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The rise of artificial intelligence techniques provides new opportunities in the robust design and tolerancing fields. These technological growths require adapting new practices. In this paper, a new tolerance allocation approach based on supervised machine learning (decision tree) and Monte Carlo simulation is proposed. The most important branch of the decision tree which is generated based on the results of the Monte Carlo simulation, is analysed and the best tolerances to be allocated are defined in terms of quality/cost trade-off. The effectiveness of the proposed approach is demonstrated and discussed through a non-linear problem: a Jansen linkage study. [ABSTRACT FROM AUTHOR]

The tolerancing process impacts the product quality, the production cost and scrap rate. Tight tolerances allow to assure product performance; loose tolerances to reduce production cost. The tolerance allocation of a complex system is performed under uncertainty. In fact, the accuracy of the behaviour simulation of the system significantly affects the tolerance analysis result, and thus the tolerance allocation result. Therefore, a method is proposed to perform tolerance allocation based on the Dempster Shafer theory, Monte-Carlo simulation and genetic algorithm. The application of the proposed framework is demonstrated through a complex case study. [ABSTRACT FROM AUTHOR]

Tolerance analysis is a well-accepted key element in industry for ensuring product quality as well as for reducing manufacturing costs. At the same time, and particularly in light of the recent advances in simulation technology, tolerancing decisions are also becoming increasingly important during earlier stages of design. One critical task hereby is the simulation of the "real-world" behavior of the product with minimum uncertainty, i.e. the calculation how geometrical deviations impact the mechanical behavior and/or multiple simultaneous physical phenomena in a multiphysical system. Given the short iterations in design, this usually represents a compromise between two contradictory requirements: an acceptable computation time and the accuracy of the results. The presented paper addresses this challenge by presenting a framework to assess the impact of model parameter uncertainty of the multiphysical system behavior on the accuracy of the results. The framework integrates evidence and probability theories to propagate geometrical variability and model imprecision for tolerance analysis. The information regarding geometrical variability is modelled using probability distributions; and the information regarding the model imprecision is more faithfully modelled using families of probability distributions encoded by probability-boxes (upper & lower cumulative distribution functions). Monte Carlo simulation is used for probabilistic analysis while nonlinear optimization is used for interval analysis. [ABSTRACT FROM AUTHOR]

This paper deals with production variabilities and their formalization: Geometrical variability that affects the product quality and Time variability that affects the productivity. Due to the manufacturing imperfections, the manufacturing system faults, the human factors,... it is not possible to attain the theoretical dimensions and the theoretical time in a repetitive manner. This causes degradations of the product & production system performances. In order to ensure the desired behavior and the performance of the engineering system in spite of uncertainty, the system features are assigned a permissible tolerance or a margin. Time margin allocation consists in establishing a certain level of work flexibility, which gives the operator the possibility to develop different ways of working to meet production targets, without having adverse effects on his or her health. The concepts of geometric tolerance and time margin are very similar; they directly have an impact on the Overall Equipment Effectiveness (OEE): An OEE of 100% means that only good parts are produced (100% quality), at the maximum speed (100% performance), and without interruption (100% availability). The human or manufacturing system fatigue and reliability affect directly the respect of the time margin and indirectly the product conformance. There exists a strong link between tolerance and time margin. [ABSTRACT FROM AUTHOR]

Variability is unavoidable in the realization of products. While design must specify ideal geometry, it shall also describe limits of variability (tolerances) that must be met in order to maintain proper product function. Although tolerancing is a mature field, new manufacturing processes and design methodologies are creating new avenues of research, and modelling standards must also evolve to support these processes. In addition, the study of uncertainty has produced widely-accepted methods of quantifying variability, and modern tolerancing tools should support these methods. The challenges introduced by new processes and design methodologies continue to make tolerancing research a fertile and productive area. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) became an advanced research topic due to its ability to manufacture complex shapes. But the ability to achieve predictable and repeatable shapes is critical. Therefore, to optimize the design of an additive manufactured product, tolerancing is a key issue. This paper focuses on geometrical quality assessment of an AM product. It includes a process oriented geometrical model to predict the surface roughness and dimensional deviations, and a geometrical simulation tool to assess the impacts of these deviations on the geometrical behaviour of the joint. An application of the approach is illustrated through a case study. [ABSTRACT FROM AUTHOR]

In the product design process, the designer needs to figure out how to integrate tolerancing process to ensure the transition from function to tolerances. Our proposition is to use energy flow modeling in Characteristics-Properties Modeling (CPM). CPM creates a framework containing function and structure of the product. Energy flow creates the link between function and structure in CPM using energy-based behavior modeling. Considering both qualitative and quantitative aspects, the result is an approach to create a quantitative function-behavior- structure relationship to ensure the transition from function to geometrical specifications. This systematic approach can be used to evaluate the tolerance impact on system's function. [ABSTRACT FROM AUTHOR]

Abstract: Uncertainty in product characteristics is ubiquitous in any engineering system at all the stages of product life-cycle. Considering uncertainty from different sources during the product design phase is critical to its reliable performance. This paper presents a framework integrating the uncertainty propagation through different product characteristics and its effect on product properties. The framework consists of three main parts: a descriptive model based on formal logic and characteristics properties model; a mathematical implementation through set theory and probabilistic approach; and an algorithm for design space evaluation and tolerancing. The application of framework is demonstrated through an industrial case study. [Copyright &y& Elsevier]