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The purpose of this paper is to focus on conducting accelerated life tests on aluminium electrolytic capacitors under accelerated temperature and voltage stress to study the effect of applied voltage and ambient temperature on the capacitor, its degradation over time, failure data collection, analysis and then modelling the failure times. Principles of DOE are used for studying the effect of temperature and voltage.

Life tests are conducted at three levels of temperature and applied voltage and the life of capacitor is ascertained at each treatment level. Life variation with voltage and temperature is studied to gain an insight as to how these factors affect the lifetime of the capacitor. The interaction effect of temperature and voltage on capacitor life is also established.

The life of the capacitor decreases exponentially with temperature and voltage at all the three factor levels. Ambient temperature, applied voltage and their interaction effect significantly affects the life of the capacitor. Applied voltage has the greatest effect followed by ambient temperature and then their interaction effect. Life of the capacitor has been estimated as 4,206 hrs when only voltage is taken as the accelerated stress using Inverse Power Law and as 4,003 hrs when both temperature and voltage are taken as accelerating stress using combination model.

This work consider only decrease in capacitance as the failure criterion. However, as a future scope, it is proposed that test may be conducted by taking into consideration not only the decrease in capacitance as the failure criteria but by monitoring all the performance parameters of the capacitor. This would give a more realistic assessment of life as it is possible that capacitor may have failed much before it reached the lower threshold capacitance value.

This work has lots of practical implications. It shows how DOE approach can be used for ALT data analysis and identification and effect of critical stresses acting on capacitors in real practice. Most critical types of stresses affecting the reliability can thus be controlled to ensure better performance. Product manufactures as well as users will be benefited by such findings. The paper has also illustrated how failure data can generated by degradation analysis using life test data collection at discrete intervals.

The methodology presents an alternative non traditional approach of accelerated life testing, which does not require continuous monitoring of test items. This only requires intermittent monitoring which reduces the need of test resources. Though the degradation study itself is not new but using degradation study for ALT data generation is new. This approach may considerably reduce the test duration and resources used for ALT. DOE approach gives more tangible result to study the effect of various variables on the dependent variable. As DOE approach uses a fractional factorial design, it can be very helpful to conduct life tests with minimum number of test units (only a fraction of full factorial test units). This will considerably reduce the test duration, resources requirement for testing, easier but accurate data analysis, and faster product development, especially when ALT is to be conducted at several stresses simultaneously.

The purpose of this study was to optimise the dyeing conditions to achieve right-first-time dyeing in hard water. Owing to the persistent water scarcity for more than two decades now, the textile industry in Pakistan is forced to rely on high-mineral-content ground water for use in textile wet processing. Furthermore, the limited amount of municipal water that is at the disposal of the textile industry is also high in mineral content. Thus, on the large scale, water hardness has become an acute problem for the textile processor. In particular, in the dyeing process, water hardness is known to have crucial effects. However, to-date, no systematic study has been conducted on this aspect of textile dyeing.

In this study, 3² full factorial design was used to optimise the dyeing conditions to achieve right-first-time dyeing in hard water. Thus, cotton fabric was dyed with Red Reactive dye (of dyebath concentration at 5, 10 and 15 g/L) in prepared hard water (of hardness at 10, 40 and 70°dH), respectively. Analysis of variance, coefficient of determination (R²) and p-values for the models were used to evaluate the adequacy of the predictive models. The surface plots of the effects were studied to further examine the interactions of two independent variables. Derringer’s desirability function was used to determine the optimum levels of each variable.

Three levels for both independent variables generate second-order polynomial models to predict the colour strength, lightness, red/green, yellow/blue and total colour difference values of dyed cotton. The obtained predictive models point out the considerable influence of both water hardness and dye concentration on right-first-time dyeing.

Such a finding enabled the dye-mill to produce the correct shade at water hardness of 10°dH and 15 g/L dye concentration, without the need for corrective reprocessing.

