A rule-based system for fixture design

Transcription

1 Scientific Research and Essays Vol. 6(27), pp , 16 November, 2011 DOI: /SRE Available online at ISSN Academic Journals Full Length Research Paper A rule-based system for fixture design Djordje Vukelic 1 *, Branko Tadic 2, Ognjan Luzanin 1, Igor Budak 1, Peter Krizan 3 and Janko Hodolic 1 1 Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, Novi Sad, Serbia. 2 Faculty of Mechanical Engineering, University of Kragujevac, Sestre Janjic 6, Kragujevac, Serbia. 3 Faculty of Mechanical Engineering, Slovak University of Technology in BratislavaNamesti Slobody, 17, Bratislava, Slovakia. Accepted 18 October, 2011 Modern market imposes stringent demands regarding the product quality/price ratio, with an ever decreasing time-to-market. Furthermore, products are increasingly manufactured in small batches and high varieties, which requires flexibility not only from manufacturing system but the entire manufacturing process. Such demands require manufacturing systems which are highly automated in the domain of preparation and realization of manufacturing activities, which include fixture design. Owing to present trends towards reduction of lead time and human effort devoted to fixture design, computer aided fixture design has gained a prominent role in computer aided environment. In this paper, a system for computer-aided fixture design is presented and verified. This system comprises methods and techniques for fixture design. The structure of this system is based on modular principle, and uses data base and knowledge base. The system allows fixtures to be designed based on geometric features of workpiece, process planning and machining information. A segment of output results is also shown. Finally, conclusions are presented with directions for future investigation. Key words: Machining fixture, rule-based systems, data base, knowledge base. INTRODUCTION During the last couple of decades, the computers have been increasingly used to assist design activities. The beginnings of their application date back to the sixties of the last century, when they were first successfully used to control machine tools. This was followed by an expansion of their application in various domains of manufacturing engineering. During the seventies of the twentieth century, the concept of Flexible Manufacturing System (FMS) was first introduced. Such systems have been capable of functioning within a fully automated environment with a very flexible manufacturing program. During the last thirty years, Advanced Production Technologies (APTs) have gained special importance. APTs include a number of technologies that are used through all steps from computer designing level, up to computerized integration of machine and equipment *Corresponding author. vukelic@uns.ac.rs. during manufacturing process (Semiz, 2010). The APTs that are most used nowadays include: Computer Aided Design (CAD), Computer Aided Manufacturing (CAM), Computer Aided Engineering (CAE), Computer Aided Planning (CAP), Computer Aided Process Planning (CAPP), Computer Aided Quality (CAQ), Computer Aided Inspection (CAI), Computer Numerically Controlled (CNC) machines, and robotics (Ostojic et al., 2010; Semiz, 2010; Tadic et al., 2011). Today, the emphasis is placed on the Intelligent Manufacturing Systems (IMS) which are able to solve problems without the use of an explicit and detailed algorithm or a mathematical interpretation of the problem. Various computer aided systems are used to assist product design and manufacture in order to shorten the time and related costs. Numerous systems have been developed which allow automation at particular stages of design and manufacture. Computer technologies have revolutionized modern manufacturing. From the standalone CAD/CAM applications, to Product Data Management/Enterprise

