CASE STUDY: THE FABRICATION OF COMPOSITE GUN TUBES

A. The Pr-ohlei,? Doincrin

The US Army’s Watervliet Arsenal in Watervliet, NY. is facing a complex, ever changing environment, both politically and technically. The end of the Cold War has brought demands for a reduction in the size of the military in terms of manpower and weaponry. Additionally, there are increasing efforts to identify unnecessary and outdated facilities, products. and processes. As a consequence, production facilities such as the Watervliet Arsenal must be proactive in their response in order to remain in operation.

The Watervliet Arsenal has been responsible for producing all-steel tank gun tubes since WWII. Traditional methods of gun tube design and fabrication have involved heavy metalworking technologies. Today’s production levels reflect the end of the Cold War and military cutbacks, focusing primarily on replacement of outdated or defective gun tubes in the field. This study is in response to three questions posed by the Watervliet Arsenal: 1 ) what technologies and materials are available that will allow for the production of more effective gun tubes. i.e., product focus. 2) how should these technologies be implemented for full-scale production. i.e., process focus, and 3) are there dual uses for these technologies in commercial environments. Le., facility focus [ 1 11. Answers to these questions may make the arsenal less prone to defense fluctuations by becoming a flexible state-of-the-art facility.

This study focuses on the second question-the production process. But first, the next section briefly describes the tech- nologies and materials that allow for the production of more effective gun tubes. without which there would be no question about the production process.

B. Effecti1,e Girri Tubes: New Techriologies triid Mater-iuls

Longer tank gun tubes provide system performance ad- vantages. These advantages are not realized without burdens and design complications. A longer all-steel tube changes

the balance of the gun as the center-of-gravity is shifted forward. The increased tube weight and droop alters the tube’s dynamic response. To overcome these complications, a system of replacing a position of the steel wall in the forward section of the tube with a combination of high strength and high modulus graphite fibers in a composite jacket has been proposed. designed and demonstrated.

The present method of fabricating “a composite tube” utilizes a hand lay-up technique and improved prototype processing procedures. High modulus graphite tape segments are hand placed among the prepared surface of the gun tube. These longitudinal fiber layers are alternated with wound hoops of graphite filaments and the process is repeated un- til the desired composite jacket geometry is achieved. The tube/jacket assembly is then instrumented and prepared for curing using the “shrink tape.” Curing is performed in an oven normally used for heat treating conventional tubes. While appropriate for prototype preparation, the present method for fabricating composite tubes is grossly inadequate from a full scale production viewpoint.

C. Project Objectives arid Requirements

As described in the previous section. composites have been successfully used to fabricate gun tubes. The key enabling technologies and materials systems have been selected and used successfully to produce a gun tube which is lighter. However. the methods employed in the fabrication of the first gun tube, precludes their use in full scale production. It was necessary to understand and document the techniques and technologies that must be employed or developed, where needed, to allow full scale production of advanced composite structures in facilities that have previously manufactured high quality, high volume, metal based products.

Traditional methods of gun tube design and fabrication have involved heavy metalworking technologies. The integration of advanced composite material systems into the “heat it and beat it” manufacturing facility will require that all aspects of the procurement, storage, inspection, fabrication, processing, and testing be fully understood. This is necessary because composite materials cannot be worked in the same way as traditional materials. The goal was to fully integrate the fabrication of composite structures into the manufacturing process at the Watervliet Arsenal.

To support this goal, a project was undertaken with objec- tives as follows.

1 ) Gather the information necessary, e.g., present pro- cesses. facilities, materials flow, inspection techniques, etc. to describe the “current” manufacturing process for two products. i.e., 105 and 120 mm long gun tubes.

2) Elicit and evaluate the decision making criteria and processes used to support the current manufacturing process.

3) Using the information above. design and develop a simu- lation model of the “current” manufacturing process, Le., 105 and 120 mm gun tubes. The model would provide a framework within which the composite manufacturing processes can be incorporated. Additionally, this model

ALABASTRO et al.: USE OF VISUAL MODELING IN DESIGNING A PROCESS FOR ADVANCED COMPOSITE STRUCTURES 231

is useful for comparative purposes to analyze the effect of the introduction of composite overwrapped gun tubes into the manufacturing system.

