Cost assessment for rapid manufacturing (RM) is highly dependent on time estimation. Total build time dictates most indirect costs for a given part, such as labour, machine costs, and overheads. A number of parametric and empirical time estimators exist; however, they normally account for error rates between 20 and 35 per cent which are then translated to inaccurate final cost estimations. The estimator presented herein is based on the ability of artificial neural networks (ANNs) to learn and adapt to different cases, so that the developed model is capable of providing accurate estimates regardless of machine type or model. A simulation is performed with MATLAB to compare existing approaches for cost/time estimation for selective laser sintering (SLS). Error rates observed from the model range from 2 to 15 per cent, which shows the validity and robustness of the proposed method.

PurposeThis paper seeks to present the results of a study carried out within rapid manufacturing (RM) service providers and engineering centres in Northern Spain. By disclosing strategies for their everyday operation, it is intended to show how the internal expertise acquired overtime copes with the lack of standards within this industry.Design/methodology/approachThe study was deployed by means of a survey including four main issues: RM concepts, process planning, materials and costs. Questions range from general RM perceptions to specific production criteria like: layer thicknesses, laser power, quality assurance methods, etc. A special emphasis is made on cost parameters, since they play a major role when selecting the final manufacturing route.FindingsThe so‐called “de facto standards” were found to be widely used in order to minimize production risks for RM. The study also suggests the need for specific RM standards based on key issues like material recycling, process planning and costs assignment.Practical implicationsThe study is mainly focused on additive RM processes used in Spanish centres. Although, some other technologies applied elsewhere might not be considered, it is assumed that most of the technologies mentioned in this study are available worldwide, thus providing valuable information to increase the general RM base of knowledge.Originality/valueUnlike most of the RM literature based on benchmarking of processes to provide information, this paper shows first hand data from users and service providers, showing RM practices and preferences from a different approach.

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In this work, the melting process of a polymeric material is numerically investigated. In general, the heat required for melting plastics is obtained throughout electrical resistances or by burning fossil fuels. The heat transfer mechanisms of these common practices correspond to conduction and convection, respectively. This work explores the feasibility of using radiation as the primary mechanism of energy supply, which has not been widely studied. The energy to achieve the phase change from solid to liquid can be obtained by concentrated solar energy radiation. The total energy required is calculated solving the energy equation using the enthalpy formulation.  An explicit formulation with an enthalpy linearization was implemented in the Mathematica programming language and compared with the solution in the commercial softwares Ansys Fluent and COMSOL Multiphysics showing a good agreement. Based upon numerical predictions, it is examined the effects of the relevant parameters, such as incident radiation and convective heat transfer coefficient, on the melting process. It is observed that under weather conditions commonly attained in different cities worldwide, with a radiation of 1000 W/m$^2$ and low convective losses with $h=8$ W/m$^2\cdot$K, the melting process of a cylindrical rod of 3/4 inches diameter can be carried out in around 2 hours.