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There are numerous techniques and technologies used in the brewing of coffee today. This study is aimed to design an umbrella experiment to examine the impacting key factors consistent across all methods of extraction. These factors were identified as: grind size, roast type, steep time, grind to water as a mass-to-mass ratio and temperature. Samples were prepared by grinding the beans, weighing the beans and adding the appropriate amount to a rectangular glass vessel. Distilled water was used. French press was selected as the brewing technique. No agitation after the addition of grind was allowed. Water was heated to initial temperature and added to the vessel and a timer was started. Upon time completion aliquots were taken and analysed using a prism-based automatic temperature compensation (ATC) refractometer. Minitab 19.2020.1 (64-bit) was used for analysis of variance and optimisation. In the initial design of experiments, the five factors were considered at different levels. Three factors found significant in impacting %TDS (total dissolved solids) were: total steep time, grind-to-water ratio and roast type. To further refine the experiments, 23 full-factorial design was conducted for these three factors. Finally, optimisation was done to determine the factor levels to produce a cup of coffee for the target %TDS of 1.26. This research found that it is best to use medium roast coffee that is ground fine with a 0.08 grind-to-water ratio, with an initial temperature of 195 Fahrenheit in a French press, and the ideal steep time is 7 min. Previous studies have focused on the extraction methods. In this study, the method of extraction has been removed as a variable. Hence, the findings are very useful for food industry.

Purpose The purpose of this paper is to develop a framework that illustrates the role of Lean Six Sigma (LSS) in creating organisational innovation climate by investigating the relationship between LSS’s intangible impacts and organisational innovation climate factors. Design/methodology/approach A self-administrated survey questionnaire was distributed among 145 public sector officials to get their opinions on the relationship between various observable elements of LSS’s intangible impacts and organisational innovation climate factors, where a response rate of 73.8% was achieved. The collected data were demographically, descriptively and statistically analysed. Accordingly, a house-of-pillars-based framework that illustrates the role of LSS’s intangible impacts in creating innovation climate in an organisation was developed. Findings Results from this study indicated that LSS’s intangible impacts on organisational structure and hierarchy, culture, change adaptability, utilisation of staff and staff’s behavioural aspects are positively related to many of organisational innovation climate factors such as trust and openness, challenge and involvement, support for ideas and freedom and autonomy. Research limitations/implications The findings of this study are based on the data collected from public sector organisations in the UAE and are supported by relevant literature. However, this study can provide useful guidance for further research for the generalisation of the results to wider scopes in terms of sectors and geographical domains. Practical implications The findings of this study will provide UAE public sector officials with a clear roadmap on how to use LSS for promoting innovation and fostering its implementation in practice. This study will also encourage professionals in public sectors to integrate LSS into their innovation strategies to enhance organisational innovativeness and improve service quality. Originality/value It is one of the first studies that explores LSS’s intangible impacts and assesses their relationship with organisational innovation climate factors. Hence, this study offers valuable insights for both academics and practitioners and is expected to lay a foundation for a better understanding of how LSS’s intangible impacts can be used in creating organisational innovation climate.

PurposeReducing wear of undercarriage track propulsion systems used in heavy construction equipment decreases the maintenance costs and increases the equipment's life. Therefore, understanding key factors that affect the wear rate is critical. This study is an attempt to predict undercarriage wear.Design/methodology/approachThis research analyzes a sample of track-type dozers in the eastern half of North Carolina (NC), USA. Sand percentage in the soil, precipitation level, temperature, machine model, machine weight, elevation above sea level and work type code are considered as factors influencing the wear rate. Data are comprised of 353 machines. Machine model and work code data are categorical. Sand percentage, elevation, machine weight, average temperature and average precipitation are continuous. ANOVA is used to test the hypothesis.FindingsThe study found that only sand percentage has a significant impact on the wear rate. Consequently, a regression model is developed.Research limitations/implicationsThe regression model can be used to predict undercarriage wear and bushing life in soils with different sand percentages. This is demonstrated using a hypothetical scenario for a construction company.Originality/valueThis work is useful in managing maintenance intervals of undercarriage tracks and in bidding construction jobs while predicting machine operating expense for each specific job site soil makeup.