4) Analyze the manufacturing process operations employed by Watervliet Arsenal for fabricating overwrapped gun tubes.

5) Using the results of the analysis in part (d), design and develop a simulation model that can be used to analyze the manufacturing process for fabricating composite overwrapped gun tubes.

The project requirements also stipulated that the design and development, and subsequently system delivery, be accom- plished in a Macintosh environment.

The manufacturing processes needed to fabricate a compos- ite gun tube incorporate eight operations: 1) preparation of the steel gun tube to accept composite material, 2) application of composite material to the steel gun tube, 3) curing. i.e., the use of heat, of the composite material, 4) cleaning or staging process, 5) straightening, 6) chrome plating the bore of the gun tube, 7) nondestructive evaluation during and after cure, and 8) transportation and handling of composite overwrapped gun tubes. Each of these operational areas were addressed with the purpose of aiding in establishing a manufacturing process for fabricating composite overwrapped gun tubes at the Watervliet Arsenal.

D. The Problem: Why Visual Modeling?

As described above, the objectives of the project were to develop simulation models of the current and proposed manufacturing processes. The problem that the researchers, here the model developers, were facing was the fact that there existed no agreement among the stakeholders as to 1) whether the fabrication of composite gun tubes was feasible in the given manufacturing facility, and 2) given that the fabrication was feasible, what the production process should be.

Stakeholders included 1) Benet Laboratories personnel, the Research and Development arm of the Watervliet Arsenal, “owners” of the composite overwrapped gun tube, 2) Water- vliet Arsenal personnel (both at the managerial and operational level), responsible for the current production of all-steel gun tubes, and ultimately responsible for any implementation of new production processes, and 3) the researchers, responsible for eliciting expert knowledge, identifying consistencies and inconsistencies, and developing a working model. Each of the stakeholder groups were deficient in understanding at least some aspect of another stakeholder’s domain.

As a result visual modeling was proposed as the means by which the stakeholders may communicate more efficiently and effectively [12]. The following sections will describe the steps undertaken during the process of developing a simulation model. These steps included interactive structured modeling using visual techniques, e.g., flowcharts and iconic program- ming, and individual and group interviews (with both Benet and Arsenal personnel) in order to understand and communi- cate the current process. Once this model was available and the composite fabrication process was researched, the simulation model that incorporates composite overwrapped gun tubes

was developed. In both individual and group sessions, the visualizations served as a means to focus discussion and communicate the current and proposed process.

I ) The Visual Modeling Tools: While the following sec- tions will detail the modeling process from conceptualization to formalization, and finally exercising and learning with the model, we will briefly describe the visual modeling tools that were used throughout the process.

Based on the project requirement that models be developed and delivered in a Macintosh environment, logical process flow diagrams were constructed using MACFLOW software. The process flow diagrams were used as the basis for “building” the simulation model in EXTEND. EXTEND is a commercial sim- ulation model development tool for the Macintosh distributed by Imagine That, Inc. With EXTEND, a simulation model can be developed as a diagram of graphic object-oriented blocks logically connected together. Blocks are object-oriented icons with scripted behavior and connections passing information between blocks. Dialog boxes let the user interface easily with the block to both input and output data.

A comparable simulation tool would be WITNESS, a prod- uct of AT&T ISTEL Visual Interactive Systems, Inc.. WIT- NESS currently runs in the Microsoft WINDOWS environ- ment. EXTEND and WITNESS both approach the devel- opment of a simulation in almost the opposite direction from more “traditional” simulation products, e.g., SIMAN or GPSSV. Traditional simulation languages, such as SIMAN or GPSSV, revolve around model “coding” languages. With these languages, one “writes” a model. While SIMAN models, for example, can be animated by using the development package called CINEMA, the model must already have been coded-that is, CINEMA cannot be used to build the model. Whereas, in EXTEND or WITNESS one interactively “builds” a model. “Code” development in EXTEND or WITNESS starts with the visualization. In both, a user first defines the entities which embody the model. EXTEND and WITNESS provide templates for a large number of the types of entities which are commonly used in manufacturing applications, e.g., conveyors, buffers, parts, etc. All initial (and many subsequent) model development tasks are accomplished through pull-down menus. Entities become “icons” in this environment. The pull-down menus, in EXTEND called “dialog boxes,” allows the user to define the details associated with the entity. For example, if a single machine is being detailed, details may include machine name, type, labor needs, input process, output process, breakdown process, and so on. Alternatively, if one is detailing a buffer, details may include queue discipline, queue capacity, reporting options, and any actions required.