Process parameters in Fused Filament Fabrication (FFF) can affect mechanical and surface properties of printed parts. Numerous studies have reported parametric studies of various materials using full factorial and Taguchi design of experiments (DoEs). However, a comparison between the two are not well-established in literature. The purpose of this study is to compare full factorial and Taguchi DoEs to determine the effects of FFF process parameters on mechanical and surface properties of Nylon 6/66 copolymer. In addition, perform in-depth failure mechanism analysis to understand why the process parameters affect the responses.

A full factorial DoE was used to determine the effects of FFF process parameters, such as infill density, infill pattern, layer height and raster angle on responses, such as compressive strength, impact strength, surface roughness and manufacturing time of Nylon 6/66. Micro-computed tomography was used to analyse the impact test samples before and after impact and scanning electron microscope was used to understand the failure mechanism of infill and top layers. Differential scanning calorimetry (DSC) scans of infill and top layers were then taken to determine if a variation in crystallinity existed in different regions of the build.

Analysis of variance and main effects plots reveal that infill density has the greatest effect on mechanical and surface properties while manufacturing time is most affected by layer height for the polymer used. A 20% reduction in infill increased impact strength by 19% on average, X-ray images of some of the samples before and after impact tests are presented to understand the reason behind the difference. Moreover, DSC revealed a difference in the degree of crystallinity between the infill and top layers for 80% infill density samples. In addition, Taguchi DoE is realized to be a more efficient technique to determine optimum process parameters for responses that vary linearly as it reduces experimental effort significantly while providing mostly accurate results.

To the author’s knowledge, no published paper has reported a comparison between predictive DoE method with full factorial DoE to verify their accuracy in determining the effects of FFF process parameters on properties of printed parts. Also, a theory was developed based on DSC results that as the infill is printed faster, it cools slowly compared to the top layers, and hence the infill is in a less crystalline state when compared to the top layers. This increased the ductility of the infill (of 80% infill samples) and thus improved impact absorption.

Policy-capturing is an experimental technique potentially capable of providing powerful insights into the cognitive bases of work-related decision processes by revealing actors’ “implicit” models of the problem at hand, thereby opening up the “black box” of managerial and organizational cognition. This chapter considers the strengths and weaknesses of policy-capturing vis-à-vis alternative approaches that seek to capture, in varying ways, the inner workings of people’s minds as they make decisions. It then outlines the critical issues that need to be addressed when designing policy-capturing studies and offers practical advice to would-be users concerning some of the common pitfalls of the technique and ways of avoiding them.

Studies the feasibility of using the metamodelling technique for the performance analysis of a just‐in‐time manufacturing system. The data for the analysis are generated by a Siman simulation model. Develops a regression metamodel following an R‐IV fractional factorial design. Results show that, of the 15 variables considered, only assembly time, kanban capacity, and the interaction effect between demand and kanban capacity are statistically significant. Performs cross‐validation. Finds the results of the metamodel developed to supplement the simulation model to be accurate.

The purpose of this paper is to show how to properly use the method of replacement to construct mixed two- and four-level minimum setup split-plot type designs to accommodate the presence of hard-to-assemble parts.

Split-plot type designs are economical approaches in industrial experimentation. These types of designs are particularly useful for situations involving interchangeable parts with different degrees of assembly difficulties. Methodologies for designing and analyzing such experiments have advanced lately, especially for two-level designs. Practical needs may require the inclusion of factors with more than two levels. Here, the authors consider an experiment to improve the performance of a Baja car including two- and four-level factors.

The authors find that the direct use of the existing minimum setup maximum aberration (MSMA) catalogs for two-level split-plot type designs may lead to inappropriate designs (e.g. low resolution). The existing method of replacement for searching exclusive sets of the form (α, β, αβ) available in the literature is suitable for completely randomized designs, but it may not provide efficient plans for designs with restricted randomization.

The authors provide a general framework for the practitioners and have extended the algorithm to find out the number of generators and the number of base factor at each stratum, which guide the selection of mixed two-level and four-level MSMA split-plot type designs.

Increasing scarcity of natural resources and the adverse effects of unsustainable practices call for more and more efficient management strategies in the energy industry. The quality of the coke plays a significant role in the quality and durability of the output steel which is produced using the energy from the coal. This paper aims to investigate the dynamic coal blending problem under overall cost and coke quality constraints in the steel industry within a periodic cycle of operations.

