Performance of Fuzzy ART neural network for group technology cell formation

This study investigates the performance of Fuzzy ART neural network for grouping parts and machines, as part of the design of cellular manufacturing systems. Fuzzy ART is compared with ART1 neural network and a modification to ART1, along with direct clustering analysis (DCA) and rank order clustering (ROC2) algorithms. A series of replicated clustering experiments were performed, and the efficiency and consistency with which clusters were identified were examined, using large data sets of differing sizes and degrees of imperfection. The performance measures included the recovery ratio of bond energy and execution times, It is shown that Fuzzy ART neural network results in better and more consistent identification of block diagonal structures than ART1, a recent modification to ART1, as well as DCA and ROC2. The execution times were found to be more than those of ART1 and modified ART1, but they were still superior to traditional algorithms for large data sets.     

Machine-component cell formation in group technology: a neural network approach

This paper presents a neural network clustering method for the part-machine grouping problem in group technology. Among the several neural networks, a Carpenter-Grossberg network is selected due to the fact that this clustering method utilizes binary-valued inputs and it can be trained without supervision. It is shown that this adaptive leader algorithm offers the capability of handling large, industry-size data sets due to the computational efficiency. The algorithm was tested on three data sets from prior literature, and solutions obtained were found to result in block diagonal forms. Some solutions were also found to be identical to solutions presented by others. Experiments on larger data sets, involving 10000 parts by 100 machine types, revealed that the method results in the identification of clusters with fast execution times. If a block diagonal structure existed in the input data, it was identified to a good degree of perfection. It was also found to be efficient with some imperfections in the data     

The Tabletting Properties of Dika Fat Lubricant

Abstract

The lubricant property of dika fat, a solid vegetable oil extracted from the kernels of Irvingiaqabonensis var gabonensis and var excelsia was investigated. An instrumented tablet machine (ITM) was used to evaluate the effect of dika fat on the unit ejection force (EJF/A) of a model direct compression formulation. Dika fat, at equivalent concentration levels, performed better than magnesium stearate, stearic acid and a hydrogenated vegetable oil STEROTEX, in reducing EJF/A of tablets compressed from the model direct compression formulation. Dika fat imparted no adverse effect on the hardness, disintegration and dissolution of directly compressed hydrochlorothiazide tablets prepared in this study.     

A sequence-based materials flow procedure for designing manufacturing cells

One of the most important problems in designing a cellular manufacturing system is the formation of machine cells and the grouping of parts. Most of the existing cell design procedures operate on a binary part-machine incidence matrix to identify block diagonality. Important factors such as part operation sequence and demand volume have not been commonly considered in past research. In this paper, a sequence-based materials flow procedure is developed to solve the cell formation problem. The new cell formation procedure is designed to consider operation sequences in accurately determining the costs of inter-cell movement, as well as forward and backward intra-cell movements. Extensive comparisons of the new cell formation procedure with the relevant existing procedures in the literature demonstrate the effectiveness of the new procedure.     

Cell formation problem with consideration of both intracellular and intercellular movements

The cellular manufacturing system (CMS) is a well-known strategy which enhances production efficiency while simultaneously cutting down the system-wide operation cost. Most of the researchers have been focused on developing different approaches in order to identify machine-cells and part-families more efficiently. In recent years, researchers have also focused their studies more scrupulously by collectively considering CMS with production volume, operation sequence, alternative routing or even more. However, very few of them have tried to investigate both the allocation sequence of machines within the cells (intra-cell layout) and the sequence of the formed cells (inter-cell layout). Solving this problem is indeed very important in reducing the total intracellular and intercellular part movements which is especially significant with large production volume.

In this paper, a two-phase approach has been proposed to tackle the cell formation problem (CFP) with consideration of both intra-cell and inter-cell part movements. In the first phase, a mathematical model with multi-objective function is formed to obtain the machine cells and part families. Afterwards, in the second phase, another mathematical model with single-objective function is presented which optimizes the total intra-cell and inter-cell part movements. In other words, the scope of problem has been identified as a CFP together with the background objective of intra-cell and inter-cell layout problems (IAECLP). The primary assumption for IAECLP is that only linear layouts will be considered for both intra-cell and inter-cell. In other words, the machine within cells and the formed cells are arranged linearly. This paper studies formation of two mathematical models and used the part-machine incidence matrix with component operational sequence.