The purpose of this paper is not to detail the differences between EXTEND (or WITNESS) and traditional simulation languages [13], [14].

E. Model Conceptualization

The current process of manufacturing traditional gun tubes at the Watervliet Arsenal involves more than 100 process steps that primarily work metal. These steps include cutting, swag- ing, honing, sawing, shaping, cleaning, and inspection. This

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, PROCESSES ,L ‘“Y4F’ r - 0 (INSPECT)

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Fig. 1. Example of a process flow diagram

process was detailed, in text version, in process routing sheets. The visual modeling process was initiated by developing a visual representation of the production of 120 mm gun tubes using flow charts to depict the present production process. This logical process flow diagram was constructed using MACFLOW software package (See Fig. 1). The graphical documentation of the production of 120 mm gun tubes was based upon process routing sheets and interviews with manu- facturing experts at Benet Labs and the Watervliet Arsenal.

Using the flow diagram for discussion, individual interviews and group sessions were conducted with Benet and Watervliet personnel to determine “how” composite fabrication could be potentially incorporated into the present process. The flow chart was augmented to incorporate the manufacturing steps needed for the composite materials. The resulting flow charts were then used to develop the final model of the manufacturing process with composite materials.

In this conceptual model, the traditional process steps were given a process number. These numbers correspond to the operation number on the routing sheets for the manufacture of 120 mm tubes. Process step 90, for example, is a honing operation. Since there are three different machines that could be used for the step there are three process blocks in the flow diagram. Composite steps generally have a operation letter. Process step B, for example, is an apply primer step. Some compromise steps were identified, but the data was incomplete. For example, the duration of the processes were not known or their order in the sequence determined. These processes appear as isolated steps in the flow diagram and remind the process planners that the step will be needed approximately at that point in the diagram.

To gain the specificity needed for modeling the gun tubes with composite material, additional detail was obtained. Each step from the flow diagram was broken down into its com- ponents. This work resulted in an object-oriented model to conduct the simulation, programmed in EXTEND. Fig. 2 presents a segment of the model, depicting a honing operation. The process shown follows operation number 80. First the barrels are stacked in a pile. A crane then moves them to one of three honing machines for honing, operation step 90. The model has an object that corresponds to honing. Three copies of this object are included in this piece of the model so that during runs of the model the utilization of each will

HONING IOP #981

QUEUE lf lfter OD #tee)

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Fig. 2. EXTEND representation of three honing objects

be calculated. The model also measures number of barrels traveling through the system and other variables.

From the process flow model a list of objects that needed to be modeled was derived by the modelers. Objects are in four main classes: process steps and their attributes, parts to be manufactured (120 mm tubes, 105 mm tubes, composite tubes, etc.), process machines, and plotting and reporting objects. A model of the manufacturing process was assembled by replicating these objects and connecting them in proper order. This model was then programmed in EXTEND for interaction with the users. This partial model aggregated the traditional gun tube production procedures for operations 0090 to 0200 and their production counterparts in the composite case. The purpose of the partial model in EXTEND was to assess the object-oriented paradigm for modeling the production of gun tubes, and provide “face validity” for our model of the process flow.

The conclusion of the conceptualization phase was that the object-oriented modeling techniques were useful for vi- sualizing the process. Interactions with Benet and Watervliet personnel resulted in continuous changes to the model, as new issues and concerns surfaced. For example, an issue arose con- cerning whether a composite jacket could be transported via a crane. i.e., composites are more fragile than traditional all- steel tubes. This issue arose during a group session as the group interacted with the EXTEND model-participants recognized that the parts, i.e., gun tubes, were being transported via an icon representing a crane.

