ORIGINAL ARTICLE
A case-based reasoning approach for design of machining fixture
Heidar Hashemi&Awaluddin Mohamed Shaharoun&
Izman Sudin
Received: 31 October 2013 / Accepted: 6 May 2014
#Springer-Verlag London 2014
Abstract Appropriate fixture design could lower process time, lower cost, and improve the quality of products. This paper proposes a case-based reasoning (CBR) method, with im- proved indexing and retrieving approaches which are critical issues in machining fixture design systems. A case storage for fixture design case indexing approach is developed so that the database is constructed into two levels—to manage and cate- gorize numerous related fixture designs. The data includes existing workpieces and associated fixture planning and unit depository as solutions. Based on this approach, a CBR method with a two-step case retrieval is presented. The objective is to improve searching results in database for machining fixture design. In this CBR-based fixture design method, the appropri- ate workpiece in the first level of database by using design requirement is found. Then, the proper conceptual fixture de- sign can be achieved by retrieving related fixture case from the second level. This method facilitates the fixture designing by means of referencing past design cases to generate a conceptual fixture design quickly and easily. It also helps in the finishing design by suggesting some alternative fixture cases. Finally, several case studies are used to validate and present the appli- cability and usability of the proposed approach.
Keywords Machining fixture design . Fixture unit design . Case-based reasoning (CBR)
1 Introduction
Fixtures are special tools, which are mostly used to precisely and firmly locate the workpiece during the manufacturing
operations. Fixtures are required for machining, welding, assembly, inspection, and other operations [1,2]. Machining fixtures directly influence the productivity, machining quality, and the cost of machined parts. Studies show that about 40 % of parts that are rejected are mainly because of dimensioning errors due to poor fixture designs [2]. In fact, the fixture design and production constitute 10–20 % of the total expenditure on manufacturing processes [3]. The process of fixture design is complicated and experience-based, so fixture designers are required to have more than 10 years of practical expe- rience, and also, there is not any throughout theory to support it [4]. Traditionally, machining fixtures have been designed and manufactured by trial-and-error method.
Therefore, developing tools is required in order to eliminate expensive and time-wasting traditional and also simplify de- sign methods of fixtures [5].
Since the 1980s, research with computer-aided fixture de- sign (CAFD) has been conducted to improve fixture design process. In this case, CAFD has been employing some artifi- cial intelligence (AI) techniques through the years in order to help the fixture designers [3]. However, fixture design is still an issue of concern in the present manufacturing field [2].
During two recent decades, much attention has been yet paid to the search and optimization of fixture layouts, while re- search on knowledge-driven fixture design still needs deep- ening [6, 7]. Regarding the complexity and on being experience-based of fixture design, CBR has become the most popular and effective method among AI techniques, but there is still some shortcomings with existing fixture design CBR- based applications [4]. CBR utilizes knowledge and experi- ence to overcome new challenges.
There are abundant available fixture designs in a lot of manufacturing companies as potential solutions for encoun- tered fixture design problems [1,4]. But, the current CBR- based fixture design systems face a great difficulty to provide convergence to a single optimum solution due to inaccuracy H. Hashemi (*)
:
A. M. Shaharoun:
I. SudinDepartment of Manufacturing & Industrial Engineering,
Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
e-mail: [email protected] DOI 10.1007/s00170-014-5930-4
of case retrieval strategy (similarity functions). Most of the systems end up to more than one fixture design solution. It is then up to the designer to select the optimum solution. Im- proving the accuracy of case retrieval will reduce the number of selected fixture designs that will be suggested. Hence, there is an opportunity to improve the case retrieval of CBR-based CAFDs if we can study and determine a better similarity function in order to find the most similar fixture design among many fixtures in the database [1,4–6]. The aim of this paper is to improve the efficiency of case retrieval as heart of the system by proposing a new similarity function and subse- quently improving an indexing strategy as foundation of the system for compatibility with the new retrieval strategy.
2 Related research
Fixtures are generally composed of four components: locators, clamp, support, and base plate. Commonly, fixture design is how to place the components onto a workpiece in order to achieve immobility and stability of the workpiece during machining or other operations. Generally, the fixture design is categorized into four sub-steps which are (1) setup planning, (2) fixture planning, (3) unit design, and (4) verification.
Figure1shows the main stages of fixture design process [8].
Along with much work being carried out to develop an efficient CAFD system, fixture design systems can be catego- rized into three main categories based on their extent of automation, i.e., fully automated, semi-automated, and inter- active class [5,9]. The fully automated and semi-automated fixture design systems-based are established through
incorporating the experience and knowledge of designers into rules and algorithms in order to automate the selection of fixturing elements and locating points. Interactive fixture de- sign modules which use the knowledge of the designer enable the designers to select the fixturing faces, points, and elements for fixture structure [10].