The IAECLP is considered as a quadratic assignment problem (QAP). Since QAP and CFP are NP-hard, genetic algorithm (GA) has been employed as solving algorithm. GA is a widespread accepted heuristic search technique that has proven superior performances in complex optimization problems and further it is a popular and well-known methodology. The proposed algorithms for CFP and IAECLP have been implemented in JAVA programming language.     

Sequence-dependent clustering of parts and machines: A Fuzzy ART neural network approach

This study addresses the problem of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems, and for streamlining material flows in general. A pattern recognition approach based on artificial neural networks is proposed, and it is shown that the Fuzzy ART neural network can be effectively utilized for this application. First, a representation scheme for operation sequences is developed, followed by an illustrative example. A more comprehensive experimental verification, based on the mixture-model approach is then performed to evaluate its performance. The experimental factors include size of the part-machine matrix, proportion of voids, proportion of exceptional elements, and vigilance threshold. It is shown that this neural network is effective in identifying good clustering solutions, consistently and with relatively fast execution times.     

A variable-parameter unsupervised learning clustering neural network approach with application to machine-part group formation

In this paper, we develop a new unsupervised learning clustering neural network method for clustering problems in general and for solving machine-part group formation problems in particular. We show that our new approach solves a very challenging problem in the area of machine-part group formation. A review of machine-part group formation methods and unsupervised learning artificial neural network methods is given. We modify the well-known competitive learning algorithm by using the generalized Euclidean distance, and a momentum term in the weight vector updating equations. The cluster structure can be adjusted by changing the coefficients in the generalized Euclidean distance. The algorithm is flexible and applicable to many practical problems. We also develop a neural network clustering system which can be used to cluster a 0-1 matrix into diagonal blocks. The developed neural network clustering system is independent of the initial matrix and gives clear final clustering results which specify the machines and parts in each group. We use the developed neural network clustering system to solve several machine-part group formation problems, in which the machine-part incidence matrix is to be clustered into a diagonal block structure. An algorithm is developed to consider lower and upper bounds on the number of machines for each cell. The computational results are compared with those from the well-known rank order clustering and directive clustering analysis methods.     

Preparation of Gelatin: Phenytoin Sodium Microsphers: An IN VITRO and IN VIVO Evaluation

Abstract

In this study phenytoin sodium microspheres were formulated with biodegradable acid-treated gelatin. The microspheres were subjected to in vitro and in vivo testing. The percent drug retained in the microspheres, as well as its release from the microspheres, was tested. In vitro data revealed a decrease in percent druq retained in the microspheres with an increase in addition of glutaraldehyde to the microsphere formulations. The statistically most consistent drug release was observed from formulations containing 10 g of gelatin and 2 g of phenytoin sodium. From this formulation the slowest release was observed when 5 ml of glutaraldehyde were added to the various formulations, whereas the fastest release was observed when no glutaraldehyde was added. In vivo studies consisted of administering phenytoin sodium in microsphere form and an aqueous solution v i a various routes of administration and determining phenytoin plasma concentration vs. time profiles in female Sprague Dawley Rats. Computer fitting of the in vivo data and subsequent statistical testing enabled comparison of the effect of microsphere formation and the effect of microsphere dose on selected pharmacokinetic parameters.     

Integrated use of fuzzy c-means and fuzzy KNN for GT part family and machine cell formation

This paper presents a new approach for GT part family and machine cell formation. It involves the integrated use of two fuzzy clustering algorithms: fuzzy c-means and fuzzy k-nearest neighbours. It is shown that the proposed approach performs better than using fuzzy c-means alone or FACT (Kamal and Burke) in terms of some commonly used measures such as grouping efficacy, grouping index, number of voids, number of exceptional elements, and number of bottleneck machines. The approach is developed a result of our quest for a better clustering algorithm to process non-binary data and to produce a non-binary classification in the domain of GT applications. These features are deemed important to handle imprecise data and to provide a higher degree of flexibility in the operation stage.     

A method for finding minimal bottle-neck cells for grouping part-machine families†

The selection of parts and machines poses an important problem in the design and planning phases of cellular manufacturing and flexible manufacturing systems. In most real-life situations, this grouping invariably leads to 'bottleneck’ parts and machines. This paper discusses a method of identifying the minimal number of bottle-neck cells (machines or parts) which, when dealt with through either duplication of machines or subcontracting of parts, will result in perfect part-machine groupings with no overlap. The polynomially bounded algorithms used in the analysis are oriented towards finding minimal cut-nodes in either partition of the bipartite part-machine graph.