The hot-dip batch galvanization (HDBG) process is one of the most popular and widely used techniques of galvanization and a prime consumer of 50% of zinc produced annually. An increase in production expenses due to excess zinc and energy consumption is a major problem in the HDBG process of many galvanizing industries. To identify the root cause of zinc and energy consumption and improve the process, a case study is presented in the HDBG process of an Indian tower parts manufacturing industry. Six Sigma’s DMAIC approach is implemented in the HDBG process of this industry and investigated that the over-coating of zinc layer is the major cause of excess zinc and energy exploitation. The 23 factorial model of the design-of-experiments (DOE) is adopted in the improve phase of DMAIC, which established the relation between coating thickness and its influencing factors. The ANOVA confirmed that immersion time and silicon present in tower parts material significantly impact the coating thickness. The immersion time of 3 min has been determined to result in optimum coating thickness. By setting immersion time up to a standard level, an improvement in the HDBG process has been achieved. Due to this, the process sigma level is improved from 0.75 to 3, and now 99.73% of products are within the acceptable range of coating thickness. These outcomes have proven that the Six Sigma implementation together with DOE enhances the product quality and improves the process, which helps industry to achieve sustainable competitive benefits.

International audience

This work proposes a Value Stream Mapping (VSM) to be used in manufacturing industry that combines Lean, Energy and Six-Sigma methodologies. Lean's VSM tool is amended to energy VSM so that the value-added and non-value-added approach can be used to determine the energy use and waste. In order to identify the maximum rejection or rework at a workstation, an entropy-based mathematical model is developed and a Lean-Energy-Six Sigma Value Stream Mapping (LESSVSM) is presented. The model can be used in manufacturing at product level where temperature and energy can be measured from input to output. The model has been implemented on a manufacturing system containing four workstations. The proposed LESSVSM model will help to industry to become more sustainable as it can minimize energy and waste in the case of rework and rejection. This study presents a systematic approach which researchers and practitioners can apply for sustainable manufacturing.

The respective American Society for Quality (ASQ) Bodies of Knowledge (BoKs) for Certified Quality Engineers (ASQ, 2015a) and Certified Six Sigma Black Belts (ASQ, 2015b) are quite similar; yet anecdotally six sigma black belts are recognized and rewarded more highly than are quality engineers. While qualityengineering work is considered preventive in nature, work performed by six sigma black belts is in the realm of improvement, hence reactive. Thus, a dichotomy exists in that preventive actions, which are less costly, are not rewarded as well as costlier reactive actions. The intent of this research was to confirm or debunk the anecdotal evidence and determine the root causes therefrom. The results confirm the anecdotal evidence and indicate the need for further research. In addition, the results confirm the use of the Kano Model as applicable to the cause for rewarding this dichotomy.

The objective of this chapter is to apply risk management framework for risk identification and analysis of steam generating unit (SGU) in sugar plant. Only internal operational risks were considered for analysis. The expert discussion and fish bone diagram were used for identification and presentation of operational risk, and in the second phase risk analysis was done for the failure causes identified. The conventional failure and mode and effect analysis (FMEA) was used for risk analysis. The uncertainties arising from biased and faulty judgements were removed by integrating fuzzy methodology in the analysis process. Fuzzy multi-attribute decision-making method (MADM)-based risk analysis was carried out to eliminate discrepancies. The fuzzy digraph and matrix approach (FDMA) was used to evaluate the risk factors and its interdependencies. The results of the analysis were compared for getting insights into the process of analysis by conventional and fuzzy-based approach. A mitigation plan was also prepared for the assistance of concerned persons. The output was shared with the concerned authorities for managerial action and implementation. The results showed a different point of view and understanding on risk priorities.

Purpose Reciprocating compressors offer an efficient method of compressing almost any gas composition in a wide range of pressures and have numerous applications. Condition monitoring of critical rotating machinery is widely accepted by operators of centrifugal compressors. However, condition monitoring of reciprocating machinery has not received the same degree of acceptance. An earlier study (Townsend et al., 2016) was conducted on temperature monitoring. The purpose of this paper is to examine the impact of continuous pressure monitoring on electric-driven compressors. Design/methodology/approach This research analyzes the impact of continuous pressure monitoring on a fleet of 14 compressors transporting CO2 for enhanced oil recovery. The reliability and efficiency data on 14 reciprocating compressors over a three-year period were analyzed for failures detectable by the condition monitoring technology. The engineering economic analysis is presented to determine the impact this technology will have on the productivity of the compressors. Findings The study considers utilizing condition monitoring technology to analyze the pressure of the swept volume of the compressor cylinders. The results of the study indicate that continuous pressure monitoring technology has a strong impact on the productivity of the compressor fleet. The internal rate of return not only exceeds the operators hurdle rate, but the payback period is also dramatic. Pressure monitoring was found to be economically better than temperature monitoring. Originality/value The study reveals the economic benefits of implementing condition monitoring in the form of continuous pressure monitoring on reciprocating compressors.

