Modeling and Design of Manufacturing Systems

As representational models, artificial neural networks are particularly useful for modeling systems whose underlying properties are too complex, too obscure, too costly, or too time-consuming to be modeled analytically using traditional methods. The use of neural networks for modeling and design of manu-facturing systems includes manumanu-facturing decision making, product design storage and retrieval in group technology, and formation of part families and machine cells for the design of cellular manufacturing systems.

Chryssolouris et al. [3] applied neural networks, in conjunction with simulation models, for resource allocation in job-shop manufacturing systems. Feedforward neural networks called multilayer perceptrons trained using the popular backpropagation (BP) algorithm were used to learn the inverse mapping of the simulation task: given desired performance measure levels, the neural networks output suitable values for the parameters of resources. Based on results generated by a simulator, the neural networks were demon-strated to be able to find a suitable allocation for the resources to achieve given performance levels. In a related work, Chryssolouris et al. [4] applied neural networks, also in conjunction with simulation models, to determine operational policies for hierarchical manufacturing systems under a multiple criteria decision making framework called MAnufacturing DEcision MAking (MADEMA). Multilayer perceptrons were used to generate appropriate criterion weights for an entire sequence of multiple criteria decisions on manufacturing policies. This neural network approach is more appropriate for complex applications entail-ing chains of decisions, such as job-shop schedulentail-ing, whereas conventional methods are preferable for sentail-ingle or isolated decisions. Madey et al. [5] used a neural network embeded in a general-purpose simulation system for modeling Continuous Improvement Systems (CIS) policies in manufacturing systems. A mul-tilayer feedforward neural network trained using the BP algorithm was used to facilitate the identification of an effective CIS policy and to provide a realistic simulation framework to enhance the capabilities of simulations. The trained neural network was embedded in the simulation model code, so that the model had intrinsic advisory capability to reduce time or complexity for linking with external software. The results demonstrated not only the feasibility, but also the promising effectiveness of the combination of neural computation within simulation models for improving CIS analysis.

The crux behind group technology (GT) is to group similar parts that share common design and/or manufacturing features into part families and bring dissimilar machines together and dedicate them to the manufacture of one or more part families. GT is an important step toward the reduction of throughput time, work-in-process inventory, investment in material handling, and setup time, thus resulting in an increase of productivity vital to survive in an increasingly competitive environment and changing customer preferences. The success of GT implementation depends largely on how the part families are formed and how machines are grouped. Numerous methods exist to solve the GT problem, each with its own limitations. As alternatives, neural networks have been proposed to provide solutions to the GT problem.

Kamarthi et al. [6] used a multilayer perceptron as an associative memory for storage and retrieval of design data in group technology. Design data in the gray-level pixel representations of design drawings were stored in the neural associative memory. The simulation results reported in this paper showed that the neural network trained using the BP algorithm was able to generate the closest stored part given the geometric characteristics of new parts. The fault tolerance capability of neural networks is particularly instrumental for cases where only partial or inexact information is available. The neural network approach is useful for the standardization of product design and process planning. A weakness of the proposed

approach is the lack of ability for translation, scale, and rotation invariant recognition of parts, which are essential for handling part drawings.

In Kaparthi and Suresh’s work [7], a multilayer feedforward neural network trained with the BP algorithm was employed to automate the classification and coding of parts for GT applications. Given the pixel representation of a part drawing extracted from computer-aided design (CAD) systems, the neural network was able to output the Opitz codes related to the part geometric information. The work is not limited to rotational parts and may be used for nonrotational parts. Nevertheless, code generation based on features other than shapes (e.g., material type) would require the neural network to be supple-mented with other algorithms/procedures.

Moon and Roy [8] introduced a neural network approach to automating part-family classification in conjunction with a feature-based solid modeling system. The part features extracted from a model or object database were used to train and test a multilayer feedforward neural network. Trained using the BP algorithm, the neural network neurons signify an appropriate part family for each part. Besides overcoming some limitations of traditional coding and classification methods, this approach offers more flexibility and faster response.