The first researches in CAFD [11] mainly developed the interactive class applications, based upon an understanding of workpiece and process information. These systems usually provide fixture designer rules to evaluate the design and a list of components to be used interactively [12]. They mostly utilize expert systems as empirical device, which is often used to help fixture designer in interactive environments to achieve a fixture design solution especially for fixture configuration design [13].
Many researches related to fixture design have concentrat- ed on the micro-aspects of fixture design on the matter of fixture design automation which are referred to singular prob- lem, e.g., deformation analysis, stability evaluation, fixture repeatability, tolerance analysis, geometric reasoning, and so on. For instance, Subramaniam et al. [14] and Sentil Kumar et al. [15] applied hybrid genetic algorithm/neural network and machine learning approaches to achieve conceptually design individual fixture units automatically. The approaches are considerable, though the output was a highly conceptual fixture unit. Recently, Wan et al. [6] applied a method to match design environment with fixture design requirement for fix- ture design configuration using smart modular fixture unit constitution. The smart fixtures can perceive the design envi- ronment by themselves and drive themselves to complete appropriate design activities automatically.
According to the fact that fixture design is highly experience-based, some researchers have proposed semi- automated fixture design systems by using combined fixture design knowledge and existing fixture design experience.
Those methods mostly applied case-based reasoning (CBR) technique to solve fixture design problems by using existing good fixture designs mentioned before [1,5,9]. Kumar and Nee [9] proposed the significant CBR-based approach which is still used as the basis of CBR-based fixture design systems.
They proposed a framework of a case-based fixture design system so that past design cases could be indexed by using the attributes that described the workpiece for which a fixture was to be designed. Then, Sun and Chen [16] proposed another method, saying that fixture design cases are stored in the database, from which similar cases meeting the design re- quirements are retrieved according to functional features; thus, a new fixture design is obtained by modifying the existing case. The weakness of this approach is the growing of the number of fixture in database, and then, the retrieving process would take a long time because of the volume of computation.
In CBR-based fixture design applications, huge amount of data needs to be retained and reused [1]. Data retaining and
Setup planning:
- Identify setups
- Determine locating datums
Unit design:
- Conceptual unit design - Detailed unit design
Verification:
- Verify fixture against fixturing requirements Work piece CAD model
Machining information Design considerations
Finished setup plan Fixture design Materials listing Fixture planning:
- Define fixturing requirements - Determine fixture layout plan
Fig. 1 The main stages of designing a fixture [8]
reusing is related to indexing and retrieval process of a CBR- based application, respectively. Therefore, this gives rise to the demand for systemizing the domain knowledge of the fixture design to clarify the CAFD design requirements. Typ- ically, Li et al. [17] suggested a flowchart of a case-based fixture design system such that the features that define the workpiece for considering designed fixture are used to index existing design cases. Fan et al. [18] introduced the XML- based CBR technique which divides the data of the fixture design into three groups with attributes of parts, setup, and fixture representation.
Accordingly, Boyle et al. [3] reported an indexing CBR methodology called“CAFixD”with two levels of database.
The first level of database contains conceptual fixture design and the second level is composed of individual fixture units.
The configuration of case library is such that the fixture units in second level are retrieved with consideration on the results of first level. To develop Boyle indexing method, Wang and Rong [1] presented a XML-based with three-level CBR meth- odology which has a database with three levels. In this re- search, three levels of database include workpiece and process information, considering fixture planning and fixture units such that each level can be navigated by the result of previous retrieval step, designer feedback, and also a combination of them. Although, some efforts have been done on information representation and case indexing, but there is not a structure for indexing method in the fixture design field, and the indexing mainly depends on the designer experience and knowledge. The designer chooses several significant attri- butes, which are essential to the present design case with respect to retrieval process [3,4].
The retrieval process of a CBR is the most important part of the application which finds the suitable case in database [19].
Traditionally, cases are mostly indexed by means of attribute- value pairs, and then, retrieving process was like a query in database [9]. In fixture design problems, there have been difficulties to measure similarity between cases in the case base because of many attributes that need to be considered [1, 9]. Recently, based on the assumption that “similar work- pieces have similar fixture design,” some similarity-based retrieval methods by using the distance metric approaches are proposed. Wang and Price [1,20] applied a distance metric method based on cosine formula metric method. The searching result of this method was not sophisticated, and also, there is another issue with this method so that two cases that are a linear multiple of each other, for example, one workpiece is n-times bigger than another, will be taken as identical cases while not being the same.
