Janet M. Twomey

Operational methods for minimization of energy consumption of manufacturing equipment

This paper develops operational methods for the minimization of the energy consumption of manufacturing equipment. It is observed that there can be a significant amount of energy savings when non-bottleneck (i.e. underutilized) machines/equipment are turned off when they will be idle for a certain amount of time. Using this fact, several dispatching rules are proposed. A detailed performance analysis indicates that the proposed dispatching rules are effective in decreasing the energy consumption of especially underutilized manufacturing equipment. In addition, a multi-objective mathematical programming model is proposed to minimize the energy consumption and total completion time. Using this approach, a production manager will have a set of non-dominated solutions (i.e. the set of efficient solutions) which he/she can use to determine the most efficient production sequence which will minimize the total energy consumption while optimizing the total completion time.

Bias and variance of validation methods for function approximation neural networks under conditions of sparse data

Neural networks must be constructed and validated with strong empirical dependence, which is difficult under conditions of sparse data. The paper examines the most common methods of neural network validation along with several general validation methods from the statistical resampling literature, as applied to function approximation networks with small sample sizes. It is shown that an increase in computation, necessary for the statistical resampling methods, produces networks that perform better than those constructed in the traditional manner. The statistical resampling methods also result in lower variance of validation, however some of the methods are biased in estimating network error.

Recycling of fiber-reinforced composites and direct structural composite recycling concept

Fiber-reinforced polymer composites are engineered materials commonly used for many structural applications because of the high strength-to-weight and stiffness-to-weight ratios. Although the service life of these materials in various applications is usually between 15 and 20 years, these often keep the physical properties beyond this time. Recycling composites using chemical, mechanical, and thermal processing is reviewed in this article. In this review of carbon, aramide, and glass fiber composites, we provide, as of 2011, a complete view of each composite recycling technology, highlight the possible energy requirements, explain the product outputs of recycling, and discuss the quality (fiber strength) of recyclates and how each recyclate fiber could be used in the market for sustainable composite manufacturing. This article also includes the new concept of ‘direct structural composite recycling’ and the use of these products in the same or different applications as low-cost composite materials after small modifications.

Life cycle and nano-products: end-of-life assessment

Understanding environmental impacts of nanomaterials necessitates analyzing the life cycle profile. The initial emphasis of nanomaterial life cycle studies has been on the environmental and health effects of nanoproducts during the production and usage stages. Analyzing the end-of-life (eol) stage of nanomaterials is also critical because significant impacts or benefits for the environment may arise at that particular stage. In this article, the Woodrow Wilson Center’s Project on Emerging Nanotechnologies (PEN) Consumer Products Inventory (CPI) model was used, which contains a relatively large and complete nanoproduct list (1,014) as of 2010. The consumer products have wide range of applications, such as clothing, sports goods, personal care products, medicine, as well as contributing to faster cars and planes, more powerful computers and satellites, better micro and nanochips, and long-lasting batteries. In order to understand the eol cycle concept, we allocated 1,014 nanoproducts into the nine end-of-life categories (e.g., recyclability, ingestion, absorption by skin/public sewer, public sewer, burning/landfill, landfill, air release, air release/public sewer, and other) based on probable final destinations of the nanoproducts. This article highlights the results of this preliminary assessment of end-of-life stage of nanoproducts. The largest potential eol fate was found to be recyclability, however little literature appears to have evolved around nanoproduct recycling. At lower frequency is dermal and ingestion human uptake and then landfill. Release to water and air are much lower potential eol fates for current nanoproducts. In addition, an analysis of nano-product categories with the largest number of products listed indicated that clothes, followed by dermal-related products and then sports equipment were the most represented in the PEN CPI (http://www.nanotechproject.org/inventories/consumer/browse/categories/2010).