Considering the variability of the natural properties over a periodic cycle, this study proposes a multi-period mixed-integer non-linear programming formulation to optimize the total blending costs while taking various coke quality constraints into account. Besides, this study applies factorial design to investigate about the significant effect of coal proportions as well as improvement into the overall cost of blending.

In this case study, utilizing real data from a coal blending facility in India, through a factorial design, the authors obtain optimal desirable levels of coal proportions and their criticality levels towards the total cost of blending (TCB) or objective function. This analysis reflects the role of the coke quality constraints in the objective function value while characterizing the price of sustainability for the case study among other critical insights.

Objective function (or TCB) includes basic coal cost, movement cost and environmental costs during the coal and coke processing at a coke-oven and blast furnace of steel industry. The price of sustainability provides managerial insights on that sacrifices the industry has to make in order to become more “sustainable”.

Since quality cannot be manufactured or tested into a product but must be designed in, effective product design is a prerequisite for effective manufacturing. However, the concept of effective product design involves a number of complexities. First, product design often overlaps with such design types as engineering design, industrial design and assembly design. Second, while costs are key variables in product design, costing issues often arise that add more complexities to this concept.

The management accounting literature provides activity-based costing (ABC) and target costing techniques to assist product design teams. However, when applied to product design these techniques are often flawed. First, the product “user” and “consumer” are not identical as often assumed in target costing projects, and instead of activities driving up the costs, managers may use budgeted costs to create activities to augment their managerial power by bigger budgets and to protect their subordinates from being laid off. Second, each of the two techniques has a limited costing focus, activity-based costing (ABC) focusing on indirect costs and target costing on unit-level costs. Third, neither technique accounts for resource interactions and cost associations.

This paper applies the new method of associative costing (Bayou & Reinstein, 2000) that does not contain these limitations. To simplify the intricate procedures of this method, the paper outlines and illustrates nine steps and applies them to a hypothetical scenario, a design of a laptop computer intended for the college-student market. This method uses the well-known statistical techniques of clustering, Full Factorial design and analysis-of-variance. It concludes that in product design programs, the design team may need to make tradeoff decisions on a continuum beginning with the design-to-cost point and ending at the cost-to-design extreme, as when the best perceived design and the acceptable cost level of this design are incongruent.

Documents a case study in the use of Shainin Design of Experiments in an industrial honing operation. Provides an overview of the process which attempts to reduce process variability by isolating the most influential factor (Red X). Shows the details of the week‐long experiment and gives some analysis of the data. Uses multiple statistical techniques to identify Red X and discusses corrective action. Suggests that this methodology is very practical and easily executable in many settings, making it one of the most approachable quality techniques available.

The aim of this paper is to proposes a simple and unified method for generating the aliasing pattern of two‐ and three‐level fractional factorial designs be they regular or non‐regular. The paper also demonstrates how the aliasing patterns obtained using the postulated method can be used to render the main measures of aliasing severity.

The proposed method is based on viewing the fractional factorial designs geometrically. It entails regarding the columns of any design as vectors. On the premise that any two vectors are orthogonal if they are at right angle and bearing in mind that aliasing is a form of departure from orthogonality, the proposed method determines the degree of aliasing between any two columns by assessing the extent to which the angle between them differs from 90°.

Three examples were used to illustrate how the proposed method can be applied and to validate its results. The first dealt with a regular two‐level L₈ 2⁵⁻² design whereas the second concerned a non‐regular two‐level L₁₂ design used to study five‐factors, and the third example is based on a non‐regular L₁₈ design employed to examine three‐factors at three‐levels. For each of these, the aliasing pattern generated using the proposed method matches the one obtained using the conventional methods.

A recent empirical study of how experimental design is applied in certain Manufacturing Engineering journals revealed that aliasing is rarely investigated. One possible reason for this is the difficulty associated with comprehending the conventional methods of dealing with aliasing particularly in the cases where non‐regular two‐ and three‐level orthogonal arrays are used. The proposal of a simple and unified method for dealing with aliasing should encourage the researchers and practitioners to assess aliasing when performing their experiments.