Purpose The purpose of this paper is to investigate the possibility of including the cost consequence of failure in the a priori risk assessment methodology known as failure mode and effect analysis (FMEA). Design/methodology/approach A model of the standard costs that are incurred when an electronic control module in an automotive application fails in service was developed. These costs were related to the Design FMEA ranking of the level of severity of the failure mode and the probability of its occurrence. Monte Carlo simulations were conducted to establish the average costs expected for each level of severity at each level of occurrence. The results were aggregated using fuzzy utility sets into a nine-point ordinal scale of cost consequence. The criterion validity of this scale was assessed with warranty cost data derived from a case study. Findings It was found that the model slightly underestimated the warranty costs that accrued, but the fit could be improved with adjustments dictated by actual usage conditions. Research limitations/implications Cost data used in the simulations were derived from government and academic surveys, analyses, and estimates of the manufacturing cost structure; and nominal costs for various quality issues experienced by Tier 2 automotive electronics supplier. Specificity is lacking. The sample size and the type of the failure modes used to validate the model are constrained by the number and type of products which have had demonstrable performance concerns over the past three years, with cost data available to the authors. The power of the validation is limited. The validation is considered a screening assessment. Practical implications This work relates the characterization of risk with its potential cost and develops a scaling instrument to allow the incorporation of cost consequence into an FMEA. Originality/value A ranking scale was developed that related severity and occurrence rank scores to a cost consequence rank that keys to a cost of quality figure (given as percent of sales) that would accompany a realization of the failure mode.

Purpose The purpose of this paper is to develop an optimal model of an integrated quality and safety management system (QSMS). Design/methodology/approach Keywords related with these systems were identified from international standards and subsequently mined from a selection of peer reviewed articles that discuss and propose varying forms of integrated models for both systems. Cluster analysis was used to establish the degree to which integrated models, as described in the articles were quality dominant vs safety dominant. Word counts were utilized for establishing content and attributes for each category. An optimal integrated model was developed from the final cluster analysis and substantiated by a one-way analysis of variance. Experts from industry were consulted to validate and fine-tune the model. Findings It was determined that characteristics of an optimal integrated model include the keywords “risk,” “safety,” “incident,” “injury,” “hazards,” as well as “preventive action,” “corrective action,” “rework,” “repair,” and “scrap.” It also combines elements of quality function deployment as well as hazard and operability analysis meshed into a plan-do-check-act type work-flow. Research limitations/implications Given the vast array of clustering algorithms available, the clusters that resulted were dependent upon the algorithm deployed and may differ from clusters resulting for divergent algorithms. Originality/value The optimized model is a hybrid that consists of a quality management system as the superordinate strategic element with safety management system deployed as the supporting tactical element. The model was implemented as a case study, and resulted in 13 percent labor-hour saving.

Numerous factors affect same day delivery (SDD) in E-Commerce. A Delphi survey was designed and sent to industry experts to rate affecting factors based on their respective importance pertaining to SDD in the UAE. The survey findings revealed that the most important factors that affect SDD substantially are: logistics strategy, operations management, IT infrastructure and facility location.

Purpose – The purpose of this paper is to investigate the impact of costs of quality on the present worth (PW) of a new product launch at a North American automobile manufacturer. Design/methodology/approach – The paper is based on the examination of various cash flows associated with a new product launch within an automobile manufacturer. Standard cash flows and a PW analysis were examined and compared to non-standard cash flows which take into consideration post-launch cost of quality (CoQ). A sensitivity analysis was used to determine if any CoQ factors affected the integrity of the product launch. Findings – The paper concludes that there is an impact on the PW of a program when CoQ is considered as a cash flow element. CoQ should be considered in a product launch PW analysis preceding any commitment to invest. Research limitations/implications – This study suggests that, given data on the costs that will accrue assuming standard quality concerns following product launch, and the occurrence of special cause issues, the business case can establish a better estimate of the costs a program will face under varying levels of post-launch quality. An understanding of the potential cost consequences of quality issues can shape the understanding of the risks in a planned project. Practical implications – The paper shows that CoQ can have a significant impact on a PW analysis. CoQ concerns should be considered during pre-launch planning of a new product. Originality/value – The paper satisfies the need to study when a manufacturer considers investment in the launch of a new product with CoQ concerns.