F. Model Formulization

The modeling process was designed with the capability to elicit domain knowledge, and incorporate it within the structure of the model. A knowledge acquisition session (with seven production personnel from Watervliet Arsenal and three composites experts from Benet Labs, ranging in experience from six to 25 years) was conducted. Personnel represented materials. chemical, and mechanical engineering, as well as project leaders. The purpose of the session was to begin the process of identifying potentially divergent viewpoints on the production of composite gun tubes. The flow diagrams discussed in the previous section were used as the basis for discussion.

The half-day session resulted in a consensus being reached regarding a feasible manufacturing configuration. Comments

ALABASTRO et a/ . : USE OF VISUAL MODELING IN DESIGNING A PROCESS FOR ADVANCED COMPOSITE STRUCTURES 239

PRESENT* (Hours) Configuration

REAL SIMAN EXTEND MACHINE UTILIZATIONS MI: Honing .40 .38 3 7 M2: Extrusion .45 .49 .5 1 M3: Grinding .58 .59 .60 MACHINE QUEUES Q1: Honing 3.37 3.39 3.42 Q2: Extrusion 1.45 I .49 1.50 4 3 : Grinding 4.61 4.55 4.57 FLOWTIME 165.1 163.69 166.52

Feasible PROPOSED** (Hours)

Configuration SIMAN EXTEND

.35 .34

.47 .45

.57 .53

3.10 3.06 1.35 1.37 4.08 4.11

180.27 180.79

by the participants revealed their desire for the simulation model to be able to analyze changes in the present man- ufacturing process as well as various composite configura- tions. In addition, they wanted the model incorporated into a user-friendly manufacturing systems design workstation. Their definition of “user-friendly” included the iconic environment provided by EXTEND. Concurrently, a “comparative” model was constructed in SIMAN (Simulation and Analysis), a general purpose simulation language with specific capabilities, such as materials handling, applicable to modeling manufac- turing systems. SIMAN, although it demonstrated the ability to handle modeling requirements, lacked the characteristics of object-oriented programming. Coding alternative manufac- turing designs required extensive knowledge of SIMAN, a FORTRAN-like procedural language.

A sample of the simulation results for the SIMAN and EXTEND models, for both the present and proposed manu- facturing configurations, are shown in Table I. The present manufacturing configuration results reflect the averages of ten simulation runs (with data collection beginning after six months of system loading) in which one hundred 120 mm gun barrels were produced in each run. As Table I indicates, the SIMAN and EXTEND models were consistent with each other, as well as the real data provided by the Watervliet Arsenal.

The simulation results for the proposed manufacturing con- figuration reflects the preliminary feasible model agreed to during the half-day session with Arsenal and Benet Labs personnel. For each of the ten simulation runs, one hundred composite barrels were produced. At this stage in model development, a mixed production of traditional and composite barrels was not investigated as the Arsenal and Benet Labs were more interested in analyzing the feasibility of the pro- posed composite manufacturing process. As Table I indicates, the SIMAN and EXTEND models produced consistent results and indicated that the proposed configuration would result in a flowtime approximating between 180 and 18 1 h. The increase in flowtime between the traditional barrels (real-time of 165.1 h) and the composite barrels was not due to any significant impacts on present machine utilizations or queues, but rather due to the addition of new operations and slower material handling requirements.

Fig. 3. General distribution dialog box.

As Table I indicates, no significant differences were found between the results of the SIMAN and EXTEND models. This finding is not unexpected, as each model reflected the same manufacturing configurations and processing times. As a result of this and the successful use of EXTEND with Arsenal and Benet Labs personnel, EXTEND was chosen for final formal- ization of the model for both the present manufacturing process and the new process with composite materials. EXTEND met the requirement of a Macintosh environment and its object- oriented approach provided usability and comprehensibility for the end-users. All further efforts with the SIMAN models were discontinued.