Venugopal and Narendran [9] applied the Hopfield network to design storage and retrieval for batch manufacturing systems. Binary matrix representations of parts based on geometric shapes were stored in the Hopfield network. Test cases carried out on rotational and nonrotational parts showed the high percentage of correct retrieval of stored part information using the neural network. The retrieval rapidity is another major advantage of the neural network model. Such a storage/retrieval system could benefit the design process by minimizing duplications and variety, thus increasing productivity of both designer and planner, aiding standardization, and indirectly facilitating quotations. Furthermore, this approach offers flexibility and could adjust to changes in products. Unfortunately, the limited capacity of the Hopfield network constrained the possible number of stored designs.

Chakraborty and Roy [10] applied neural networks to part-family classification based on part geo-metric information. The neural system consisted of two neural networks: a Kohonen’s SOM network and a multilayer feedforward network trained using the BP algorithm. The former was used to cluster parts into families and provide data to train the latter to learn part-family relationships. Given data not contained in the training set, the feedforward neural network performed well with an accuracy of 100%

in most of test cases.

Kiang et al. [11] used the self-organizing map (SOM) network for part-family grouping according to the operation sequence. An operation sequence based similarity coefficient matrix developed by the authors was constructed and used as the input to the SOM network, which clustered the parts into different families subsequently. The performance of the SOM network approach was compared with two other clustering techniques, the k-th nearest neighbor (KNN) and the single linkage (SLINK) clustering methods for problems varying from 19 to 200 parts. The SOM-network-based method was shown to cluster the parts more uniformly in terms of number of parts in each family, especially for large data set.

The training time for the SOM network was very time-consuming, though the trained network can perform clustering in very short time.

Wu and Jen [12] presented a neural-network-based part classification system to facilitate the retrieving and reviewing similar parts from the part database. Each part was represented by its three projection views in the form of rectilinear polygons. Every polygon was encoded into a feature vector using the skeleton standard tree method, which was clustered to a six-digit polygon code by a feedforward neural network trained by the BP algorithm. By comparing the polygon codes, parts can be grouped hierarchi-cally into three levels of similarity. For parts with all three identical polygon codes, they were grouped into a high degree similarity family. For parts shared one identical polygon code, they were grouped into a low degree similarity family. The rest of the parts were put into a medium degree similarity family.

Searching from the low degree of similarity family to the high degree of similarity family would help designers to characterize a vague design.

Based on the interactive activation and competitive network model, Moon [13] developed a competitive neural network for grouping machine cells and part families. This neural network consists of three layers

of neurons. Two layers correspond respectively to the machines (called machine-type pool) and parts (called type pool), and one hidden layer serves as a buffer between the machine-type pool and part-type pool. Similarity coefficients of machines and parts are used to form the connection weights of the neural network. One desirable feature of the competitive neural network, among others, is that it can group machine cells and part families simultaneously. In a related work, Moon [14] showed that a competitive neural network was able to identify natural groupings of part and machine into families and cells rather than forcing them. Besides routing information, design similarities such as shapes, dimensions, and tolerances can be incorporated into the same framework. Even fuzziness could be represented, by using variable connection weights. Extending the results in [13, 14], Moon and Chi [15] used the com-petitive neural network developed earlier for both standard and generalized part-family formation. The neural network based on Jaccard similarity coefficients is able to find near-optimal solutions with a large set of constraints. This neural network takes into account operations sequence, lot size, and multiple process plans. This approach proved to be highly flexible in satisfying various requirements and efficient for integration with other manufacturing functions. Currie [16] also used the interactive activation and competition neural network for grouping part families and machines cells. This neural network was used to define a similarity index of the pairwise comparison of parts based on various design and manufacturing characteristics. Part families were created using a bond energy algorithm to partition the matrix of part similarities. Machine cells were simply inferred from part families. The neural network simulated using a spreadsheet macro showed to be capable of forming part families.