Then, Peng [5] proposed a combination of RBR-fuzzy and a Chebyshev distance metric in CBR to improve searching result in database. RBR and fuzzy are used to help CBR method in case of searching by reducing search space. Then, Chebyshev distance metric is used to find the similar case/s in
database as solution. The result of this method is better com- pared to the existing ones, but the problem is that the largest attribute of the case highly affects the result process. So, one single largest attribute would not offer enough description of the data set to lead to accurate similar selection and final predictions. Many efforts have been done on retrieval stage as the engine of CBR, but some deep studies are still required in order to improve the performance of CBR-based fixture design applications in case of case searching accuracy to achieve more applicable CAFD systems.
3 A CBR-based computer-aided machining fixture design approach
CBR is developed in the artificial intelligence field. CBR is an obvious type of comparison based on similarity. It solves the new problems using past experiences. Therefore, a database is constructed by storing past problems with their relevant solu- tions, and then, the new cases as faced problems can be categorized by determining the best-matched case from the database [21]. So, the output of CBR-based methods would be the case in database, which matches the target problem [22].
To develop a CBR-based system, the significant sectors, which affect the performance of the whole process, are the right case description (indexing method), similarity function (retrieval strategy), and organization of the number of cases in database. In case of fixture design, a CBR technique should be capable of identifying the best matching case among the existing cases in database to the target design requirement by using a proper indexing and retrieval strategy. Then, the system enables the designer to revise the retrieved workpiece and the relevant fixture to meet the design necessities in the new design difficulty if required. The general steps of a typical CBR fixture design system are expressed in Fig.2.
3.1 Overview of the proposed CBR-based fixture design application
The proposed CAFD application could be an effective assis- tant tool to perform the fixture design process. The proposed system, whose working diagram is illustrated in Fig.3, mainly consists of the following sectors:
& The input of the system is the extracted information from
the sketch or CAD model of the part and machining process information. The data is prepared at this step to be used in the next step.
& The database or case library, which is constructed in two
levels with internal relations. Level 1 contains all parts information. Level 2 is composed of fixture design, design requirements, fixture units with specifications, 3D models and drawing of fixture design, etc.
& Case retrieval sector includes two sequential steps so that the first step (template search) is as an assistant for the second step, but the main and crucial step is the second one (Euclidean-based closest method). Retrieval process is as the engine of the system so that the engine has a significant impact on the performance of whole system.
& Case reuse and case modification. Case reuse is to choose
the proper fixture design as the solution based on the result of the previous step. Case modification is as a verification strategy like an adviser tool for the designer to complete the fixture design.
In the following sections, we will give a detailed explana- tion on the mentioned sectors of the fixture design proposed CBR system.
3.2 The indexing approach for data storage
Indexing is to identify features that past solutions should be related to the existing problem. Case indexing is a crucial matter in CBR, and CBR systems with inadequate indexing may work imperfectly because of an incapability to separate cases [3]. On the other hand, case indexing has a high impact on the case retrieval as the main stage of any CBR systems. Case retrieval is concerned with discov- ering a related case in the database that is highly influ- enced by indexing approach.
Generally, the proposed indexing method is to divide the design process into two dependent sub-process, first searching
the case library (database) for a sub-solution in the first step as main search process, and then finding the solution/s in the case library based on the result of the first step. In fixture design systems, when proposing indexing approach, two factors need to be considered. First, the workpiece specification and ma- chining process information as the customer attributes are the only available information to start fixture designing. Second, the main assumption of fixture design systems that should be taken into account is that “similar workpieces would have similar fixturing solutions”[9]. Hence, searching for similar workpieces in case library should be the first step in this case, and the second step is to use relevant fixture designs as solution.
According to the idea above, this method needs to have a two-level case library. One contains workpiece and process information and is used during finding similar case, and the second includes details of fixture planning, fixture units, and supportive detailed design. Accordingly, in retrieval process, the focus needs to be on assessing the similarity of design cases. The use of this case-base configuration would have some significant benefits in case of fixture design systems;
(1) it can simplify fixture design process by comparing only workpieces apart from their fixtures in the first step; (2) it would reduce the reliance on designer experience so that this case library configuration enables the designer to easily arrive at conceptual fixture design by directly using workpiece and process information.
The brief design of the case library is shown in Fig.4. The case base is composed of two-leveled libraries. Case library level 1 is related to workpiece information and machining process. It stores important features extracted from 3D CAD model regarding design requirements which are the type of workpiece, size of workpiece, dimensions of workpiece, type of used machine, axis of machining, number of machining operations, type of operations, machining feature type, machining feature dimensions, and number of machining features. The level 2 holds all information which form a fixture design for each individual workpiece separately, for example, fixture planning, fixture unit, function (lo- cating, supporting, or clamping), position, contact sur- face, 3D models and drawing of fixture design and all details of the design.
