An Economical Approach to Stop an Experimental Campaign with the Aim of Reducing Cost

Nowadays, in a period of stagnation and economic crisis, the continuous improvement of the production technologies in order to optimize economic, energetic and productive resources is crucial. The increase in efficiency, measured in terms of cost reduction, is therefore a key problem that requires the attention of more and more companies and researchers. In particular, the productivity of a machining system and its related costs depend on the setup of the machining parameters. This choice plays a key role when the machining material is expensive, the production batch has a limited size and the tool to be used is new: typical examples are the aircraft and die/mold industries. In order to optimally setup a machine, the study of the tool life according to the material and the machining parameters is critical. The expression of the tool life could be estimated using an appropriate experimental campaign, which should have a limited size in order to reduce the experimental costs. This approach becomes of primary importance when the production is not in series where the costs can be spread over a large number of pieces. The aim of this paper is to propose a new methodology that stops the experimental campaign as soon as the expected gain in carrying on the experimentation does not justify the marginal cost of experimentation. To prove our idea, a simple problem from the well-known turning cutting condition optimization is used and the optimization technique Response Surface Methodology is selected

Densification mechanism for different types of stainless steel powders in Selective Laser Melting

Selective laser melting is a powder based additive manufacturing process where the metallic powder particles are melted by a high power laser beam. Different types of stainless steel powders made by gas and water atomization were analyzed before processing, in particular regarding their particle size distributions and morphology. Particle analysis was carried out using laser diffraction technologies and digital image analysis. A suitable designed experiment has been carried out and the specimen density has been measured and linked to the properties of the powders. Eventually the possibility to reach high density specimen by adjusting process parameters is discussed

Efficacy of a new technique - INtubate-RECruit-SURfactant-Extubate - "IN-REC-SUR-E" - in preterm neonates with respiratory distress syndrome: Study protocol for a randomized controlled trial

Background: Although beneficial in clinical practice, the INtubate-SURfactant-Extubate (IN-SUR-E) method is not successful in all preterm neonates with respiratory distress syndrome, with a reported failure rate ranging from 19 to 69 %. One of the possible mechanisms responsible for the unsuccessful IN-SUR-E method, requiring subsequent re-intubation and mechanical ventilation, is the inability of the preterm lung to achieve and maintain an "optimal" functional residual capacity. The importance of lung recruitment before surfactant administration has been demonstrated in animal studies showing that recruitment leads to a more homogeneous surfactant distribution within the lungs. Therefore, the aim of this study is to compare the application of a recruitment maneuver using the high-frequency oscillatory ventilation (HFOV) modality just before the surfactant administration followed by rapid extubation (INtubate-RECruit-SURfactant-Extubate: IN-REC-SUR-E) with IN-SUR-E alone in spontaneously breathing preterm infants requiring nasal continuous positive airway pressure (nCPAP) as initial respiratory support and reaching pre-defined CPAP failure criteria. Methods/design: In this study, 206 spontaneously breathing infants born at 24+0-27+6 weeks' gestation and failing nCPAP during the first 24 h of life, will be randomized to receive an HFOV recruitment maneuver (IN-REC-SUR-E) or no recruitment maneuver (IN-SUR-E) just prior to surfactant administration followed by prompt extubation. The primary outcome is the need for mechanical ventilation within the first 3 days of life. Infants in both groups will be considered to have reached the primary outcome when they are not extubated within 30 min after surfactant administration or when they meet the nCPAP failure criteria after extubation. Discussion: From all available data no definitive evidence exists about a positive effect of recruitment before surfactant instillation, but a rationale exists for testing the following hypothesis: a lung recruitment maneuver performed with a step-by-step Continuous Distending Pressure increase during High-Frequency Oscillatory Ventilation (and not with a sustained inflation) could have a positive effects in terms of improved surfactant distribution and consequent its major efficacy in preterm newborns with respiratory distress syndrome. This represents our challenge. Trial registration: ClinicalTrials.gov identifier: NCT02482766. Registered on 1 June 2015