2 5788 Sci. Res. Essays Resource Planning (PDM/ERP) systems, the computer technologies have fulfilled the dreams of every manufacturer shorter product development time, higher quality, and lower costs (Simunovic et al., 2010; Wazed et al., 2009). The level and the trend of further development of technological machining processes in metal manufacturing industry depend on all the composing factors, such as; types of blank, machining process, order of operations, machinery, operation and sequence concentration, cutting tools, fixtures, measuring devices, etc. In order to raise technological solutions to a higher level, it is necessary to solve optimally all these elements (Peng et al., 2009; Vukelic et al., 2011). In the course of the numerous machining processes that are performed upon a typical workpiece, which all represent a part of the manufacturing process, workpiece must maintain its location and safe clamping. A machining fixture is a device used to provide base locating, support, and clamping for a workpiece, in such a way which provides machining within required tolerances (Hunter et al., 2006). Fixtures have important role in modern manufacturing system as well as, computer integrated manufacturing environment (Peng et al., 2010). Fixture design is a complex procedure which requires a designer with extensive knowledge and experience (Sanchez et al., 2009). Last decades have seen significant effort aimed towards rationalization and automation of the fixture design process. Automation of fixture design is a difficult task, and represents one of the bottlenecks in flexible manufacturing systems. Nowadays, the fixture design process is oriented towards automated systems based on knowledge models (Pehlivan and Summers, 2008). The most recent development available about automated fixture design indicated that expert and knowledge systems played a prominent role in this field (Boyle et al., 2011). Markus et al. (1984) propose one of the first fixture design expert systems which configure clamps about a part using design specifications and rules related to geometrical constraints. Gandhi and Thompson (1986) use a method for determining location and clamping positions by analysing forces and torques in fixtures and utilizing expert system for automated modular fixture design for a flexible manufacturing system. Darvishi and Gill (1988) developed a dedicated fixture design expert system which comprised both of design rules and databases for workpieces, machine tool, fixturing elements, and fixture design principles, and so on. Boerma and Kals (1989) developed a system for automatic selection of setups and datum based on feature tolerances for prismatic parts. The system automatically selects the positioning, clamping, and supporting faces for each setup. Bidanda and Cohen (1990) describe a rule-based expert system to identify the locating and clamping faces on rotational parts. The clamping mechanism is used to perform both the locating and clamping functions. Darvishi and Gill (1990) develop a fixture design expert system which is based on examining the design goals to be achieved and then creating rules to satisfy these imposed specifications. Nnaji and Lyu (1990) develop a framework for a rulebased expert fixturing system for face milling a planar surface on a CAD system using flexible fixtures. Kumar et al. (1992) proposed an expert fixture design system, which integrates setup planning and fixture design for an automated manufacturing environment. Nee et al. (1992) presented a feature-based classification scheme for fixtures using a 3D solid modeller, a feature extractor and an object-oriented expert system shell. Lin and Young (1995) develop an expert system for modular fixtures. The system provides a fixturing procedure analysis for workpieces with different shapes such as L, I, T, and U types for face milling process. Dai et al. (1997) described a method for application of a rule-based reasoning on the modular element database, which can be used effectively for integrating with a CAD system and for modelling fixture subassemblies. Bugtai and Young (1998) discuss the information model in an integrated fixture design support system. Information model can be applied to a particular workpiece to select possible locating and clamping processes in conjunction with the appropriate available resources. Ma and Rong (1999) presented an automated fixture design system, in which the fixturing surfaces are automatically determined based on geometric and operational information. Kumar et al. (2000) develop a classification model and classification rules for conceptual design of fixture in milling and drilling process, for prismatic parts. Mervyn et al. (2003) developed an Internet-enabled interactive fixture design system. The Internet and the use of XML as a file format provide a means for the transfer of information and knowledge between the various computer-aided manufacturing systems. The system is based on a threetier thin client-fat server architecture. Gologlu (2004) presents a rule-based reasoning methodology for setup planning and datum selection incorporating machining and fixturing constraints. Stampfer (2009) develops an automated setup and fixture planning method for the machining of box-shaped parts on horizontal machining centres. More information on these and other systems developed for automated fixture design can be found in (Bi and Zhang, 2001; Cecil, 2001; Hargrove and Kusiak, 1994; Vukelic et al., 2009; Wang et al., 2010). The aforementioned expert and knowledge systems for fixture designs have some deficiencies in common. One of the common deficiencies of the systems for automated fixture design is the issue of creating fixture layout solutions. All previous investigations, regardless of the basic approach, produce systems which generate partial fixture solutions, that is, the selection of locating and/or clamping elements. The remaining element groups (fixture body elements, guiding elements, and tool

