Defect Probability Estimation for Hardness-Optimised Parts by Selective Laser Melting

Abstract

The development of reliable additive manufacturing (AM) technologies to process metallic materials, e.g. selective laser melting (SLM), has allowed their adoption for manufacturing final components. To date, ensuring part quality and process control for low-volume AM productions is still critical because traditional statistical techniques are often not suitable. To this aim, extensive research has been carried out on the optimisation of material properties of SLM parts to prevent defects and guarantee part quality. Amongst all material properties, defects in surface hardness are of particular concern as they may result in an inadequate tribological and wear resistance behaviour. Despite this general interest, a major void still concerns the quantification of their extent in terms of probability of defects occurring during the process, although it is optimised. Considering these issues, this paper proposes a novel approach to quantify the probability of occurrence of defects in hardness-optimised parts by SLM. First, three process variables, i.e. laser power, scan speed and hatching distance, are studied considering their effect on hardness. Design of Experiments and Response Surface Methodology are exploited to achieve hardness optimisation by controlling process variables. Then, hardness defect probability is estimated by composing the uncertainty affecting both process variables and their relationship with the hardness. The overall procedure is applied to AlSi10Mg alloy, which is relevant for both aerospace and automotive applications. The approach this study proposes may be of assistance to inspection designers to effectively and efficiently set up quality inspections in early design phases of inspection planning.

Performance measurement for offline inspections under variable interactions and inspection errors in low-volume production

“…Regarding in-process inspections, some studies have proposed methods to design an economical in-process control procedure, supporting the choice of the best sampling strategy for low-volume productions [18,19]. Another line of research has focused on the development of suitable defect prediction models for low-volume manufacturing processes and their use to plan quality inspection strategies [20][21][22][23][24][25][26][27]. Also with regard to offline inspections, some studies aimed to develop probabilistic models for predicting defects and define adequate performance indicators outlining the overall effectiveness and affordability of alternative offline inspection strategies [26,27].…”

“…Another line of research has focused on the development of suitable defect prediction models for low-volume manufacturing processes and their use to plan quality inspection strategies [20][21][22][23][24][25][26][27]. Also with regard to offline inspections, some studies aimed to develop probabilistic models for predicting defects and define adequate performance indicators outlining the overall effectiveness and affordability of alternative offline inspection strategies [26,27]. Despite this general interest, previous studies concerning offline inspections were based on the hypothesis of no interaction between process and inspection variables.…”

Inspection planning by defect prediction models and inspection strategy maps

“…Overall, for each inspection strategy to be assessed and compared, the two performance indicators may be calculated by Eqs. (8) and (9). According to the scientific literature about Multi-Criteria Decision-Making (MCDM), several methods may be implemented to choose from different alternatives when multiple criteria and trade-offs are involved [56][57][58][59].…”

“…Defects in the final product, particularly those generated during the production process, can significantly affect the product itself, both in terms of quality and cost [1][2][3]. In this regard, designing effective and cost-efficient inspection strategies for the detection of defects and the reduction of quality-related costs has always been a great challenge and a crucial factor for achieving market competitiveness [4][5][6][7][8].…”

Quality Monitoring of Assembled Products by Defect Prediction Models

“…Most of them refer to mass productions, involving hundreds or thousands of pieces produced per month [10-15, 17, 19]. Recently, the authors focused on identifying appropriate defect prediction models in assembly processes [9,[21][22][23][24] according to the product structural complexity paradigm proposed by Alkan [25] and Sinha [26]. The same model was also recently adopted to help inspection designers in the inspection process planning from the early design phases [9].…”