In recent years, formerly depleted domestic oil fields have become producers once again through tertiary oil recovery. In tertiary oil recovery, water and Carbon Dioxide (CO 2 ) are alternatively injected into reservoirs through injection wells. This raises the field pressure and forces oil to producing wells where it is then pumped to a storage tank referred to as a battery. This paper is focused on an operating division in the Permian Basin (USA). The CO 2 is acquired from underground domes in Colorado and then transferred through pipelines to oil fields in West Texas and New Mexico. The compressors are used to move CO 2 and boost the gas to the required field pressure, usually around 2,200 psig. Reciprocating compressors are flexible and able to handle wide capacity and condition swings, offer an efficient method of compressing almost any gas composition in a wide range of pressures and have numerous applications and wide power ratings. This makes them a vital component in various industrial facilities. Condition monitoring of critical rotating machinery is widely accepted by operators of centrifugal compressors. However, condition monitoring of reciprocating machinery such as compressors and internal combustion engines has not received the same degree of acceptance. This paper examines the reliability impact as a result of upgrading the temperature monitoring devices on the connecting rods of electric driven reciprocating compressors. A cost analysis is also presented to demonstrate that the upgrade in hardware and software will eventually yield a saving in the operating cost.

Purpose – The purpose of this paper is to investigate the impact of ISO 9001 certification on US firms’ financial operating performance for a period of five years including one-year prior to certification, year of certification, and three fiscal years after certification. Design/methodology/approach – The paper is based on an examination of a sample of 397 firms that had received ISO 9001 certification from 1991 to 2002. Certified-firm, non-certified-firm, and matched-control-firm operating performances were examined over the same period based on the ratio of pre-tax operating income to total assets (EBITA/TA). Wilcoxon signed-rank test was used to test for significant differences in operating performance among the certified, non-certified, and matched-control firms. Findings – The paper concludes that there was significant improvement performance from prior year to year of certification. The certified firms performed better than the non-certified firms. The certified firms also performed better than the matched-control firms. Research limitations/implications – Based on the research approach and the findings, the paper recommends the use of newer ISO data; any additional certifications; and further research into the lack of significant operating values by the certified firms in the post-certification years. Practical implications – The paper shows that ISO 9001 certification is statistically related to operating performance. Certified firms have better operating performance. The positive significance in decile operating performance from prior year to certification year is an important finding for the implementation of ISO 9001. Originality/value – The paper satisfies the need to study when ISO 9001 certification impacts firms’ financial operating performance.

This main objective of this study is to determine if common platform dishwashing products have similar warranty failure rates when brands and price points are variable. Data for this study are based upon over four years of data involving one million individual sales and over 150,000 warranty failures of a common platform dishwashing appliance. It is concluded that there are similar failure rates between common platform dishwashers when different brands are at the same price point. Other conclusions indicate that there are non-similar failure rates between common platform dishwashers when the same brand is at different price points. There is a significant resource cost in the development of products. To minimise the resource cost and maximise the return on investment, many companies develop several new products from one common platform. Common platforms spread across several separate brands. Different brands attract different customers who have different perceptions of quality. Customers view quality as a mix of utility and reliability and expect high reliability during the warranty period. It is often assumed by the developer that the warranty failure rate will be similar for common product platforms even when products are marketed under different price points. The goal of this research is to test if this common assumption is accurate.

Kulakova and Kulakov (2012) have presented the generalised net present value (GNPV) method to evaluate non-conventional projects. They have illustrated the method with three examples: a project of 2-year life with multiple internal rate of returns (IRRs), a project of 2-year life without IRR and a project of 3-year life with a unique real-valued IRR, which is not the rate of return. The purpose of the present study was to test the validity and feasibility of the GNPV method in evaluating other non-conventional projects with a longer life. The method was used to evaluate five scenarios: a project of 5-year life with multiple IRRs; a project of 7-year life with multiple IRRs, which were not the rates of return; a project of 10-year life with a positive and a negative IRR meaning the positive value was the unique IRR; a project of 7-year life with a salvage value and a unique IRR; and a staged expansion project of 10-year life with a unique IRR. It was concluded that the GNPV method worked in all the five cases considered. Advantages of the GNPV method were noticed in agreement with Kulakova and Kulakov (2012) that (i) the minimum attractive rate of return (MARR) was not required to find a solution; (ii) the internal (financing) rate may be different from the external (reinvestment) rate; and (iii) internal and external rates are not necessarily required for solution, but are required for evaluation of the projects. One disadvantage was observed that the computational work in some cases might be overwhelming.