3 Vukelic et al aligning elements) are not selected based on the previously defined criteria, which represents a significant drawback since these elements are of great importance in fixture design. Some researchers deal exclusively with fixture planning, disregarding all other stages in fixture design, such as the fixture configuration design, and fixture design verification. In addition, all of the so far work is based on a locating method, although it is not the only locating method available. This overlooks the fact that, in this way fixture price is driven up just by increasing the number of fixture constituent elements. Also, there is an increased possibility of machining error. Modern fixture design systems also pay little attention to the influence of locating error on the total machining error. The majority of systems are dedicated, that is, the systems have been developed for particular workpiece geometry, most often prismatic and rotational, as well as for particular machining processes on specific machine tools. It should be noted that previous solutions work with workpieces of simpler geometry, which brings their practical value into question, bearing in mind that all such machining processes could also be performed using universal fixtures which come with machine tools. Based on the conducted analysis and the identified problems, the primary goal of this paper is the development of an automated fixture design system which would allow selection of fixture elements from all functional groups - not only the selection of locating and clamping elements, but also the selection of fixture body elements, tool guiding elements, tool aligning elements, connecting elements, and other elements. Besides, the system should also be universal as much as possible, that is, it should provide fixture design for as many classes of workpiece geometry. The system should also provide consistency of designed solutions, so as to prevent the generation of two different fixture designs for the same workpiece and machining process. The system must posses certain degree of intuitiveness, allow shorter design times and related costs, as well as provide quality fixture design solutions. The ultimate goal is to generate complete fixture solutions which completely present details of physical structure elements based on the previously defined tasks for each element. MATERIALS AND METHODS The structure of the proposed system for automated fixture design is given in Figure 1. System input consists of required input data which are derived from the engineering drawing, and process plan specification. Input data are appropriately coded so that the software can use them for selection of fixture elements from the database using a set of pre-defined production rules. When designing a new fixture, it is possible to segment it into several functional groups, and then design it through phases. Finally, it is necessary to define fixture layout, perform analysis, remove potential collisions, and generate required engineering drawings (2D and 3D fixture representations, and Bill of Materials). In order to perform these activities successfully, it is necessary to systematize basic prerequisites for fixture design, develop database, and knowledge base, and produce software for automated selection of fixture elements. The database The database ensures system functioning in a sense of qualitative performance of its main functions, data search, and update. The database consists of three segments: fixtures data file, workpieces data file, and fixture elements data file. Data files are easy to organize in tables (Figure 2) such that columns present the existing fixtures, workpieces, and fixture elements, and rows present their characteristics. Tables are organized such that rows present different fixtures, fixture elements, and workpiece variations, while columns represent geometric, technological, and other characteristics of each of them individually. These tables can be used by the automated system as a means of formalization of the process of selection of fixture elements. Fixture file comprises the following data: fixture ID, fixture classifier code, list of fixture constituent elements, name of machining process for which the fixture is designed, name and designation of machine tool, workpiece code, first name and last name of designer, date of design, graphical data in the form of 2D drawings, and 3D models of fixture with corresponding notes. Contained within the workpiece file are all workpieces for which fixture solutions already exist. Each workpiece is attributed with the following data: ID, code, material, batch size, name and code of machining process, 2D drawing, and 3D model of workpiece. Fixture elements file stores all the data on fixture elements, which are required for design of novel fixture solutions. The information is broken down into two groups. The first group is the data, which defines all fixture elements, namely, element designation, and name, affiliation with a particular functional group of elements, 2D drawings, and 3D models of elements. The other group contains data, which vary depending on the functional group to which a particular element belongs. They differ according to the function of element and its geometric features. These data are characteristic of each element in particular. For instance, one of important parameters for clamping elements is force magnitude, which they are capable of producing. For other functional groups of elements, this information neither exists nor is relevant. Different elements have different shape and geometric features. Moreover, not even elements which belong to the same functional group have the same data. The knowledge base The knowledge base is used for acquisition and storage of procedural, declarative, heuristic, and meta-knowledge from the fixture design domain. Knowledge is represented through a set of syntax and semantic rules which allow formal description of fixture elements. Knowledge representation means coding and formalisation of knowledge into a format which is suitable for computer processing. The knowledge is organized in such a manner which allows the software to directly use it in the process of fixture design. The systems which store their knowledge in the form of rules are often called rule-based systems. The rules can be understood as elements of knowledge, that is, knowledge quantum from a particular area of fixture design. Production systems are software applications which perform an inference process based on human-expert knowledge which is coded and stored as a knowledge base. A special mechanism utilizes such knowledge to perform the inference process in order to come up with a fixture solution. Functioning of the system is based on a symbolic representation and processing of the built-in knowledge. This knowledge is represented through formal symbols and suitable data structures defined by a higher programming