The indexing approach is to route through case li- brary level 1 in order to first find and retrieve a similar workpiece, before going to the level 2 with the aim of retrieving proper fixture designs. Thus, the output of level 1 which is the retrieved similar workpiece restricts the search domain within case library level 2. Hence, the key retrieving and indexing in the proposed system are retrieve past solutions by using workpiece matching, re- trieve past fixturing solution/s by retrieved workpiece/s, and then send workpiece/s and consider fixture solution/s with similarity rank to the next stage.
New work- piece Problem
Retrieve
Reuse
Revise
Retain
Retrieved work -piece
New work- piece
Solved fixture Revised fixture
Answer
Case Library
Fig. 2 The diagram of the general steps of a CBR (adapted from Aamodt, Plaza [22])
3.3 Fixture case retrieval as similarity measurement method Case retrieval is the critical issue in all CBR systems since it has great effect on the efficiency of the whole process [1]. In case of fixture design criteria, fixtures should be designed considering the main customer attri- butes of fixture design which are the workpiece
information and machining information [3]. So, the main part of this CBR-based method is to search past cases of similar workpiece(s), regarding the information of workpiece and design requirements. In order to de- velop a retrieval method, a retrieval method is proposed, which is a combination of two effective methods in this field, namely, template search and closest method.
Briefly, template search is a simple tool of database searching which acts like query in database so that it uses the certain name of some attributes to find cases with the same attributes. The closest method, which is the vital and heart part of retrieval stage, is able to calculate Euclidean distance as a similarity measure between cases in the database to the query case using important attributes of workpiece and machining process in case of fixture design.
Case Retrieval
Template search
Determine the important criteria Workpieces that match with the query
Similarity assessment
Retrieve the fixture of wokpiece with smallest distance as main solution
Use the fixture configuration for input workpiece
Locating and clamping point adjustment Case
Reuse
Case
Modification Retrieve the closely similar fixtures based on computed distances between workpiece
Show the retrieved cases with their distances as guidance for modification
Design objective
Data pre-treatment
Output Fixture Design
Workpiece Informaon Machining Process
Workpiece Dimensions
Number of Machining Features and Specifications
Case Library
First Level:
Workpiece information Machining process
Second Level:
Fixture planning Fixture unit and functions
•
•
•
• Fig. 3 The flowchart of CBR-
based fixture design algorithm
Decomposes into
Case Library
Case library 1 (level 1):
Work piece and process information (fixture problems)
Case library 2 (Level 2):
Individual fixture planning and fixture unit
Fig. 4 The improved case library with two levels of detailed data
3.3.1 Template search
In this study, template search as the first step of this CBR retrieval stage is to avoid redundant similarity calculation between irrelevant cases in case library. The mission of this step is to reduce search space by using some certain general attributes, for instance, excluding rotary parts and restricting the search space to prismatic parts only. The predefined phrase for each attribute is used to find the cases, which has the same attributes. Therefore, only pure symbolic attributes can be used as the input of this step of searching because there are a few accountable variations with these attributes. Some impor- tant attributes (mentioned as pure symbolic attributes) that could be used in template search is as follows: the type of workpiece, size of workpiece, machining feature, material of workpiece, locating mode, clamping mode, fixture element type, machine type, machining tool, machining precision re- quirement, etc.
In this method, the preferred attributes for this step are workpiece type, size of workpiece, machine type, axis of machining, and machining operation. Among symbolic attri- butes, the mentioned attributes highly influence fixture de- signing according to the basic rules of fixture design and machining process for prismatic parts [9]. Secondly, they are the most important available attributes for this step (regarding only having workpiece and process information), although, designer can use more attributes in this step of case searching if necessary. The cases in case library, which are matched with this query, will be used in the next step to find the optimum case/s among them to be used for resolving fixture design problem. If many attributes are used in this step, it is likely to not find a set of matched cases, then the case library should be searched again by using fewer attributes.
3.3.2 The closest method
The closest method is the main step and heart of retrieval stage, which affects highly the whole performance of the proposed CBR. The mission of this step is to find the most similar case in database which is the main objective of all CBR-based fixture design applications. This step is performed by a function which is derived using a distance metric method.
The function needs to measure the similarity (distance) be- tween the target problem and the cases in database in order to find the proper case as the solution. In this research, Euclidean distance as distance metric is used. The Euclidean distance is the ordinary distance between two points in space, as given by the Pythagorean formula [23]. This distance is an aggregation operator that uses order-inducing variables in the reordering of the arguments. Thus, it is possible to consider more complex reordering processes that can describe the problem in a more complete way such as fixture design problems [24]. Moreover, in real applications, it is seldom the case that the Euclidean
distances of two cases are exactly the same which can help in improving the accuracy of result [25].