     

Medicament Release from Suppository Bases: III. Ibuprofen - Physicochemical Characteristics and Bioavailability in Rabbits

Abstract

This investigation was designed to determine the invitro release of ibuprofen from suppository bases, and their invivo bioavailability in rabbits. Suppositories containing ibuprofen were made by the fusion method with Theobroma oil, Witepsol H-15 and PEG 1540. In order to produce an exact dosage form, the displacement value was determined. The suppository hardness was determined by utilizing the SBT (Erweka) apparatus and it was found that the Witepsol H-15 allows the formation of brittler suppositories. The release rates were determined with the USP dissolution apparatus and with cellophane membrane and it was found to be: PEG 1540 > Witepsol H-15 > Theobroma oil. The bioavail-ability of Indomethacin after rectal administration was: PEG 1540 > Witepsol H-15 > Theobroma oil which correlates with the invitro release.     

Synthesis and Chemical Transformations of the Diels-Alder Adduct of [60] Fullerene and 4-Amino-O-quinodimethane

Abstract

Diels-Alder reaction of 4-amino-o-quinodimethane (6) with [60]fullerene (1) affords the thermally stable and chemically reactive adduct 7(n). The nucleophilic amino group of 7(n) is well suited for the covalent attachment of different molecules to the [60]fullerene core by further chemical reactions.     

Chemical-mechanical failure of oxide scales on 9% Cr steels in air with H2O

Abstract

The oxidation behaviour of 9% Cr steels P91 and Nf616 has been investigated at 650°C in dry air and in air with water vapour, where particular attention was given to breakaway failure. Additional emphasis was given to the quantitative characterisation of the kinetics of chromium depletion in the metal subsurface zone resulting from scale growth, CrO₂H₄ evaporation, and scale cracking and healing, with scale cracking being monitored by acoustic emission measurements. While in dry air the steels show protective oxidation behaviour up to 10000 h, breakaway oxidation may occur already after 100 h in humid environments, which was correlated with the stronger Cr-depletion and the development of intrinsic oxide scale growth stresses exceeding a critical value, in the case of water vapour containing air. In the paper the different parameters that are responsible for breakaway oxidation were identified and discussed with regard to the role of water vapour in the environment. As a conclusion it turns out that breakaway is not a consequence of Intrinsic Chemical Failure (InCF) but of a Mechanically Induced Chemical Failure (MICF).     

Further Considerations in Correlating in Vitro - In Vivo Data Employing Mean-Time Concept Based on Statistical Moments

Abstract

Dissolution of a dosage form in vivo is often the rate-limiting factor determining the bioavailability and subsequently the therapeutic response. If a good correlation exists between an in vitro dissolution parameter and some bioavailabilty parameter, then monitoring of dissolution profile should permit the prediction of bioavailability.

The concept of Mean Residence Time based on statistical moments provides one method for correlating in vitro - in vivo data. The exemplification of this approach involving urinary excretion data is relatively straight forward. For plasma drug concentration-time data, however, this may not be the case realistically. This paper sets forth the mechanics of correlating in vitro data with bioavailability data employing both urinary excretion data as well as plasma data.     

Effective two-phase p-median approach for the balanced cell formation in the design of cellular manufacturing system

In this paper, we present an effective two-phase p-median approach for the balanced cell formation (CF) in the design of cellular manufacturing system. In phase 1, the p-median mathematical model of machine CF, which adopts a linear integer programming formulation, is developed. Our formulation uses a new similarity coefficient based on the generalised nonbinary part-machine incidence matrix (PMIM) which incorporates realistic manufacturing aspects such as setup time, processing time, operation sequences and lot size of parts and duplicate machine types. In phase 2, a systematic part assignment procedure based on the new classification scheme of part types is established in pursuit of balancing the workload among machine cells. New efficiency measures for evaluating the quality of the binary and nonbinary PMIM-based block diagonal solutions are proposed to judge the degree of cell load imbalance. Computational experiments with moderately intermediate-sized data-sets selected from the literature show effectiveness of our two-phase p-median approach for the balanced CF.     