4 5790 Sci. Res. Essays Figure 1. Structure of the proposed system for automated fixture design. language, while the problems are solved by producing a sequence of inductive and deductive inferences through manipulation of these symbols and data structures. Fixture elements are chosen by repeating the rules until adequate elements are selected from the data base. Elements are selected on the basis of their function. Should more than one fixture element satisfy the requirements, the designer decides which solution shall be adopted. The data on a particular functional group of fixture elements is accessed through an appropriate form (Figure 3). Basic components of this system module are; 1) Questions for the user; 2) Rules which are checked for example, if the conditions for a particular action are satisfied; 3) Choices - actions which are undertaken if a particular condition is met. Based on the selected options, and the designer s answers to questions which are specific for each group of fixture elements, appropriate production rules are generated which allow particular fixture elements to be selected and stored into knowledge base. Using the form shown in Figure 3, it is possible to edit and delete existing production rules. The system also resolves conflicting knowledge situations during editing of existing knowledge, or entering new one into knowledge base. Automated detection of the fixture for which this rule was used during design process is also enabled. In this case, the designer must decide whether the new rule is applicable just to the new (previously designed) fixture layout or several of them, and to perform adequate corrections, or, alternatively, to avoid applying this rule, while deciding to keep both

5 Vukelic et al Figure 2. A segment of the database table with fixture elements. Figure 3. Dialog form for production rules in the knowledge base. the existing and the novel rule in the database. In this case, the system in the ensuing steps generates at least two fixture elements which allow the same function to be performed. Defining characteristic work piece surfaces One of the key problems which have to be solved in automated fixture design is the unambiguous definition of characteristic work piece surfaces. Orientation of any surface in space can be unambiguously defined by defining its coordinates in a coordinate system, which can be optionally attached to some characteristic point on the workpiece (Figure 4). In both of these cases it is possible to use a special coding system to completely define particular surfaces on the workpiece. Each surface can have its own coordinate system. Cartesian coordinate system is suitable for elementary workpiece surfaces. The reference coordinate system (0 XYZ), which is the reference for defining workpiece surfaces, can have arbitrary orientation in space. However, for simplicity s sake, it is convenient to fix it to a point on workpiece. Testing showed that it is most convenient to place its origin on the intersection of the locating surfaces (if possible), so that coordinates belong to locating