Based on the ART-1 neural network, Kusiak and Chung [17] developed a neural network model called GT/ART for solving GT problems by block diagonalizing machine-part incidence matrices. This work showed that the GT/ART neural network is more suitable for grouping machine cells and part families than other nonlearning algorithms and other neural networks such as multilayer neural networks with the BP learning algorithm. The GT/ART model allows learning new patterns and keeping existing weights stable (plasticity vs. stability) at the same time. Kaparthi and Suresh [18] applied the ART-1 neural network for clustering part families and machine cells. A salient feature of this approach is that the entire part-machine incidence matrix is not stored in memory, since only one row is processed at a time. The speed of computation and simplicity of the model offered a reduction in computational complexity together with the ability to handle large industrial size problems. The neural network was tested using two sets of data, one set from the literature and the other artificially generated to simulate industrial size data. Further research is required to investigate and enhance the performance of this neural network in the case of imperfect data (in the presence of exceptional elements).

Liao and Chen [19] evaluated the ART-1 network for part-family and machine-cell formation. The ART-1 network was integrated with a feature-based CAD system to automate GT coding and part-family formation. The process involves a three-stage procedure, with the objective of minimizing operating and material handling costs. The first stage involved an integer programming model to determine the best part routing in order to minimize operating costs. The first stage results in a binary machine-part incidence matrix. In the second stage, the resulting incidence matrix is then input to an ART-1 network that generates machine cells. In the last stage, the STORM plant layout model, an implementation of a modified steepest descent pairwise interchange method is used to determine the optimal layout. The limitation of the approach was that the ART-1 network needs an evaluation module to determine the number of part families and machine cells.

Extending their work in [18], Kaparthi et al. [20] developed a robust clustering algorithm based on a modified ART-1 neural network. They showed that modifying the ART-1 neural network can improve the clustering performance significantly, by reversing zeros and ones in incidence matrices. Three perfectly block diagonalizable incidence matrices were used to test the modified neural network. Further research is needed to investigate the performance of this modified neural network using incidence matrices that result in exceptional elements.

Moon and Kao [21] developed a modified ART-1 neural network for the automatic creation of new part families during a part classification process. Part families were generated in a multiphase procedure interfaced with a customized coding system given part features. Such an approach to GT allows to

maintain consistency throughout a GT implementation and to perform the formation and classification processes concurrently.

Dagli and Huggahalli [22] pointed out the limitations of the basic ART-1 paradigm in cell formation and proposed a modification to make the performance more stable. The ART-1 paradigm was integrated with a decision support system that performed cost/performance analysis to arrive at an optimal solution.

It was shown that with the original ART-1 paradigm the classification depends largely on order of presentation of the input vectors. Also, a deficient learning policy gradually causes a reduction in the responsibility of patterns, thus leading to a certain degree of inappropriate classification and a large number of groups than necessary. These problems can be attributed to the high sensitivity of the paradigm to the heuristically chosen degree of similarity among parts. These problems can be solved by reducing the sensitivity of the network through applying the input vectors in the order of decreasing density (measured by the number of 1’s in the vector) and through retaining only the vector with the greatest density as the representative patterns. The proposed modifications significantly improved the correctness of classification.

Moon [23] took into account various practical factors encountered in manufacturing companies, including sequence of operations, lot size, and the possibility of multiple process plans. A neural network trained with the BP algorithm was proposed to automate the formation of new family during the classification process. The input patterns were formed using a customized feature-based coding system.

The same model could easily be adapted to take more manufacturing information into consideration.

Rao and Gu [24] combined an ART neural with an expert system for clustering machine cells in cellular manufacturing. This hybrid system helps a cell designer in deciding on the number and type of duplicate machines and resultant exceptional elements. The ART neural network has three purposes.

The first purpose is to group the machines into cells given as input the desired number of cells and process plans. The second purpose is to calculate the loading on each machine given the processing time of each part. The last purpose of the neural network is to propose alternative groups considering duplicate machines. The expert system was used to reassign the exceptional elements using alternate process plans generated by the neural network based on processing time and machine utilization. The evaluation of process plans considered the cost factors of material handling, processing, and setup.

