Design of general-purpose sampling strategies for geometric shape measurement

Quality inspection is a preliminary step for different further analyses (process monitoring, control and optimisation) and requires one to select a measuring strategy, i.e., number and location of measurement points. This phase of data gathering usually impacts on inspection times and costs (via sample size) but it also affects the performance of the following tasks (process monitoring, control and optimisation). While most of the approaches for sampling design are specifically presented with reference to a target application (namely, monitoring, control or optimisation), this paper presents a general-purpose procedure, where the number and location of measurement points are selected in order to retain most of the information related to the feature under study. The procedure is based on principal component analysis and its application is shown with reference to a real case study concerning the left front window of a car. A different approach based on multidimensional scaling is further applied as validation tool, in order to show the effectiveness of the PCA solution

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

Thin wall geometrical quality improvement in micromilling

Micromilling is one of the most versatile tooling processes being able to effectively manufacture three-dimensional complex features on moulds and dies achieving a good accuracy performance. Typical and challenging features for these microcomponents are high aspect ratio thin walls but no systematic approaches, as the one presented in this paper, exist in literature dealing with the relationship between nominal workpiece characteristics/process parameters, cutting forces, and workpiece quality. The present study focuses on 0.4 % carbon steel (C40) thin wall micromilling and evaluates two approaches for the thin wall geometrical quality improvement: a direct approach (relating process parameters, material and nominal workpiece characteristics to the workpiece quality characteristics) and a force-based approach (relating the same quantities through the cutting forces determination). The force-based approach relates the process parameters to the workpiece quality introducing physical quantities as cutting forces, which are suitable for monitoring and controlling purposes. A suitable experimental campaign has been designed in order to statistically analyze the cutting force responses, and a proper technique (ANalysis of COVAriance) has been applied to remove the tool wear effect. The relationship between cutting forces and workpiece quality has been quantitatively studied; this way, the feasibility of a general approach able to meet tolerances by controlling forces has been demonstrated

Experimental comparison between traditional and cryogenic cooling conditions in rough turning of Ti-6Al-4V

Titanium alloys, mainly because of their poor thermal conductivity, need to be cut at relatively low cutting speeds to avoid a severe diffusion wear, with obvious negative consequences on the profitability of machining. An important amount of research activities has been done in order to increase productivity in titanium machining operations and one of the most promising solutions is represented by the use of liquid nitrogen as a coolant during the machining operation. The aim of this paper is to compare traditional and cryogenic turning of Ti6Al4V in a region of cutting parameters particularly relevant to the aerospace industry where no previous data are available. The cutting parameters are those typical of titanium alloys rough machining which is considered, cost-wise, the most important operation because, for aerospace components, the so-called Buy-To-Fly ratio can reach values up to 20:1. The experiments have been performed using a full factorial design in order to statistically evaluate, using ANOVA and regression analyses, the significance of the input factors on the process most interesting outputs. The considered input factors are: type of cooling method, cutting speed and feed rate. The main analysed responses are: tool wear, surface roughness, cutting forces, coefficient of friction and chip morphology. The results show the significance of the cooling method on the tool life and that cryogenic machining is able to increase the tool life with respect to wet cutting. On the other hand, the beneficial effect of the liquid nitrogen cooling is reduced at high cutting speed and feed rate. Besides, the results showed that a small but significant reduction can be achieved for both the repulsion force and the coefficient of friction at the tool-workpiece interface

A comparison study of distribution-free multivariate SPC methods for multimode data

The data-rich environments of industrial applications lead to large amounts of correlated quality characteristics that are monitored using Multivariate Statistical Process Control (MSPC) tools. These variables usually represent heterogeneous quantities that originate from one or multiple sensors and are acquired with different sampling parameters. In this framework, any assumptions relative to the underlying statistical distribution may not be appropriate, and conventional MSPC methods may deliver unacceptable performances. In addition, in many practical applications, the process switches from one operating mode to a different one, leading to a stream of multimode data. Various nonparametric approaches have been proposed for the design of multivariate control charts, but the monitoring of multimode processes remains a challenge for most of them. In this study, we investigate the use of distribution-free MSPC methods based on statistical learning tools. In this work, we compared the kernel distance-based control chart (K-chart) based on a one-class-classification variant of support vector machines and a fuzzy neural network method based on the adaptive resonance theory. The performances of the two methods were evaluated using both Monte Carlo simulations and real industrial data. The simulated scenarios include different types of out-of-control conditions to highlight the advantages and disadvantages of the two methods. Real data acquired during a roll grinding process provide a framework for the assessment of the practical applicability of these methods in multimode industrial applications

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