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Manufacture of a product in a desired shape and size with the desired characteristics and properties depends not only on the design of the product but also on the selection of an appropriate manufacturing process(es), which requires knowledge about the various alternatives available. This paper describes the process selection methodology for unconventional or advanced machining processes (AMPs), along with a preliminary selection strategy for basic type of manufacturing process. These two tasks along with parametric optimization form the core of an integrated and automated process planning system for an advanced machining environment. The process selection methodology for the AMPs is based on elimination and ranking strategy. To facilitate the process selection, AMPs have been reclassified or regrouped according to their material application capabilities, shape or manufacturing feature generating capabilities, operational capabilities, economic aspects, and environmental aspects. The described process selection methodologies for basic manufacturing process and AMPs, have been implemented in a software named as APSPOAMPS (Automated Process Selection and Parametric Optimization of AMPs). This paper also describes the proposed reclassifications of AMPs, implementation details of the developed software along with the two test examples.

The application of additive manufacturing (AM) has increased exponentially in recent years. Industries are keen to explore this innovative technology but are apprehensive about the high processing cost of the process. Hence, it is crucial to carry out a cost analysis of the process. This paper presents an approach to compare the costs of an AM process (selective laser sintering (SLS)) and a traditional manufacturing process (injection molding (IM)) in the presence of uncertainties. Initially, the deterministic cost models comprising necessary cost variables for SLS and IM are described. The deterministic models are converted to fuzzy set-based models for tackling uncertainties. For this purpose, important uncertain variables are treated as fuzzy members and fuzzy arithmetic is employed. Only linear triangular fuzzy numbers are used in this work. Fuzzy cost estimates produce three values (low, most likely and high estimates) of cost corresponding to membership grades. A methodology to compare two fuzzy costs of the processes is proposed for a variable demand scenario. Concept of fuzzy reliability is suitably utilized and variability in demand is tackled from probability theory. Variable demand is assumed to follow uniform as well as normal probability distributions. The methodology is illustrated with the help of two examples.