At this step, the cases that are found in the first stage are used in order to find the optimum case. This step ranks the workpieces in the case library regarding the degree of simi- larity relating to the target workpiece. In this function, the distance is used instead of similarity so that each workpiece that has been compared would have a distance value regarding the target workpiece. The larger the distance, the less similar is the compared workpiece to the target workpiece. Best fixture designs for the target workpiece are those that their work- pieces have small distance value.
According to fixture design procedure, in this function, some numerical and symbolic attributes are considered. Gen- erally, symbolic attributes are converted to numbers and the rest of attributes are normalized before use at this function. As mentioned before, the fixture design is affected by sev- eral factors, but only important factors are considered.
The key drivers for fixture design that are taken into account are the workpiece dimensions, machining opera- tion, type and number of each machining features, and machining feature dimensions. In the two next paragraphs, it is discussed why these mentioned factors are important and considerable.
This research is restricted to prismatic parts in case of workpiece type. Commonly, fixtures are designed for this kind of parts and also for clearness; only this type of parts is considered. About machining operations, drilling and milling operations are taken into account because fixtures for these machining operations are most often required. Other machin- ing operations such as turning, grinding, etc. usually can be performed using universal fixtures, which are already part of machine tool accessories [26].
The machining features are limited to pockets, through slot, notch, slot, steps, and holes which can be described easily. Each of the mentioned features can be easily described by the values of their parame- ters such as height, width, length, and radius [27].
Secondly, the case studies and the cases stored in the database have mentioned machining features. In this research, regular surfaces of machining features are con- sidered. The reason is that the machining of irregular contoured surfaces mostly required four- or five-axis machines so that this type of machine usually uses universal fixtures, which is a part of machines themselves. If the sec- tional curves of a surface only include lines or quadratic curves, they can be called regular surface. Otherwise, they would be called irregular surface [28]. And also, the consid- ered features are important from machining criteria especially in case of making a prismatic part. All the considered features are shown in Fig.5.
In order to derive the distance function using for similarity measurement, let p∈P and q∈Q be the two feature sets
corresponding to workpiece P and Q. Next, P and Q are compared by the following distance function:
d P;ð QÞ ¼ Xn
i¼1d pð i;qiÞ n þwfm
fmP−fmQ 2 Mean fmP;fmQ
2þ wfd
fdP−fdQ 2 Mean fdP;fdQ
2þwh
hP−hQ 2 Mean hP;fQ
2þ
ww wP−wQ2
Mean wP;wQ2þwl lP−lQ2 Mean lP;lQ2
ð1Þ d(pi,qi) is the distance betweenpiandqi. The details of this parameter are explained in the next paragraph. In the second part of the formula,fmP,fmQare the number of milling features for the machining surface of partsPandQ, respectively, that the machining operations are supposed to be done on that.
In the third section of formula (1), fdP,fdQ are the number of drilling features for the machining surface of partsPandQ. In the next sections, the dimensions of the workpiecesPandQare compared, height, width, and length.
Generally,Mean(x,y) gives the average values between the variables in parentheses. Subsequently,d(pi,qi) is calculated through the formula (2):
d pð i;qiÞ ¼wv vp−vq
2
þwcðn pð Þ−n qð ÞÞ2 ð2Þ Function 2 is to compare machining features of the setup.
In this function,vprepresents the normalized volume of fea- tures of the part surfaces being compared and explains in detail in the next paragraph.n(p) is the number of one type of feature on the workpiece surface. The reason behind in- cluding“n”is the distribution of machining features on the machining surface has high impact on fixture design. Second- ly, by using“n,”the number of total features is taken into account by separate effect on fixture designing. The volume of
each machining feature type is normalized using the average volume of all features in one setup of two parts as follows.
VPQ¼VpþVQ
2 ð3Þ
vp¼ v0p VPQ
ð4Þ
vq¼ v0q
VPQ ð5Þ
VP,VQ are the volume of all features of the considered surfaces for partPandQ. vp′,vq′ are the volume of one specific type of feature in the surface of the comparing parts. To calculate the volume of each feature, the height, width, length, and radius of all features are used. Generally, in this distance function, the machining features, which are going to be ma- chined, are taken into account not all existing machining features on that setup. In this section of function, quadratic mean is used because each workpiece may have many fea- tures, and attributes of all features should be compared one by one, but not independently.
Based on the given distance by function (1), the distance between each member of selected workpieces set with the target workpiece is calculated. Then, the workpiece/s based on the distance value are ranked. The workpiece/s with small distance is/are chosen to use for retrieving appropriate fixture design from case library level 2. The fixture design with the smallest distance is taken by the case reuse stage. The reuse stage suggests the fixture design with the workpiece to the designer as the solution. The other ranked workpieces, which have small distance value with their fixture designs, are shown to the designer with rank ordering as help to perform adaption or modification stage if needed. The process the proposed CBR is explained by a case study in the next section. The technical framework of the proposed case retrieval and data pretreatment process is shown in Fig.6.
