Representing group technology classification and coding techniques with object oriented modeling principles

The purpose of this paper is to show the common basis of several object-oriented design modeling principles as compared with those used in classification and coding (C&C) for group technology applications. Five types of decision tree (E-trees, N-trees, X-trees, D-trees, and C-trees) are shown to be in exact correspondence with the object-oriented modeling principles generalization with disjoint subclasses, generalization with overlapping subclasses, classification, generalization with restriction, and aggregation, respectively. In addition, C&C modeling and subsequent database implementation are shown to be enhanced through application of such object-oriented modeling principles as association and role names. Finally, a case study is presented that illustrates the application of these principles in the design and implementation of a C&C system for abrasive flow machining.     

Optimal decision of an economic production quantity model for imperfect manufacturing under hybrid maintenance policy with shortages and partial backlogging

Considering the characteristics of the stochastic shift of the machine state and the uncertainty of the product quality of production, in this paper, we develop an optimisation decision of economic production quantity model for an imperfect manufacturing system under hybrid maintenance policy with shortages and partial backlogging. We assume that the production process is imperfect stemming from the machine reliability and the probability of out-of-control, a hybrid maintenance policy combined of emergency maintenance and preventive maintenance is executed during each production run. Three decision models based on the scenarios of machine breakdown and repair time are developed. The optimal production quantity and maintenance inspection number during each production run are solved with minimising the expected average cost of the system. Numerical examples are used to demonstrate the effectiveness and feasibility of the model. Sensitivity analysis is conducted to analyse the impacts of key parameters on the optimal decision. Some implications related to the effective and economical execution of maintenance policy for practitioners are derived.     

Relating product specifications and performance data with a neural network model for design improvement

This paper presents research resulting in a neural network model relating product design specifications and performance testing results using data from a sanitary ware manufacturer. The main constraint of the work was the limited availability of actual data for neural network training and testing, a situation often found in real situations where a priori product knowledge is limited during the product design phase. The authors used two training techniques, the standard hold-back and the leave-k-out, for the neural network model to leverage the sparseness of the data. Neural network results are compared and contrasted to statistical models relating product design and performance. This work is an exploration of the value of neural network models to assist with interactive product design.     

On the development of a robotic workcell for sanitary ware spray glazing

This paper describes planning activities that need to be undertaken before the implementation of robotic workcells for sanitary ware spray glazing operations. An existing economic justification model that can be used to evaluate the economic feasibility of these workcells is also modified.     

Predicting glass furnace output using statistical and neural computing methods

This paper describes the development of predictive models for glass production at a regional manufacturing company. The objectives of the models are to predict the actual batch tonnage produced per week from the glass furnace based on the planned production schedule. Four modelling methods were explored: (i) linear regression; (ii) nonlinear regression; (iii) artificial neural network using back-propagation; and (iv) radial basis function neural network. Using 175 cases of production schedule data and subsequent furnace output, the two neural network-based prediction models resulted in lower average absolute error and lower maximum absolute error than the linear or nonlinear regression models. Accurate neural network-based prediction models of furnace output will subsequently be used in the overall production planning system by utilizing estimates of furnace output to determine the necessary energy, raw material, repair and personnel requirements of the glass manufacturing facility.     

On the Use of Students for Developing Engineering Laboratories

This paper describes a unique and innovative approach that solved the dual problem of starting up a new engineering instructional laboratory in a timely manner, and for teaching engineering students advanced skills in Automatic Data Collection. Students enrolled in a special pilot course were used to develop and startup an Automatic Data Collection laboratory. These students were assigned individual Automatic Data Collection technologies of interest and given total responsibility for the successful startup of the laboratory. The organization and structure of the course modeled the typical team oriented project development efforts in industry. Feedback from students showed the course to be better than a typical lecture/laboratory/demonstration type course in the following ways: 1) students believed they had greater amount of contact with equipment; 2) their experience on the project was more realistic than more traditional courses; 3) they believed they gained a more thorough understanding of the technology under study; and 4) they believed they improved their professional skills making them more marketable to potential employers. With respect to the laboratory itself, startup time was reduced from an estimated 18 months to 14 weeks with the help of the student teams.     

Reducing waste in casting with a predictive neural model

This paper describes an interactive neural network model that predicts the quality of cast ceramic products using multiple quantitative and qualitative inputs. This has been done to enable a major sanitary ware manufacturer to reduce product waste by better control of the slip casting process. The input variables describe the raw materials, ambient conditions and line settings for the ceramic casting process. The neural network predictive model assigns one of seven quality categories to the cast based on the input data. This prediction is used by the quality control engineer to make a priori adjustments to materials and line settings so that a good quality cast is produced without trial and error. The neural model can also be used to determine optimum settings of each adjustable input variable in the light of values of non-adjustable input variables.     

A genetic cluster algorithm for the machine-component grouping problem

This research presents the usage of a genetic algorithm for the clustering of parts and machines. A detailed analysis is shown comparing GCA results with single link cluster analysis, rank order clustering, and the direct clustering algorithm. GCA was also compared with several additional cell formation heuristics described in the recent literature, including GRAPHICS, MODROC, and a cost-based heuristic. Results showed that the GCA was far superior over single link cluster analysis and provided equivalent results to those of the direct clustering algorithm and rank order clustering. GCA was also found to provide superior results to the other heuristics. The discussion explains these findings by illustrating the inflexibility of traditional cell formation heuristics in the selection of final machine-component groupings.     

Development of an intelligent castability and cost estimation system

Performing the castability analysis of a part and its cost estimation requires substantial knowledge and experience. It is also time consuming and dependent on the expert personnel available. This paper describes the development of an intelligent system for castability analysis and product cost estimation for a permanent-mould casting company. The system allows personnel with little technical knowledge to analyse the part, estimate the cost, and prepare quotations with little or no assistance from an expert whose time is better utilized on high potential quotations and more direct product development activities.     

Laser and Photonic Systems Integration: Emerging Innovations and Framework for Research and Education

The purpose of this article is to review the key emerging innovations in laser and photonics systems as well as their design and integration, focusing on challenges and opportunities for solutions of societal challenges. Developments, their significance, and frontier challenges are explained in advanced manufacturing, biomedicine and healthcare, and communication. Systems, networks, and integration issues and challenges are then discussed, and an integration framework for networking laser‐ and photonic‐based services and products is proposed. The article concludes with implications and an agenda for education, research and development, and policy needs, with a focus on human, society, science, and technology integration. © 2013 Wiley Periodicals, Inc.