Medicament Release from Ointment Bases: III. Ibuprofen: In Vitro Release and In-Vivo Absorption in Rabbits

Abstract

The in-vitro release of ibuprofen from various topical bases including: water-washable base, hydrophilic base, cream, Canadian formulary base, gel, emulsion, water-soluble base and University of California Hospital base were studied. Also, the effects of the additives (ethanol, polyethylene glycol—400, urea and dimethylsulfoxide) on the release rate of the drug from the water-washable base were evaluated.

In general, the in-vitro release rate rank order of the drug was observed to be: water-washable base > hydrophilic base > Canadian formulary base > gel > PEG water washable > emulsion > cream > University of California base. The additive ingredients had a Little or no effect in enhancing the release of drug from the samples studied.

The formulations with optimum in-vitro drug release were scaled up for in-vivo percutaneous absorption in rabbits. The blood samples were analyzed by a HPLC method. Among the candidates evaluated in-vivo, the bioavailability of the drug was significantly higher from the water-washable base when compared to the hydrophilic base and others. The addition of 10% dimethylsulfoxide to the hydrophilic enhanced the release of ibuprofen and adversely affected the release from the water-washable base.

In-vitro and in-vivo data were treated by various kinetic models and the values for diffusion coefficient, permeability coefficient and partition coefficient were calculated. Also, some pharmacokinetic parameters, such as, absorption rate constant, elimination rate constant and half-life of the drug were calculated for meaningful interpretations of the data for the release of drug from topical bases.     

A two-phase procedure for duplicating bottleneck machines in a linear layout,cellular manufacturing system

We discuss a two-phase procedure for duplicating bottleneck machines in a cellular manufacturing system. Given a preliminary solution by a clustering technique, the first phase solves a cellular layout problem in which it assigns machine-cells to locations to minimize the total inter-cell material handling costs that result from the bottleneck parts. The purpose of this phase is to find an optimal linear layout of cells. The second phase finds the bottleneck machines that need to be duplicated to minimize the costs. A binary (integer) linear programming model is developed in this phase to minimize the total duplication costs and material handling costs (if not duplicated). Finally, a decision is made as to whether a solution with bottleneck machines, or duplication of bottleneck machines to avoid the bottleneck problem is to be accepted. An example is demonstrated to show how such a bottleneck problem in cellular manufacturing is solved.     

A close neighbour algorithm for designing cellular manufacturing systems

The first step in creating a cellular manufacturing system is to identify machine groups and form part families. Clustering and data organization (CDR) algorithms (such as the bond energy algorithm) and array sorting (ARS) methods (such as the rank order clustering algorithm) have been proposed to solve the machine and part grouping problem. However, these methods do not always produce a solution matrix that has a block diagonal structure, making visual identification of machine groups and part families extremely difficult. This paper presents a ‘close neighbour algorithm’ to solve this problem. The algorithm overcomes many deficiencies of the CDR and ASM methods. The algorithm is tested against ten existing algorithms in solving test problems from the literature. Test results show that the algorithm is very reliable and efficient.     

Performance of Fuzzy ART neural network and hierarchical clustering for part-machine grouping based on operation sequences

The problem context for this study is one of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems and for streamlining material flows in general. Given this problem context, this study develops an experimental procedure to compare the performance of a fuzzy ART neural network, a relatively recent neural network method, with the performance of traditional hierarchical clustering methods. For large, industry-type data sets, the fuzzy ART network, with the modifications proposed here, is capable of performance levels equal or superior to those of the widely used hierarchical clustering methods. However, like other ART networks, Fuzzy ART also results in category proliferation problems, an aspect that continues to require attention for ART networks. However, low execution times and superior solution quality make fuzzy ART a useful addition to the set of tools and techniques now available for group technology and design of cellular manufacturing systems.     

Influence of Hydroxypropyl Methylcellulose and of Manufacturing Technique on in Vitro Performance of Selected Antacids

Abstract

Factors affecting the performance of antacids F-MA 11, dihydroxy aluminum aminoacetate, magaldrate and magnesium hydroxide were studied in vitro using Schaub's acid neutralization test, a modified Reheis reaction velocity test and the USP test. From the results obtained it was evident that type and combination of antacid, the adjuvants and formulation techniques used in preparation of antacids affect their performance. The USP preliminary antacid test and acid neutralization test are not optimal in vitro tests to evaluate in vitro onset and duration of action of antacids.