4 Case study and processing steps of the system
The case library consists of 40 cases. For validation purposes, only ten cases were used in this report due to highly time- consuming activity that takes several hours to a few days per case. The purpose of this analysis is to show that the output of the retrieval strategy proposed in this research, and the fixture design estimation correlates well to each other. It is believed that ten workpiece-fixture designs are sufficient for evaluating the applicability and performance of the proposed system. In Fig. 5 Features considered.apocket,bslot,cnotch,dthrough slot,f
hole, andestep
the subsequent section, a detailed analysis using the proposed CBR method via a case study is illustrated.
Step 1. Input the test workpiece
A prismatic part named test workpiece is used as a case study and shown in Fig.7. A through slot on the top of the workpiece is going to be milled that is parallel to the sides, and the machine for this opera- tion is a vertical milling manufacturing. The detailed information of the test workpiece can be extracted from 3D CAD model and CAPP file automatically or input manually. The input information to the system is shown in Table1.
Step 2. Indexing and retrieving similar workpieces
First retrieval stage: A few important attributes for template retrieval step are used to separate workpieces
with the same attributes (only considered ones) in level 1 of the case library to the test workpiece. Five attributes are used for this step such as (1) workpiece type, (2) size of workpiece, (3) machine type, (4) axis of machining, and (5) machining operation. For test workpiece, the attributes are as follows: prismatic part, medium-size, milling machine, vertical ax- is machine, and milling, respectively, as shown in Table 2. At this stage, five workpieces out of ten in database which do not have these attributes in common with the test workpiece are excluded. It shows the applicability of template search. Then, the rest of workpieces in database which are five workpieces are sent to the next stage as the main step to find the solution among them regarding fix- ture design criteria.
Data pre-treatment
Input data
Closest Method
Calculang Euclidean distance between input workpiece and each one of selected cases
Number of Milling Features
Number of Drilling Features
Volume of Features Dimensions
of Workpiece Template Search
Match the symbolic aributes with cases in database
Select cases with the same aributes (selected cases set)
Numeric-Symbolic aributes Symbolic aributes
Work piece type, size of work piece, machine type,
axis of machining,
machining
Symbolic Aributes Numeric Aributes
Convert to Numbers Normalizaon
Prepared Data
Select the case with the smallest distance as the soluon
Retrieve fixture of the selected case to be used for the target workpiece
Perform required modificaon and adjustment of fixture components
Output Fixture Design Fig. 6 The technical framework
of the proposed case retrieval and data pretreatment process
Second retrieval stage: The found cases in the first stage are used in this stage that is shown in Fig. 8.
This stage is the most important part of the process, which is to find the most similar case among the cases as the solution by the closest method. Therefore, the distance/similarity between the test workpiece and the found cases are measured. Hence, all attributes of test workpiece with all found cases are compared one by one, and the summation of the distances of each pair attribute (test workpiece and compared one) finally yields the total distance/similarity value between the workpieces and the test workpiece. The detailed pro- cedure of closest method has been explained in Sect.
3.3.2. The result of this stage, which is the distance between the found workpieces and the test workpiece, is shown in Table2. The numbers in Table2show how close (similar) the test workpiece to other ones.
Accordingly, the workpieces can be ranked as the main output of this system, which is the basis of all the CBR-based fixture design applications.
In CBR-based fixture design applications, the mat- ter is the rank ordering of workpieces by the system form which the system suggests the fixture design
solution. In order to compare this method with the two significant previous methods, the same work- pieces (shown in Fig.8) are used in comparing the methods. The previous methods are based on a value that is between 0 and 1 such that if the value is 0, it means that there is no similarity, and the value of 1 means identically similar, although the output is the workpiece rank ordering not the numbers. The output of the methods which is workpiece rank ordering is shown in Fig.9. The second column is the benchmark [18], third column is our proposed method output, fourth column is Chebyshev-based method output, and fifth column is the cosine formula-based method output. The desire output is the workpiece rank or- dering according to benchmark from which the sys- tem is able to suggest the best fixture design solution;
otherwise, the system is unable to find the best solu- tion in database. As shown in Fig.9, the cosine-based method suggested four workpieces and the Chebyshev-based method suggested three work- pieces as the best solutions while the best solution is only the first rank workpiece of the benchmark. But, the proposed method was able to find the best solution and moreover rank the rest of the workpieces same as benchmark. The advantage of the right workpiece rank ordering is explained in step 4.