Predicting peak pinch strength: Artificial neural networks vs. regression

Abstract Cumulative trauma disorders (CTDs) of the upper extremities are one of the major ergonomics areas of research. Pinching is a common risk factor associated with the development of hand/wrist CTDs. The capacity standards of peak pinch strength for various postures are needed to design the tasks in harmony with the workers. This paper describes the formulation, building and comparison of pinch strength prediction models that were obtained using two approaches: Statistical and artificial neural networks (ANN). Statistical and ANN models were developed to predict the peak chuck pinch strength as a function of different combinations of five elbow and seven shoulder flexion angles, and several anthropometric and physiological variables. The two modeling approaches were compared. The results indicate ANN models to provide more accurate predictions over the standard statistical models.

MULTIPLE REGRESSION AND COMMITTEE NEURAL NETWORK FORCE PREDICTION MODELS IN MILLING FRP

This work utilizes the mechanistic modeling approach for predicting cutting forces and simulating the milling process of fiber-reinforced polymers (FRP) with a straight cutting edge. Specific energy functions were developed by multiple regression analysis (MR) and committee neural network approximation (CN) of milling force data and a cutting model was developed based on these energies and the cutting geometry. It is shown that both MR and CN models are capable of predicting the cutting forces in milling of unidirectional and multidirectional composites. Model predictions were compared with experimental data and were found to be in good agreement over the entire range of fiber orientations from 0 to 180°. Furthermore, CN model predictions were found to greatly outperform MR model predictions.

Unit Process Life Cycle Inventory for Product Manufacturing Operations

Rapid access to or generation of life cycle information is a potentially valuable tool for the design of products to meet the needs of sustainability improvement. A new approach is developed to use the manufacturing unit process, commonly outlined in manufacturing process taxonomy systems, as the basis for life cycle inventory. This will initially involve 50–70 unit processes from the taxonomy and will generate energy and mass profiles for each unit process life cycle (uplci). These uplci can be adjusted for each case to include the major variables affecting such operations as related to any specific product. The sum of the performance of a sequence of uplci thus provides the life cycle of the specific product from a defined set of plant process inputs.Copyright

Recycling of Aircraft: State of the Art in 2011

Recently, the end-of-service life for aging aircraft and related parts has become a key subject in recycling industries worldwide. Over the next 20 years, approximately 12,000 aircraft currently utilized for different purposes will be at the end of service. Thus, reclaiming retired aircraft by environmentally responsible methods while retaining some of the value becomes a significant need. Recycling aircraft components and using these in different applications will reduce the consumption of natural resources as well as landfill allocations. Compared to the production of virgin materials, recycling aircraft will also reduce air, water, and soil contaminations, as well as energy demand. In the present study, we have investigated the environmental benefits of recycling and reusing aircraft components in the same or similar applications as low-energy input materials. During the aircraft recycling, most of the aircraft components can be recycled and reused after reasonable modifications and investments.

A predictive model for slip resistance using artificial neural networks

This paper describes the formulation, building and validation of an artificial neural network model of the dynamic coefficient of friction (DCOF) as measured by a slip resistance testing device. The model predicts the DCOF as a function of six independent variables over a wide range of conditions. A grouped cross validation method is used to show the consistent performance of the model in predicting the DCOF for new values of the independent variables.

A sequential Bayesian Cumulative Conformance Count approach to deterioration detection in high yield processes

Cumulative conformance count (CCC) control chart is a powerful alternative to the traditional p-control chart, particularly in monitoring high yield processes with extremely low proportions of nonconformance. However, a prevalent limitation of the CCC control chart is its inability to detect small process deterioration. A sequential Bayesian CCC approach capable of detecting small process deterioration is proposed in this paper. The new approach outperforms the traditional CCC chart in that it does not require a large sample of initial observations of the process, which may be difficult, if not impossible to obtain in practice. Moreover, the approach is self-starting, and thus may be used in short production runs. A Bayesian updating procedure is developed, which allows for the determination of initial control limits based on only three initial observations or some prior knowledge about the proportion of nonconformance of the process. Values of proportions of nonconformance, ranging from 0.1 to 0.00001, are tested to demonstrate the deterioration detection capability of the new approach in conjunction with the proposed deterioration detection rules. Copyright