G. Model Exercising and Learning

The final model configuration was in EXTEND and run on a Macintosh Quadra workstation [l 11. The model, Arsenal Simulation Model (AMs), with the manufacturing engineering workstation, provides an iconic environment within the object- oriented programming paradigm. Exercising of the model is conducted by Benet Laboratory personnel. Alternative design configurations for fabricating the composite overwrapped gun tubes are “programmed” by using the icons. Objects represent- ing manufacturing technologies were developed and assessed

240 IEEE TF.ANSACTlONS OC ENGINEERING MANA(;EMtN~I ’ . VOL 12. NO. 3, AUGUST 1995

Fig. 4. Primary operational elements

Enter Number of Steps or Period

Repealable Random Sequence # (Zero or blank is Random1

Repeat Simulations

pStepslze Calculations- @ nulostep last, use ‘Stepsize’ caiculotions [may use more steps) 0 Rutortep Slow, use ‘Stepsize’ calculation divided by 5 for accuracy 0 Rlways use entered ‘Number of Steps or Periods’ (‘Stepsize‘ disabledl

“left to right. One step delay per Atti to left connection.

simulotion Method Default laft to right simulation or or No connection delay from

Fig. 5. Run dialog box

for their impact on the manufacturing process. Fig. 3 presents the display for modifying service times that represents their impact .

There are a variety of other capabilities to model changes in the manufacturing process. Resources, e.&., machines, can be added or deleted; operations can be added or deleted; and linages between the operations added or deleted-all in the iconic environment. For example, adding a new operation requires creating the relevant operational elements as show11 in Fig. 4.

Execution of a simulation consists of bringing the pointer to the Run option in the menu bar and highlighting “Run Simulation.” A dialog box appears in which the Start Time, Ending Time, and number of times the simulation is to be ruri. The entire simulation was designed in the time units of hours (See Fig. 5). The output of a sample run of a mixture of 35% 120 mm, 35% of 105 mm and 30% composite overwrapped gun tubes is shown in Fig. 6.

Benet and Arsenal personnel are currently using AMS t 3 build and test alternative process configurations.

;t File Edit Libraries Define Run Font Slule Windows 0) A ‘

: 6375 1275 19125 2550 . Fig. 6. mm, and 30% composite gun tubes.

Throughput time versus simulation time for 3.5% 120 mm, 35% 105

I v . CONCLUDING REMARKS

Manufacturing is seen as a competitive weapon and organi- zations use developments in advanced technologies to gain critical competitive advantages [ 151. However, designing a manufacturing process involving advanced materials requires ascertaining knowledge that may not be available in either the literature or in pilot or experimental results. We have demonstrated how visual modeling can facilitate knowledge acquisition and communication throughout the design process. We used visualization to “build” the model, not just for veri- fication or use. Visual modeling made all stakeholders active participants in the design process, which is very important for a “not yet realized” process. It also increased commitment.

Many design processes require the acquisition of knowledge from “experts.” In addition to the use of “pictures,” this modeling process is iterative; one can build, rebuild, and be able to track the various changes to their source-which often can’t be accomplished with verbal communications. We drew upon the literature in cognition to support this use of visualization.

The model was formalized in an iconic environment using an object-oriented programming paradigm to facilitate under- standability, i.e., openness of the model, and permit easy modification. Building a model in this e vironment consists of 1) obtaining the necessary data descrioing the real-world system: 2 ) determining what icons best simulate a specific resource or behavior; 3) providing a specific icon with the data needed to perform the desired activity; and 4) creating the necessary relationships among the icons to determine the overall behavior of the system. EXTEND, an object oriented software package, was selected over traditional simulation en- vironments to capitalize on the benefits of iconic programming. In EXTEND, model parameters can be changed easily. The addition, deletion, or changing the order of operations andor resources does not require rewriting or modifying the source code.

It must be noted that. while a SIMAN model was concur- rently developed for comparative purposes, the SIMAN model

ALABASTRO er ul: USE OF VISUAL MODELING IN DESIGNING A PROCESS FOR ADVANCED COMPOSITE STRUCTURES 24 1

was not easily modified whereas the EXTEND model could be changed within a matter of minutes. Modification of the SIMAN model required knowledge of the SIMAN language and of programming techniques in addition to the knowledge of the manufacturing process. Conversely, EXTEND required only knowledge of the technology and how it relates to the parameters of certain icons. Pazirandeh and Becker [ 131 argue that a deficiency of traditional simulation programming is that there is no visual benefit and the user has to pore over lines of code to “see” the process. Managers and system designers have tended to treat modeling as an end and an obligation rather than a tool for designing an efficient system. Visual modeling, however, is a useful tool for communication with and between various stakeholders; they can build a model, easily revise it and rebuild in an object-oriented programming environment. Additionally, once elicited and agreed upon, decision making rules can be embedded within the simulation via the object-oriented blocks. Our “customers,” designers and manufacturing engineers, preferred the object oriented interface of EXTEND and specified that configuration for their workstations.