Lean Six Sigma is a hybrid continuous improvement methodology that has various definitions, from those that are Lean dominant to those that are Six Sigma dominant. Text mining and cluster analysis based research has helped to illuminate the degree to which Lean Six Sigma models, as described in articles published in the International Journal of Lean Six Sigma, are Lean dominant versus Six Sigma dominant. The iterative cluster analysis was used to identify clusters of articles that were interpretable. The research found that some Lean dominant Lean Six Sigma articles ascertain Lean as the dominant philosophy and Six Sigma as a subordinate tool used in achieving the Lean objectives. The findings of this research as well extrapolation of the literature informed a recommended Lean Six Sigma model as described in this article. The recommended model is Lean dominant and consists of two subordinate methods – Six Sigma and statistical process control. The three synergistic approaches not only each serve in their own way to manifest process improvements, they also all contribute to organizational learning, which is considered a chief contributor to competitive advantage.

This paper presents some of the most important findings of studies of the impact of ISO 9000 and ISO 14000 certifications on organizations, based on a literature review. The article discusses potential synergistic advantages that can be derived from an integrated quality-environment system and qualitative benefits using ISO 9000 and 14000 certifications. This article also discusses some of the limitations of the current literature and how these can be addressed in the future research.

Profitability of a company can be affected by the costs associated with backlogs and large inventories due to the bullwhip effect in the supply chain. This work aims to find an ordering strategy that is practical and can minimize the bullwhip effect. Five strategies with different levels of information about inventory and components along the supply line have been compared with the just in time (JIT) pull strategy and the usage of point of sale (POS) data. This work uses the beer game spreadsheet simulation developed by Adams, Flatto, and Gardner (2008). The simulation shows material and information flow in a four-echelon supply chain. Expressions for cost incurred and profit obtained by each player (manufacturer, distributor, wholesaler, and retailer) have been developed. Graphs for cost and profit with time are plotted. The strategy using POS data is found to be the best, and the pull strategy to be the next best. However, both require discipline. This study shows that putting information about the inventory levels and components of the supply line into an ordering strategy can also minimize the bullwhip effect.

This work is a case study dealing with the production of examination table covering used in gynecological offices which has been a problem for its manufacturer, Little Rapids Corporation. During production, the rolls of paper break or tear causing production to cease while the machine is reloaded, costing about $10,000 of lost production in a year. This study was done to investigate the independent variables that contribute to those breaks and to determine if breaks have a significant impact on production rate (speed). Tensile strength and stretch of the paper were chosen as the independent variables in a binary regression analysis. In addition, breaks were compared to machine speed settings to see if there was a correlation. The results showed that the independent variables did have an impact on the probability of breaks occurring and a corresponding relationship was derived using regression analysis. The study recommended that the tensile strength or stretch of the specified paper be increased by 25% to lower the probability of breaks to an acceptable level and allow operators to maintain the desired speed setting. Any greater increase in tensile strength and stretch would incur capital expense that would be prohibitive.

Preserving important troubleshooting experience gained in industrial practice, often referred to as “tribal knowledge”, and making it readily available to be re-applied in similar situations represent a serious challenge for many companies in heavy industry. The solution is very often tied to building mental models to capture and express key latent cognitive variables. In this paper, we first attempt to identify the appropriate knowledge-based system diagnosis paradigms. A conceptual map-based framework is then proposed as a potential solution for retaining tribal knowledge. The validity and feasibility of the approach are demonstrated through a training program developed under the support of National Science Foundation (NSF) for improving engineering and technology students' troubleshooting skills.