Fig. 7 CAD model of test workpiece to be machined as case study
Table 1 The input information of test part to the system
Workpiece type Prismatic Machine type Milling machine Axis of machining Horizontal Machining operation Milling Workpiece size Medium-size
Height 50 mm
Width 90 mm
Length 130 mm
Through slot height 90 mm Through slot width 25 mm Through slot length 23 mm
Table 2 The result of
the closest method Part number The distance value with the test workpiece
2 0
4 1.37
3 2.22
5 4.34
1 5.74
Step 3. Fixture design reuse
Based on the proposed method, the workpiece rank ordering and their fixture designs, which are stored in the first and second level of database, respectively, are shown in Fig. 10. So, the most similar workpiece is the first one in the ranking list.
Therefore, it is navigated to the level 2 of case library in order to retrieve the fixture design of the work- piece for reusing. The retrieved information from level 2 includes all information to from a fixture design such as 3D models and drawing of fixture design and all detail design of every fixture compo- nents. As the distance between the test workpiece and the suggested solution is very small (high
similarity), the fixture design is proper for the test workpiece and can be reused directly. The suggested fixture design, test workpiece, and constructed final fixture design on the test workpiece are shown in Fig.11.
Step 4 Adaption and modification
When the assembly of new fixture for query work- piece is done, the designer can examine the adaption and modification of the fixture and then verify the design. In this instance, since the suggested case has little difference, the fixture design is appropriate.
Generally, adaption and verification steps are based on the knowledge of the designer. This method can rank the alternative fixture designs properly, so, the
Part 1 Part 2 Part 3
Part 4 Part 5
Fig. 8 The set of found workpieces by template search
Rank ordering
Benchmark The proposed method result (Euclidean
Based)
The Chebyshev based method
result (2011)
The cosine based method result
(2008)
1
2
3
___
4
___ ___
5
___ ___
0.95
1 1
0.92
0.99
Fig. 9 The comparison:second columnis the benchmark [18];
third columnis the proposed method;fourth columnis Chebyshev-based method;fifth columnis cosine formula-based method
fixture designs, which are after the suggested solution in rank ordering, can be optionally retrieved and shown. It helps the designer effectively in case of modification and verification so that he would easily and rapidly arrive at the final design by considering the alternative and similar fixture designs. The ranked fixtures are retrieved and illustrated in Fig.12as help for the modification step if needed.
5 Discussion and results
In Sect. 4, a case study is used to test the proposed system (Fig.7). The output of the test shows the good performance of the system in terms of applicability and more importantly
accuracy (as explained in Sect. 4). As the indexing is the foundation of any CBR based application, it can be concluded that the proposed indexing method is applicable in this CBR- based fixture design application. The reason is that when the whole system especially the retrieval stage performs properly which is constructed on the basis of indexing strategy, it shows the good applicability of the indexing.
The applicability of the template search is checked so that firstly it can sequentially work as a pre-step of retrieval stage with the main step of retrieval stage (closest method as heart of the system), and secondly, it was able to reduce the search space as expected. The applicability of the closest method as main contribution of this research which is the bottleneck of fixture design based applications is proved by considering the output of this method and the benchmark which is shown in Fig. 9. In order to compare the proposed method with two other recent existing methods [1,5], the same test has been done by the two existing methods. Figure9shows the results of three methods and also the benchmark. By comparing the results, which is explained in step 2 of Sect. 4, it indicates that the proposed method is more efficient compared to the two recent existing methods.
6 Conclusion
A CBR method for machining fixture design is proposed. To improve the efficiency of the fixture deign, indexing and retrieval approaches are adopted and then improved. By using
Rankordering Similarity
ranked workpieces
from first level of case
library
The considering fixture design
of the workpieces in second level of
case library
1
2
3
4
5
Fig. 10 The output of the systemsecond columnthe ranked workpieces andthird columnthe corresponding fixtures
Fig. 11 The suggested fixture design, test workpiece, and fixture design of test workpiece (fromlefttoright)
Fig. 12 The found similar workpieces with their fixture designs to help the designer perform modification
the adopted retrieval paradigm, the semi-automated fixture design system is able to find the desirable case in database as the solution and ranks the other similar solutions to use in adaption and modification steps. The proposed indexing ap- proach helps the CBR-based system to arrive at appropriate solution by distinguishing between cases and easily navigat- ing cases in database. A case study has been used to check the performance and efficiency of proposed method. As the result shows, the proposed method worked successfully in case of machining fixture design for prismatic parts. However, system development is undergoing in two areas, first making data extraction automated from part sketch and also research on automated case adaptation method because these two sub- approach can help in achieving more automated fixture design applications.
