Planning offline inspection strategies in low-volume manufacturing processes

Verna, Elisa; Genta, Gianfranco; Galetto, Maurizio; Franceschini, Fiorenzo

doi:10.1080/08982112.2020.1739309

Cited by 25 publications

(37 citation statements)

References 49 publications

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“…Laser additive manufacturing methods have been successfully developed for the last 30 years [1][2][3]; however, the parts' final quality is often under the quality threshold required by the industry level [4][5][6][7][8] (anisotropy of properties, natural profile waviness, surface roughness parameter R a higher than 6.3 µm, trapped unmelted granules, side effect) despite the evident advantages [9,10], especially concerning the production of the functional wear-resistant responsible products for more complex mechanisms for the needs of the transport, aircraft, and aerospace industries [11,12]. Another problem is the postprocessing of the obtained surfaces with a complex character and internal cavities that are mostly required for the parts produced by additive manufacturing [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Influence of Postprocessing on Wear Resistance of Aerospace Steel Parts Produced by Laser Powder Bed Fusion

et al. 2020

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The paper is devoted to the research of the effect of ultrasonic postprocessing—specifically, the effects of ultrasonic cavitation-abrasive finishing, ultrasonic plastic deformation, and vibration tumbling on surface quality, wear resistance, and the ability of real aircraft parts with complex geometries and with sizes less than and more than 100 mm to work in exploitation conditions. The parts were produced by laser powder bed fusion from two types of anticorrosion steels of austenitic and martensitic grades—20Kh13 (DIN 1.4021, X20Cr13, AISI 420) and 12Kh18N9T (DIN 1.4541, X10CrNiTi18-10, AISI 321). The finishing technologies based on mechanical action—plastic deformation, abrasive wear, and complex mechanolysis showed an effect on reducing the submicron surface roughness, removing the trapped powder granules from the manufactured functional surfaces and their wear resistance. The tests were completed by proving resistance of the produced parts to exploitation conditions—vibration fatigue and corrosion in salt fog. The roughness arithmetic mean deviation Ra was improved by 50–52% after cavitation-abrasive finishing, by 28–30% after ultrasonic plastic deformation, and by 65–70% after vibratory tumbling. The effect on wear resistance is correlated with the improved roughness. The effect of used techniques on resistance to abrasive wear was explained and grounded.

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Section: Introductionmentioning

confidence: 99%

Influence of Postprocessing on Wear Resistance of Aerospace Steel Parts Produced by Laser Powder Bed Fusion

et al. 2020

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“…Existing MVI solutions are therefore often delimited; this includes limiting quality control based on material properties [33] or constraining the problem to individual product types in a controlled setup with fixed object placement and illumination [34]. Consequently, automatic quality inspection is lacking solutions for low volume high quality manufacturing due to the lack of data and the inadequacy of traditional statistical approaches [35]. In general comprehensive data sets are challenging to obtain due to the randomness and uniqueness of the extensive number of different defect appearances [33].…”

Section: Automatic Visual Inspectionmentioning

confidence: 99%

Defect or Design? Leveraging the Angle of Opportunity for Detecting Scratches on Brushed Aluminium Surfaces

2021

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Design features such as polishing strokes share similarities with defects; this makes defect detection and quality assessment difficult to perform both manually and automatically. Human assessors rotate objects to probe different incoming illumination angles and evaluate the defect dimension to limits samples i.e. decide whether differences between defect candidates and design features qualify as a defect. This process has poor access to quantifiable defect descriptors needed for automation and expose a gap in the existing evaluation of defects. To integrate this notion into automated defect detection we propose a spatio-temporal image acquisition setup capturing the defect descriptor Angle of Opportunity (AoO) which can be used as a feature for image-based classification. The Random Forest approach classified defects with an area under the ROC-curve of 92%.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

“…In in-process inspections, units are inspected during the production process [10][11][12][13], while in offline inspections, finished products are inspected after the production process is completed [10,14]. Although in-process inspections are considered more economical and effective than offline inspections, in some situations, they are impossible to perform, not adequate or not affordable [7,10].…”

Section: Introductionmentioning

confidence: 99%

“…In literature, few studies investigated the design of offline inspection strategies which can be also suitable for low-volume manufacturing productions. In a recent study, a methodology to select the best compromise between the effectiveness and the affordability of alternative offline inspection strategies in a low-volume Additive Manufacturing (AM) production was proposed [7]. Regarding the design of in-process inspections, existing methods rely on the decomposition of the manufacturing process of interest into several steps, in which specific defects can occur [4,29,30].…”

Section: Introductionmentioning

confidence: 99%

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Inspection planning by defect prediction models and inspection strategy maps

Verna

Genta

Galetto

et al. 2021

Prod. Eng. Res. Devel.

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Designing appropriate quality-inspections in manufacturing processes has always been a challenge to maintain competitiveness in the market. Recent studies have been focused on the design of appropriate in-process inspection strategies for assembly processes based on probabilistic models. Despite this general interest, a practical tool allowing for the assessment of the adequacy of alternative inspection strategies is still lacking. This paper proposes a general framework to assess the effectiveness and cost of inspection strategies. In detail, defect probabilities obtained by prediction models and inspection variables are combined to define a pair of indicators for developing an inspection strategy map. Such a map acts as an analysis tool, enabling positioning assessment and benchmarking of the strategies adopted by manufacturing companies, but also as a design tool to achieve the desired targets. The approach can assist designers of manufacturing processes, and particularly low-volume productions, in the early stages of inspection planning.

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Planning offline inspection strategies in low-volume manufacturing processes

Cited by 25 publications

References 49 publications

Influence of Postprocessing on Wear Resistance of Aerospace Steel Parts Produced by Laser Powder Bed Fusion

Influence of Postprocessing on Wear Resistance of Aerospace Steel Parts Produced by Laser Powder Bed Fusion

Defect or Design? Leveraging the Angle of Opportunity for Detecting Scratches on Brushed Aluminium Surfaces

Inspection planning by defect prediction models and inspection strategy maps

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