While the use of green energy has gained popular support and efforts have been made to market it, few studies have investigated the economic advantages and the savings that could be gained by implementing green solutions for energy challenges. This study aims to measure the financial viability of installing and using a residential grid-connected photovoltaic (PV) system in the State of Indiana while predicting its performance in eighteen geographical locations within the state over the system’s expected lifetime. A systematic approach of six steps was used to collect and analyze the data. The analysis has been condcutded using engineering economic methods including payback period, net present value (NPV) and internal rate of return (IRR). It has been found that installing a PV system for a single family residence in the State of Indiana will not pay for itself within 25 years assuming the average cost of a system. The government incentive programs are not enough to offset the cost of installing the system against the cost of the electricity that would not be purchased from the utility company.

This paper deals with productivity enhancement of a Caravan Manufacturing company located in United Arab Emirates. A continuous improvement technique: define, measure, analyse, improve, and control (DMAIC) has been implemented in the production department of the case company. A model was developed using Arena software to simulate the caravan manufacturing process and the proposed solution. The model was verified and validated. Control charts were also developed to control and maintain productivity in the case company. It was suggested to change caravan doors from wooden to aluminium. After thorough and careful analysis using simulation, it was found that the production of caravan increased from six per every three days to 11 per every three days. This resulted in increased profit of 500 AED (USD 135) per caravan, 600 more caravans per year and enhanced product life cycle. Moreover it resulted in saving cost, increased production, and product life cycle.

PurposeThe purpose of this study is to examine the effect of individual components' reliability on a system's reliability. The system refers to the Financial Management Information System (FMIS), the US Federal Highway Administration's (FHWA) web‐based project approval and tracking software. Its components are 61 project information fields.Design/methodology/approachThe analysis would view each highway project‐funding request as an activity with cycle‐dependent performance for which success probability can be calculated as Reliability, R. The reliability analysis of the 61 FMIS fields results in a series system with Rsys the “estimated reliability” of finding “true” values in all 61 information fields during one highway‐related project funding authorization review.FindingsOf an estimated 200 projects approved, there was previously estimated a 50 percent to 80 percent unreliability rate, while the study found an unreliability rate of approximately 80 percent.Research limitations/implicationsOwing to the nature of federal government software, data can be very difficult to acquire in this working environment, but a simple calculation was relatively successful in confirming the “estimated reliability” of finding “true” values and showing how the reliability could dramatically decrease.Originality/valueThe paper contributes to the applicability of reliability analysis to project approval software, showing the progression from estimated data to bounding the estimate using reliability theory.

The quality, productivity and safety of machining can be significantly improved through the optimization of cutting conditions. The first step in achieving such an objective is the development of accurate and reliable models for predicting the critical process parameters. In this paper, an innovative Artificial Neural Network (ANN) model that predicts both cutting force and surface roughness in end milling is developed and validated. A set of five input variables is selected to represent the machining conditions while twelve quantities representing two key process parameters, namely, cutting force and surface roughness, form the variables of the network output. Full factorial design of experiments is used to generate data for both training and validation. Successful training of the neural network is demonstrated through comparison of simulated and experimental results for four different output variables, namely cutting force, surface roughness, feed marks, and tooth passing frequency. The predictive ability of the model is verified experimentally by comparing simulated output variables with their experimental counterparts. A good agreement is observed.

In earlier work [Bader et al., ASME J. Manuf. Sci. Eng. 125(2), pp. 263–271 (2003); Int. J. Mach. Tools Manuf. 45(1), pp. 63–75 (2005)] the authors have presented an adaptive sampling method utilizing manufacturing error patterns and optimization search techniques for straightness and flatness evaluation. The least squares method was used to compute a tolerance zone. In this paper, experimental analysis is performed to verify the sturdiness of the adaptive sampling procedure. Experiments are carried out to investigate the effects of different factors on the sample size and absolute percent error of the estimated tolerance from that of a large population sample. Twelve 7075-T6 aluminum plates are end-milled and 12 cast iron plates are face-milled. Two sets of four plates from each lot are selected randomly, one each for straightness and flatness estimation. Factor A used in both straightness and flatness analyses is manufacturing process (i.e., surface error profile). Factor B for straightness is step size whereas for flatness it is search strategy (i.e., number of bad moves and restart allowed). Factor C for flatness is search algorithm (i.e., tabu and hybrid). Plates are nested within the levels of manufacturing process. The results have been analyzed and compared with other sampling methods. The analyses reveal that the current approach is more efficient and reliable.