The final configuration of the model, The Arsenal Model Simulator, is being used for continuing analysis of the pro- duction of composite overwrapped products. Management is assessing a variety of products in order to capitalize on their expertise and their investment. A very unstructured review showed that after more than a year, the workstation is being used to develop additional models with visual modeling process: routing sheets to MAC DRAW flow charts to EX- TEND, with both the designers and manufacturing engineers involved in the process.

Facilitating communications between various stakeholders from different organizational units, in our case, R&D at the Benet Laboratories and manufacturing engineers at Benet and at the manufacturing facility, the Watervliet Arsenal. is neces- sary in any attempt at integrating design and manufacturing [17]. Such integration is also necessary for the successful implementation of concurrent engineering [IS].

In conclusion, visual modeling, as supported by software like EXTEND, provides a modeling environment. Users can construct simple but useful representations quickly and easily. These representations can provide insights into the impact of new procedures, products, equipment, materials, etc., which in turn can direct the expenditures of funds for research and capital investment.

ACKNOWLEDGMENT

The authors wish to recognize the invaluable assistance and guidance of Phil Wheeler from Benet Laboratories and Peter Bessette from Watervliet Arsenal.

REFERENCES D. Schmidt. J . Haddock, W. A. Wallace, and R. Wright. “Visual mod- elling: A knowledge acquisition method for intelligent process control systems,” in Proc. 5th h t . Conf on /rid. & Engineering Applications cfArtijciu/ Intell. and E.yprrr Syst., F. Bell. Ed. Heldeberg, Germany: Springer-Verlag. 1992. P. N. Johnson-Laird. Meritul Models: TOwurd.\ u Cognitive Sr.ience of Lnnguage. Inference arid Consciousness. Cambridge, MA: Harvard Univ . Press, 1983.

R. Herman, “Taking the high road,” OWMS Today, pp. 2&25, June 1991. L. A. Cardinale, “Conceptual models and user interaction with comput- ers.” Computers in Human Behavior, vol. 17, pp. 163-169, 1991. 0. S. Yu, “Interface between model and computer models,” Energy, vol. 15, no. 7/8, p. 621, 1990. E. F. Loftus and C. B. Wortman, Psychology, 3rd ed. New York: Knopf, 1988. M. A. Centeno and C. Standridge, “Modeling manufacturing systems: An information-based approach,” in Proc. 24th Annu. Simularion Symp., A. Rutan, Ed. Los Alamitos, CA: IEEE Computer SOC., 1991, pp. 23C239. P. C Bell and R. M. O’Keefe, “Visual interactive simulation--History, recent developments, and major issues,” Simulation, vol. 49, no. 3, pp.

B. LeClair, “Object-oriented: An overview of key concepts,” O M S Today, vol. 18, no. 1, pp. 2&24, 1991. A. Sadashir, “Software modeling of manufacturing systems: The case for an object-oriented programming approach,” Annals Oper. Res., vol. 17, pp. 363-378, 1989. M. Alabastro et al., Manufacturing Methods for Fabricating a Composite O\,ewrap for Gun Tubes: Final Report, The Center for Manuf. Produc- tivity and Tech. Transfer, and Dept. of Decision Sci. and Engineering Syst.. Rensselaer Polytechnic Inst., Troy, NY, May 1992. G. Gifford. A Comparison of Visual Modelling Tools in the Developmenr of u Process Model for Composite Gun Tube Production, M.S. thesis, Decision Sciences and Engineering Syst., Rensselaer Polytechnic Inst., Troy, NY, Dec. 1991. M. Pazirandeh and J. Becker, “Object-oriented performance models with knowledge-based diagnostics,” in Proc. 1987 Winrer Simulation Con&, A. Thensen, H. Grant, and W. D. Kelton, Eds. Piscataway, NJ: IEEE Press, 1987, pp. 518-534. J . J . Swain, “Flexible tools for modeling,” OWMS Today, vol. 20, no. 6. pp. 62-78, Dec. 1993. C . T. Mosier, “A pedagogical contrast of two discrete event simu- lation languages: WITNESS and SIMAN/CINEMA,” Working Paper, Clarkson Univ., Potsdam, NY, 1993. G. W. Mechling and J. W. Pearce, “Exporting and the adoption of advanced manufacturing technologies in small manufacturing firms,” in / Y Y 3 Proc. Decision Sci. Institute, vol. 3, pp. 12441246. N. Balaguer and D. Stoddard, “Design-manufacturing integration and the global context,” in Global Manufacturing: Technological and Economic Opportunities and Research Issues, D. Berg, D. Ome, and W. A. Wallace, Eds. N. Gaither, Production Operations Management, 5th ed. New York: Dryden, 1992.