References
1. Wang H, Rong Y (2008) Case based reasoning method for computer aided welding fixture design. Computer-Aided Design 40 (12):1121–
1132. doi:http://dx.doi.org/10.1016/j.cad.2008.11.001
2. Wang H, Rong Y, Li H, Shaun P (2010) Computer aided fixture design: recent research and trends. Computer-Aided Design 42 (12):
1085–1094. doi:http://dx.doi.org/10.1016/j.cad.2010.07.003 3. Boyle IM, Rong K, Brown DC (2006) CAFixD: A case-based
reasoning fixture design method. Framework and indexing mecha- nisms. Transof the ASME-S-Comput and Inf Sci in Eng 1:40–48 4. Zhou Y, Li Y, Wang W (2011) A feature-based fixture design meth-
odology for the manufacturing of aircraft structural parts. Robot Comput Integr Manuf 27(6):986–993
5. Peng G, Chen G, Wu C, Xin H, Jiang Y (2011) Applying RBR and CBR to develop a VR based integrated system for machining fixture design. Expert Syst with Appl 38(1):26–38
6. Wan N, Wang Z, Mo R (2013) An intelligent fixture design method based on smart modular fixture unit. Int J Adv Manuf Technol:1–21 7. Vasundara M, Padmanaban K (2013) Recent developments on machin- ing fixture layout design, analysis, and optimization using finite element method and evolutionary techniques. Int J Adv Manuf Technol:1–18 8. Kang Y, Rong Y, Yang JC (2003) Computer-aided fixture design
verification. Part 1. The framework and modelling. Int J Adv Manuf Technol 21(10–11):827–835. doi:10.1007/s00170-002-1399-7 9. Nee AYC, Whybrew K, kumar AS (1995) Advanced fixture design
for FMS. Springer-Verlag,
10. Li Q (2009) Virtual reality for fixture design and assembly.
University of Nottingham, Nottingham
11. Nnaji BO, Alladin S, Lyu P (1988) A framework for a rule-based expert fixturing system for face milling planar surfaces on a CAD system using flexible fixtures. J Manuf Syst 7(3):193–207 12. kumar AS, Fuh JYH, Kow TS (2000) An automated design and
assembly of interference-free modular fixture setup. Comput Aided Des 32(10):583–596
13. Nee AYC, Tao ZJ, Kumar AS (2004) An advanced treatise on fixture design and planning, vol 1. World Scientific Publishing Company, Singapore
14. Subramaniam V, Senthil Kumar A, Seow KC (1999) Conceptual design of fixtures using genetic algorithms. Int J Adv Manuf Technol 15(2):79–84. doi:10.1007/s001700050042
15. Senthil Kumar A, Subramaniam V, Boon Teck T (2000) Conceptual design of fixtures using machine learning techniques. Int J Adv Manuf Technol 16(3):176–181. doi:10.1007/s001700050024 16. Sun SH, Chen JL (1996) A fixture design system using case-based
reasoning. Eng Appl Artif Intell 9(5):533–540
17. Li W, Li P, Rong Y (2002) Case-based agile fixture design. J Mater Process Technol 128(1–3):7–18
18. Liqing F, Kumar AS (2005) XML-based representation in a CBR system for fixture design. Comput-Aided Des & Appl 2(1–4):339–
348
19. Boyle I, Rong Y, Brown DC (2011) A review and analysis of current computer-aided fixture design approaches. Robot Comput Integr Manuf 27(1):1–12
20. Price S (2009) A study of case based reasoning applied to welding computer aided fixture design. Worcester Polytechnic Institute, Worcester
21. Perner P (2002) Are case-based reasoning and dissimilarity-based classification two sides of the same coin? Eng Appl Artif Intell 15(2):
193–203
22. Aamodt A, Plaza E (1994) Case-based reasoning: foundational is- sues, methodological variations, and system approaches. AI Commun 7(1):39–59
23. Potolea R, Cacoveanu S, Lemnaru C (2011) Meta-learning frame- work for prediction strategy evaluation. In: Enterprise Information Systems. Springer, pp 280–295
24. Merigó JM, Casanovas M (2011) Induced aggregation operators in the Euclidean distance and its application in financial decision mak- ing. Expert Syst with Appl 38(6):7603–7608
25. Qian G, Sural S, Pramanik S A comparative analysis of two distance measures in color image databases. In: Image Processing. 2002.
Proceedings. 2002 International Conference on, 2002. IEEE, pp I-401-I-404 vol. 401
26. VukelićD, TadićB, HodoličJ, Križan P, SimeunovićN (2009) Development of an intelligent system for fixture design using case- based reasoning (CBR) technique. J of Manuf Eng 8(4):8–11 27. Cardone A, Gupta SK, Deshmukh A, Karnik M (2006) Machining
feature-based similarity assessment algorithms for prismatic ma- chined parts. Comput Aided Des 38(9):954–972
28. Sheen B-T, You C-F (2006) Machining feature recognition and tool- path generation for 3-axis CNC milling. Computer-Aided Design 38(6):553–562