6 5792 Sci. Res. Essays Figure 4. Defining the locations and orientations of elementary surfaces in space an example given for locating and clamping surfaces. surfaces. Location of a local coordinate system, (A XYZ), which defines some elementary surface, should be set so that it belongs to elementary surfaces. Relative to the origin of a local coordinate system, it is necessary to unambiguously define the geometry of workpiece elementary surface. For the sake of efficiency, there should be defined as much as possible characteristic surface types. Should some additional surface types be required in the design process, their unambiguous mathematical description should be provided. Input information Input information can be broken down into two principal groups: information on workpiece, and manufacturing information. In general, input information comprises information based on; 1) Type of machining (turning, drilling, milling, grinding and so on); 2) Main machine tool group (lathes, drilling machines, milling machines, grinders and so on); 3) Machine tool sub-group (universal lathe, copy lathe, revolver lathe, single-spindle pillar drilling machine, horizontal single-spindle boring machine, multi-spindle drill head machines, aggregate drilling machines, horizontal milling machine, vertical milling machine, universal milling machine, copy milling machine, surface grinding machine, universal grinding machine, copy grinding machine, round grinding machine and so on); 4) Type of machine tool (conventional, CNC); 5) Specific machine tool which is selected for machining (work table surface info, info on the dimensions of work table grooves); 6) Number of work pieces being machined at the same time (one, two, three, and so on); 7) Number of tools (single tool, more identical tools, more various tools); 8) Number of machining surfaces (single surface, more identical surfaces in linear displacement, more identical surfaces in circular displacement); 9) Method of connecting fixture with machine tool (spindle, work table); 10) Method of arresting fixture during machining (by fixture elements, manually); 11) Batch size; 12) Work piece shape (prismatic, rotational, irregular); 13) Overall workpiece dimensions (length, height, width); 14) Number of degrees of freedom arrested with locating elements (3, 4, 5, 6); 15) Workpiece locating method (3-2-1, 4-1-1); 16) Basic fixture characteristic (locating and clamping on external surfaces, locating and clamping on internal surfaces, locating and clamping on internal and external surfaces); 16) Forces and moments acting during machining process; 17) Shape of locating surfaces (external flat, internal flat, external cylindrical, internal cylindrical, external conical, internal conical, external spherical, internal spherical and so on; 18) Integrality of locating surfaces (continuous, step-like); 19) Quality of locating surfaces (ISO tolerance grade - IT); 20) Type of locating surfaces (ring, triangle, quadrilateral, rhomb, trapeze); 21) Characteristic dimensions of locating surfaces; 22) Position of primary locating surfaces relative to machine tool work table (horizontal, vertical, angled); 23) number of clamping force directions; 24) Shape of clamping surfaces; 25) Clamping scheme in particular directions (clamping force is parallel to the plane of cutting moment, clamping force is orthogonal to the plane of cutting moment, clamping force is at an angle relative to the plane of cutting moment); 26) Clamping drive in particular directions (manual, pneumatic, hydraulic, electrical, combined); 27) Direction of clamping force relative to locating surface in particular directions (parallel, orthogonal); 28) Intensity of clamping force in particular directions; 29) Types of clamping surfaces by particular directions; 30) Characteristic dimensions of clamping surfaces. Some of this input information are specific to particular machining cuts. So, for example, the required input information for drilling on conventional drilling machines is the drilling diameter, similarly, for milling on conventional milling machines - it is necessary to know the number of dimensions which define the machining surface. Selection of fixture elements Within this system module, all required fixture elements which belong to various functional groups are selected. In order to allow

