PurposeThe purpose of this paper is to present a state‐of‐the‐art review of dimensional tolerance synthesis and to demonstrate the evolution of tolerance synthesis from product to process‐oriented strategy, as well as to compare the same for single stage and multistage manufacturing systems (MMS). The main focus is in delineating the different approaches, methods and techniques used with critical appraisal of their uses, applicability and limitations, based on which future research directions and a generic methodology are proposed.Design/methodology/approachStarting with issues in tolerancing research, the review demonstrates the critical aspects of product and process‐oriented tolerance synthesis. The aspects considered are: construction of tolerance design functions; construction of optimization functions; and use of optimization methods. In describing the issues of process‐oriented tolerance synthesis, a comparative study of single and multistage manufacturing has been provided.FindingsThis study critically reviews: the relationship between the tolerance variables and the variations created through manufacturing operations; objective functions for tolerance synthesis; and suitable optimization methods based upon the nature of the tolerance variables and the design functions created.Research limitations/implicationsThis study is limited to dimensional tolerance synthesis problems and evolution of process‐oriented tolerance synthesis to counteract dimensional variation problems in assembly manufacturing.Originality/valueThe paper provides a comprehensive and step‐by‐step approach of review of dimensional tolerance synthesis.
Consumer acceptance of food products is largely driven by the dietary and functional quality of their ingredients. Though whole cereal grains are well known for bioactive components, scientists are facing dire need for better technologies to prevent the nutritional losses incurred through the conventional food processing technologies. Application of enzyme for depolymerisation of carbohydrates present in bran layer of grain is becoming an efficient method for phenolic mobilization and dietary fiber solubilisation. The present article emphasizes deep insights about the application of enzyme as an alternative technology for cereal grain processing to improve the product quality while forbidding the nutritional losses in an eco-friendly manner.
“Smart cities” start with “Smart Buildings” that improve the quality of urban services while ensuring sustainability. The current scenario in India reveals that the corporate and residential building structures are incorporating various self-sustainable techniques. Out of the multiple factors governing the comfort of smart buildings, indoor room temperature is an important one, since it drives the need of cooling or heating through controlling systems. Around one-third of total energy consumption of commercial buildings in India is attributed to Heating, Ventilation and Air Conditioning (HVAC) systems. Accurate prediction of indoor room temperature helps in creating an efficient equilibrium between energy consumption and comfort level of the building, thus providing opportunities for efficient decision making for energy optimization. Considering Indian climatic and geographical conditions, this paper proposes an efficient decision making approach using Bayesian Dynamic Models (BDM) for short-term indoor room temperature forecasting of a corporate building structure. The results obtained from Bayesian Dynamic linear model, using Expectation Maximization (EM) algorithm, have been compared to standard Auto Regressive Integrated Moving Average (ARIMA) model, and have been found to be more accurate. Forecasting of indoor room temperature is a highly nonlinear phenomenon, so to further improve the accuracy of the linear models, a hybrid modeling approach has been proposed. The inclusion of state-of-the-art nonlinear models such as Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) improves the forecasting accuracy of the linear models significantly. Results show that the hybrid model obtained using BDM and ANN is the best fit model.
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