Process optimization via confidence region: a case study from micro-injection molding

In industrial research, experiments are designed to determine the optimal factor levels of the process parameters. Typically, experimental data are used to fit empirical models (for example, regression models) to derive one set of optimal conditions that maximize (or minimize) the response. Unfortunately, the optimization rarely provides a Confidence Interval for the location of the optimal solution, even though the optimal solution itself is subjected to variability. From a practitioner's point of view, identifying a region of possible optimal values provides high operational flexibility to adjust process parameters online during production. This paper provides a procedure for computing a confidence region for the optimal point based on experimental data, bootstrapping, and data depth. The procedure is validated using a case study from micro-injection molding, where the part weight is maximized under a constraint of the probability of flash formation. The proposed method considers that the objective function (part weight) and the constraint (probability of flash formation) are estimated from experimental data and subjected to sampling variability

Fast optimisation procedure for the selection of L-PBF parameters based on utility function

L-PBF is an additive manufacturing process forming parts with complex geometries by adding material layer by layer. The selection of the process parameters in L-PBF has a significant impact on the mechanical properties of the printed parts. Scan speed, laser power, and hatch distance are among the most influential process parameters in L-PBF because, depending on their combination, different solidification mechanisms take place. However, the procedure for selecting these parameters can be expensive from an experimental point of view. Therefore, it is necessary to identify simplified models that allow fast and reliable optimization of the parameters in L-PBF. Furthermore, the choice of parameters cannot be based exclusively on qualitative aspects but must also consider the productivity of the process to obtain a satisfactory compromise. Increasing productivity leads to the formation of lack of fusion porosity which should be avoided. This paper proposes a procedure for selecting parameters based on a semi-analytical thermal model, which, together with a geometric-based defect model, allows identifying an optimality region where good solidification and productivity are considered. The optimization is carried using a properly defined utility function. The procedure is validated through the production of AISI 316L specimens using an industrial L-PBF system

The effect of energy density and porosity structure on tensile properties of 316L stainless steel produced by laser powder bed fusion

Understanding the influence of process parameters and defect structure on the properties of parts produced via laser powder bed fusion (L-PBF) is a fundamental step towards the broader use of additive manufacturing technologies in critical applications. Furthermore, the ability to predict mechanical properties by simply considering information on the process parameters and defects observed via X-ray computer tomography (XCT) allows one to avoid expensive destructive testing, provide an in-depth understanding of the process quality and represents a viable solution towards process optimisation. Most of the previous works showed that energy density could be used as an excellent synthetic indicator to predict the mechanical properties of parts produced by L-PBF. This paper explores the effect of different energy density levels on the tensile properties of 316L stainless steel parts produced by L-PBF. Different from previous works in the literature, the same level of energy density is obtained considering various combinations of process parameters (speed, power and hatch distance). While energy density is shown to be a good synthetic indicator for predicting ultimate tensile strength (UTS) and yield strength (YS), a different behaviour is observed for elongation. Elongation shows a significant variability even when samples are produced at the same level of energy density, which contrasts with results obtained for UTS and YS. Synthetic indices representing the porosity structure are shown to be quite significant for predicting elongation even when the optimal energy density is considered. By combining process parameters with porosity structure, we show that almost a full prediction of the tensile properties can be achieved, paving the way for a significant reduction in expensive destructive tests

Optimization of cutting conditions using an evolutive online procedure

This paper proposes an online evolutive procedure to optimize the Material Removal Rate in a turning process considering a stochastic constraint. The usual industrial approach in finishing operations is to change the tool insert at the end of each machining feature to avoid defective parts. Consequently, all parts are produced at highly conservative conditions (low levels of feed and speed), and therefore, at low productivity. In this work, a framework to estimate the stochastic constraint of tool wear during the production of a batch is proposed. A simulation campaign was carried out to evaluate the performances of the proposed procedure. The results showed that it was possible to improve the Material Removal Rate during the production of the batch and keeping the probability of defective parts under a desired level