7 Vukelic et al Figure 5. Fixture assembly. efficient system operation, the following functional groups of fixture elements were identified: 1. Locating elements - Uniquely define workpiece location in a fixture, bring workpiece into correct and final orientation in a fixture, arrest degrees of workpiece motion, in order to allow proper machining; 2. Clamping elements - Provide stable contact with locating elements; prevent workpiece movement during machining due to acting forces; 3. Fixture body elements - Provide platform for all other elements and receive loads which act upon workpiece during machining; 4. Tool guiding elements - Used with conventional machine tools to guide the cutting tool relative to the workpiece; 5. Tool aligning elements - Used with conventional machine tools to locate the cutting tool position relative to the workpiece; 6. Connecting elements - Connect fixture elements with each other; 7. Add-on elements/elements for bridging height and length distances - Bridge the distances in required directions in order to provide fixture integrity; 8. Add-on elements/elements for fixture manipulation - Allow fixture manipulation during mounting/dismounting, transport, and similar; 9. Add-on elements/elements for fixture positioning on machine tool - Uniquely define position of fixture on machine tool table; 10. Add-on elements/elements for attaching fixtures to machine tool - Attach fixture to machine tool table; 11. Add-on elements /securing elements - Allow workpiece to be set in a proper position in fixture; 12. Add-on elements/translating elements - Translate workpiece in order to bring it, in one clamping, into a new working position, relative to the tool; 13. Add-on elements/rotating elements - Rotate workpiece in order to bring it, in one clamping, into a new working position, relative to the tool. The basic idea behind production systems is to iteratively apply the rules from the knowledge base to solve the problem which is described by data in the operating memory. Inference engine is used to find the required knowledge in the knowledge base. It operates as a mediator between the knowledge base, and user interface. Inference engine contains rule interpreter, which is used to process and interpret production rules during system operation. During inference process, based on initial information stored in the operating memory, and the knowledge which is stored in the knowledge base, the inference engine attempt to find adequate fixture elements. Executing the process it then generates new data in the operating memory, which extends the existing set of data in the operating memory. This updated state of the operating memory can be sufficient for the selection of fixture elements, in which case the process ends there. If that is not the case, then the extended set of data is processed again using the knowledge base, which again updates the data set in the operating memory. The process is re-iterated until a state is reached when there is sufficient data to find the required fixture elements. Otherwise, the system concludes that the solution is impossible to obtain. If a required fixture element does not exist, then it is necessary to design that element or purchase it. Such element would also be stored in the data base, and the appropriate production rules for its selection would have to be generated. Fixture assembly, analysis of solution, and definition of output information Input into the last segment of the system is previously generated data on fixture elements. In the first step, fixture is assembled using the database, that is, the file with all the fixture elements. An example of step-wise fixture synthesis is given in Figure 5. The required elements are entered into the assembly one-by-one, for better clarity and to avoid collisions. Workpiece is entered first, then the locating elements, clamping elements, followed by the remaining fixture elements. Once the fixture is assembled, the design solution is analysed. This analysis comprises detection and removal of possible collisions. There are three types of collisions which can be identified: 1. The collision between immovable fixture elements (Figure 6) can prevent successful fixture assembly. The same holds for the collision between adjustable and exchangeable fixture elements, which is often the case in group technology. 2. The collision between fixture elements and workpiece (Figure 7) can directly influence machining process requirements, hindering the set-up and take-out of workpiece from the fixture. Also, workpiece can often be setup into fixture in more than one variant. This can cause various errors in locations of the machined features. 3. The collision between fixture elements and the cutting tool (Figure 8) can occur in cases when placing fixture elements along the tool path during he machining. This can damage the tool or fixture elements and in some cases can result in tool and fixture elements failure, damaging the machine and other parts of

8 5794 Sci. Res. Essays Figure 6. Analysis of collision between immovable fixture elements. Figure 7. Analysis of collision between fixture elements and workpiece. machining system or even injuring workers. After the final fixture solution is formed, the required output information are generated fixture drawings, and bill of materials. Should any additional information be required, they can be generated within this system module. RESULTS This section contains a case study conducted in real industrial environment. On the workpiece shown in Figure 9, three holes Ø12H10 were to be drilled on a conventional pillar single-spindle drilling machine. Drilling was performed with just one tool twisted drill. Machining was performed at 30 m/min cutting speed, and 0.2 mm/rev. feed. During machining, the fixture was placed on the machine tool worktable and held down manually. Batch size was 7000 pcs. Workpiece was located by one external and one internal surface. The workpiece needed to be denied a total of five degrees of freedom, based on the locating principle. Clamping was done over an external surface. Cutting force direction was normal to