This paper presents an adaptive sampling procedure, which uses manufacturing surface error patterns and optimization search methods for reducing sample size, while maintaining high accuracy. The methodology is demonstrated with straightness and flatness evaluation. Two manufacturing processes, end and face milling are used to fabricate plates used for modeling and subsequent testing. Respective surface errors for each process are quantified and validated using previously published models. The initial points for sampling are identified through such a characterization of the process and its affect on the workpiece. These sampled points are fit using the least-squares method to complete the form evaluation. Points are added to the initial set using optimization search heuristics. The final tolerance value obtained is compared with that obtained from a large population sample to check the accuracy. With such an adaptive approach, it is proposed that the number of points sampled is potentially less than that which would be expected to achieve the same level of accuracy using traditional sampling methods. This paper demonstrates the error modeling and validation aspects of this adaptive sampling procedure.

Aeroplanes are repairable systems consisting of several non‐repairable parts; tyres are one of these. Proper record of failure data is valuable in interpreting the failure pattern, for comparative evaluation of the quality of tyres of various manufacturers, and for prediction of the future needs in a specified planning horizon or for specified operational hours. Analyses failure data of several aeroplane tyres in an aviation set‐up, and interprets these in a reliability framework. Three parameters Weibull model is found to be quite appropriate for reliability characterization of tyres. These reliability models can be effectively integrated into a computerized material requirement planning system of the aviation facility to forecast the number of tyres needed for a given planning horizon.

The Monte Carlo simulation technique for determining uncertainties of the thermal parameters needed in designing heat exchangers is discussed. Parameters such as the outside diameter D o , thickness t and thermal conductivity k are assumed to have Gaussian distributions, while the heat-transfer coefficients h i , and h o are considered to follow normal, log-normal and Weibull forms of distribution. An illustrative example is presented. The overall heat-transfer coefficient U obtained from the simulation is found to have a distribution that is well approximated by the Weibull model. Using the Weibull distribution of U , the additional percentage area is calculated for 80 and 99% confidence levels (CLs). The proposed approach of design analysis incorporates realistic risks associated with uncertainties of the parameters.

An analytical method for the second-law-based thermoeconomic optimization of a sensible heat-storage system, in which the energy is stored in a large liquid bath from a hot-gas source, is presented. In this approach, monetary values are attached to the irreversible losses caused by the finite-temperature difference heat transfer and pressure drop in the storage system. Results are presented in terms of the optimum number of transfer units as a function of a dimensionless unit-cost ratio, charging time, and reduced temperature difference of the storage system.

The safety of oil and gas pipelines has increasingly considered day by day to their vulnerability. Pipelines play a very critical role in the transportation of oil and natural-gas. As they have become the veins of oil industries, the productive design and analysis became more important. This made them more vulnerable to terrorist attacks. Although it is impossible to design pipelines to withstand any conceivable damage due to external (terrorist attacks, seismic effects) and internal effects (design and manufacturing defects), it is possible to improve the performance of pipelines. By understanding the design criteria, it saves lots of money and more over human lives and also protects the product in pipelines, which cannot be recovered and which is more and more scares day by day. This research aims: 1) to understand the different types of pipeline damages, reasons for their occurrence and their effects on the pipelines, such as mechanical damages, material defects, cracks, manufacturing defects, 2) to understand the explosions in pipelines, internal or external explosions and seismic distress, 3) to do research and literature review in analytical and numerical methods which allow researching the influence of shock waves (explosions, seismic), 4) to develop description of experimental research of pipelines subjected to shock waves (explosions, seismic), 5) to establish an effective methodology (develop mathematical model) to study the risk management in pipeline exploitation which can be subjected to such conditions like shock waves (caused by explosions, seismic, as well as mining activities) on pipeline systems (buried, on surface, or underwater), and 6) to establish criteria for risk management. This paper includes a review of the related literature covering the first two goals.Copyright © 2007 by ASME

Various methods for designing hydrodynamic partial journal bearings are reviewed and an integrated and dependable design procedure is (developed. Knowledge and rule bases pertaining to the design of journal bearings having arcs of 180°, 120°. and 60° are either gathered or derived and represented properly. An expert system is developed using the databases and rulebases. The bearing design is based on one of the following decision criteria: the maximum load, the minimum friction, or the optimal clearance The expert system makes an exhaustive search for all the design solutions. Utility value of each of the final solutions is calculated and the design solutions having utility values above a certain limit are stored The results are presented to demonstrate the usefulness of the knowledge-based approach.