109-1 16, 1987.

Greenwich, CT: JAI, 1993.

Mark S. Alabastro received B S degree in com- puter science from Rensselaer Polytechnic Institute, Troy, NY, where he is now studying for the M.S. degree in electrical, computer, and systems engi- neenng

He has served as a Research Assistant for Dr. Marthd R Grabowslu in numerous human factors studies His work expenence includes simulation de- velopment and behavioral studies in group decision making His current research interests are human interface design, parallel and concurrent computing,

and computer simulation. Mr Aldbastro is a member of the As3ociation of Computing Machinery

Gerd Beckmann received the B.S. degree in metallurgy from Camegie Mellon University, and the Ph.D. degree in materials engineering from Rensselaer Polytechnic Institute.

He is presently bead of Beckmann Engineering, Troy, NY. He has over 17 years of experience as a Principal Investigator and Associate Director for the Center for Manufacturing Productivity at Rensselaer Polytechnic Institute and AI Tech Speciality Steel.

232 l E E t 'TRANSACTIONS 01 ENGINEERING klANAGEb1ENT. VOL. 32. NO. 3. AUGUST 1995

Gina Gifford received the B.S. degree in management from Clarkwii University and the M.S. degree in industrial and management engineering from Rensselaer Polytechnic Institute.

She is presently a staff engineer at Anderson Consulting in New York City.

Anne P. Masse) recei\ed the Ph D degree in

decision sciences and engineering \y\tem\ trom Rensselaer Polytechnic Institute

She is an Asuistant Professor of Mandgement Information Systems in the School ot Management at Clarkson Unicersit) Her present research interests are in the areas of problem structuring and formulation, indicidual and group communication, the use and application of group support jystemr and visual modeling Her work has been published in DECISION SCIENCEE, IEEE T R A \ ~ A C TIOI\S

ON KNOUIFDFE AND DATA E~GIZEERIYG. the JOLRhaL Or K\O\hLtl)(lt ACQLISITIO~, and the JOLRNAL OF I~FORMATIOV TECHWL OCY MA\ AGEME\T

Dr Massey is a member of the Decision Sciences Institute TIMS m d the Academy of Management

William A. Wallace (M'90) received the B.Ch.E degree from Illinois institute of Technology and the M.S. and Ph.D. degrees in management science from Rensselaer Polytechnic Institute, Troy, NY.

He is Professor of Decision Sciences and Engi- neering System\. Rensselaer Polytechnic Institute. As a researcher and a consultant in Management Science and Information Systems, he has over 20 years experience in research on and the development of decision support systems for industry and govem- nient. He i h presently engaged in the application of

artitical intelligence and advanced communications and location technology to problems i n planning and control. He has held academic positions at Carnegie- Mellon Uniirrsity and the State Universit) of Neb York at Albany, was a Research Scientist at the International Institute of Ehvironment and Society, Science Center. West Berlin. Germany. and a project engineer at Illinois Institute of Technology Research Institute: b a s Visiting Professor, Polyproject; Risk and Satcty of Technical Sy\tems. Swiss Federal Institute of Technology, Zurich. and I C a Nav) veteran. He was selected as a Visiting US Faculty of Management information Sy\teiii\ and the National Center for Industrial Science anti Technology Management Development, Dalian, China. He has authored and co-authored six books and m e r 100 articles and papers.