9 Vukelic et al Figure 8. Analysis of collision between fixture elements and the cutting tool. Figure 9. Workpiece - flange. base surface. Clamping force was normal to cutting moment plane. Clamping was driven manually. As seen in Figure 10, there are two locating and clamping strategies for workpiece. In both cases, locating elements deny the workpiece five DoFs, using the following surfaces: 1. A - primary locating surface denies the workpiece four DoFs, 2. B - secondary locating surface denies work piece a single DoF. According to the first locating strategy, the locating error

10 5796 Sci. Res. Essays Figure 10. An example of two workpiece locating and clamping strategies. Figure 11. Characteristic drilling operations and the indexing assembly. equalled zero. According to the second locating strategy, due to the fact that two different surfaces were used for datum and locating planes, a locating error occurred which could be traced down to the tolerance of dimension 120 mm (Figure 10). The locating error equalled ΔL=0.2 mm, which exceeded the tolerance of T=0.02 mm. Thus, the workpiece had to be located according to the first strategy, in order to machine the holes within the designated tolerance. The workpiece could be clamped over the C surface which was parallel to surface A. The discussed strategies requires that the hole drilling be performed with a single tool using an indexing device. Within the same machining process (the same clamping) workpiece was brought into new position relative to cutting tool. Besides moving the workpiece into new position, these devices allowed indexing by a precisely defined angle. The exact alignment of the drilling bit was allowed by the indexing device which was rotated together with the workpiece for the given angle (120 ), immediately before the execution of next machining process. Workpiece was fixed into required position using the indexing assembly (Figure 11). Subsequently, the definition of workpiece geometry regarding the characteristic surfaces was presented. Firstly, reference coordinate system was established (Figure 12), and then all other local coordinate systems were defined relative to it. Surface geometries were defined by their characteristic types and dimensions (Figure 13). In addition, it was also necessary to define other input information (Figure 13), which was entered based on workpiece engineering drawing, as well as the manufacturing and geometric constraints defined by the manufacturing process plan. Once the input information was coded, database was searched, and the elements which meet the criteria were retrieved. Figure 14 shows the form with fixture elements which were classified into element groups, and then used for fixture assembly. In addition to the elements suggested by the software, two reinforcement ribs were also applied to increase fixture rigidity. Also, the cylindrical mandrel required a

11 Vukelic et al Figure 12. Location of coordinate-system origin. Figure 13. Segment of forms for input information coding. hole, the diameter of which was a couple of millimetres larger than the diameter of the hole which was to be drilled at the predefined distance from the locating segment, in order to prevent collision between the locating element and cutting tool (Figure 15). Once the fixture had been assembled (Figure 16), its construction was analysed for detection and elimination of possible collisions. The final step was the forming of engineering documentation. The output results from the system for automated fixture design are shown in Figure 17. DISCUSSION In order to verify functionality of the proposed software solution, all its modules were tested. The system was

12 5798 Sci. Res. Essays Figure 14. Fixture elements. Figure 15. Correct and incorrect locating of workpiece. tested in a real industrial environment, in 8 manufacturing systems, on a total of 258 different workpieces which required turning (34 workpieces), drilling (92 workpieces), milling (99 workpieces), and grinding (33 workpieces). Shown in Figure 18 are some examples of fixtures designed using the developed system. The results of testing are shown in Figures 19 and 20. Figure 19 shows the success rate of identification of fixture elements from

13 Vukelic et al Figure 16. Fixture assembly. Figure 17. Output results from the system - 2D fixture drawing, 3D fixture drawing, BoM. each particular functional group. The system managed to identify adequate fixture elements from the majority of functional groups. In this way, practical applicability of this investigation was confirmed. Figure 20 illustrates the minimum, average, and maximum time required for fixture design. As can be seen from the Figure 20, the times for fixture design were very short, reaching 236 minutes in the worst case