Finally, the neural network was updated for future use with any changes in machine utilization or cell configuration.

Rao and Gu [25] proposed a modified version of the ART-1 algorithm to machine-cell and part-family formation. This modified algorithm ameliorates the ART-1 procedure so that the order of presentation of the input pattern no longer affects the final clustering. The strategy consists of arranging the input pattern in a decreasing order of the number of 1’s, and replacing the logic AND operation used in the ART-1 algorithm, with an operation from the intersection theory. These modifications significantly improved the neural network performance: the modified ART-1 network recognizes more parts with similar processing requirements than the original ART-1 network with the same vigilance thresholds.

Chen and Cheng [26] added two algorithms in the ART-1 neural network to alleviate the bottleneck machines and parts problem in machine-part cell formation. The first one was a rearrangement algorithm, which rearranged the machine groups in descending order according to the number of 1’s and their relative position in the machine-part incidence matrix. The second one was a reassignment algorithm, which reexamined the bottleneck machines and reassigned them to proper cells in order to reduce the number of exceptional elements. The extended ART-1 neural network was used to solve 40 machine-part formation problems in the literature. The results suggested that the modified ART-1 neural network could consistently produce a good quality result.

Since both original ART-1 and ART-2 neural networks have the shortcoming of proliferating categories with a very few patterns due to the monotonic nonincreasing nature of weights, Burke and Kamal [27]

applied the fuzzy ART neural network to machine-part cell formation. They found that the fuzzy ART performed comparably to a number of other serial algorithms and neural network based approaches for part family and machine cell formation in the literature. In particular, for large size problem, the resulting solution of fuzzy ART approach was superior than that of ART-1 and ART-2 approaches. In an extended

work, Kamal and Burke [28] developed the FACT (fuzzy art with add clustering technique) algorithm based on an enhanced fuzzy ART neural network to cluster machines and parts for cellular manufac-turing. In the FACT algorithm, the vigilance and the learning rate were reduced gradually, which could overcome the proliferating cluster problem. Also, the resultant weight vector of the assigned part family were analyzed to extract the information about the machines used, which enabled FACT to cluster machines and parts simultaneously. By using the input vector that combining both the incidence matrix and other manufacturing criteria such as processing time and demand of the parts, FACT could cluster machines and parts with multiple objectives. The FACT was tested with 17 examples in the literature.

The results showed that FACT outperformed other published clustering algorithms in terms of grouping efficiency.

Chang and Tsai [29] developed an ART-1 neural-network-based design retrieving system. The design being retrieved was coded to a binary matrix with the destructive solid geometry (DSG) method, which was then fed into the ART-1 network to test the similarity to those in the database. By controlling the vigilance parameter in the ART-1 network, the user can obtain a proper number of reference designs in the database instead of one. Also, the system can retrieve a similar or exact design with noisy or incomplete information. However, the system cannot process parts with protrusion features where additional oper-ations were required in the coding stage.

Enke et al. [30] realized the modified ART-1 neural network in [22] using parallel computer for machine-part family formation. The ART-1 neural network was implemented in a distributed computer with 256 processors. Problems varying from 50350 to 2563256 (machines3parts) were used to evaluate the performance of this approach. Compared with the serial implementation of the ART-1 neural network in one process, the distributed processor based implementation could reduce the processing time from 84.1 to 95.1%. Suresh et al. [31] applied the fuzzy ART neural network for machines and parts clustering with the consideration of operation sequences. A sequence-based incidence matrix was introduced, which

Enke et al. [30] realized the modified ART-1 neural network in [22] using parallel computer for machine-part family formation. The ART-1 neural network was implemented in a distributed computer with 256 processors. Problems varying from 50350 to 2563256 (machines3parts) were used to evaluate the performance of this approach. Compared with the serial implementation of the ART-1 neural network in one process, the distributed processor based implementation could reduce the processing time from 84.1 to 95.1%. Suresh et al. [31] applied the fuzzy ART neural network for machines and parts clustering with the consideration of operation sequences. A sequence-based incidence matrix was introduced, which