14 5800 Sci. Res. Essays Figure 18. Examples of fixtures designed by the developed system. Figure 19. Percentage of successful identification of fixture elements in the database. scenario. Compared to the time required previously, this time has, on average, been shortened by approximately 218 min. The developed system for automated fixture design allowed fixture solutions to be designed with significantly shorter times. In this way, techno-economic output parameters were greatly improved. The investigation, presented in this paper, allowed formulation of basic prerequisites for the development of knowledge base which encompassed more functional

15 Vukelic et al Figure 20. Minimum, average, and maximum time required for fixture design. groups of fixture elements, and reliable rules for the selection of fixture elements. Beside the selection of elements for locating and clamping, also featured by the existing CAFD systems, the proposed system additionally allowed selection of: fixture body elements, tool guiding elements, tool aligning elements, connecting elements, and a substantial number of add-on elements. In order for the developed system for automated fixture design to be widely used, it is necessary to complete the building of its knowledge base. Special attention should be focused on the development of additional criteria for the selection of securing elements (add-on elements), and elements for bridging height and length distances (add-on elements). System efficiency could be improved by integration with a system for computer-aided design and process-planning. Such systems would provide sufficient input information for the fixture design system. The kinetic model of fixture considers workpiece as a rigid, not elastic, body. In other words, the kinetic model does not allow for workpiece deformations. Machining dynamics is also one of requirements which should be met during fixture design process. Instead of examining all possible requirements, this investigation was focused on defining a general framework. Once the framework is established, it will be possible to identify, study, and integrate all other requirements into the proposed system. ACKNOWLEDGEMENT This research was supported by by the Ministry of Science and Technological Development of the Republic of Serbia. REFERENCES Bi ZM, Zhang WJ (2001). Flexible fixture design and automation: Review, issues and future directions. Int. J. Prod. Res., 39(13): Bidanda B, Cohen PH (1990). Development of a Computer Aided Fixture Selection System for Concentric, Rotational Parts, Symposium on Advances in Integrated Product Design and Manufacturing, ASME WAM 1990, PED, 47: Boerma JR, Kals HJJ (1989). Fixture Design with FIXES: the Automatic Selection of Positioning, Clamping and Support Features for Prismatic Parts. CIRP Ann-Man. Technol., 38(1): Boyle I, Rong Y, Brown DC (2011). A review and analysis of current computer-aided fixture design approaches. Robot. Comput.-Integr. Man., 27(1): Bugtai N, Young RIM (1998). Information models in an integrated fixture decision support tool. J. Mater. Process. Technol., 76(1-3): Cecil J (2001). Computer-Aided Fixture Design - A Review and Future Trends. Int. J. Adv. Man. Technol., 18(11): Dai JR, Nee AYC, Fuh JYH, Kumar SA (1997). An approach to automating modular fixture design and assembly. Proc. Inst. Mech. Eng. Part B-J. Eng. Man., 211(7): Darvishi AR, Gill KF (1988). Knowledge representation database for the development of a fixture design expert system. Proc. Inst. Mech. Eng. Part B-J. Eng. Man., 202(B1): Darvishi AR., Gill KF (1990). Expert system rules for fixture design. Int. J. Prod. Res., 28(10): Gandhi MV, Thompson BS (1986). Automated design of modular fixtures for flexible manufacturing systems. J. Man. Syst., 5(4): Gologlu C (2004). Machine capability and fixturing constraints-imposed automatic machining set-ups generation. J. Mater. Process. Technol., 148(1): Hargrove SK, Kusiak A (1994). Computer-aided fixture design: a review. Int. J. Prod. Res., 32(4): Hunter R, Rios J, Perez JM, Vizan A (2006). A functional approach for the formalization of the fixture design process. Int. J. Mach. Tools Man., 46(6): Lin ZC, Yang CB (1995). An expert system for fixturing design for face milling using modular fixture. Int. J. Adv. Man. Technol., 10(6): Ma W, Li J, Rong Y (1999). Development of automated fixture planning systems. Int. J. Adv. Man. Technol., 15(3):

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