1,721,050 research outputs found
Machine learning-based predictive maintenance: empirical insights of challenges and countermeasures
No full textPredictive Maintenance (PdM) has gained attention to reduce production-related costs and downtime, with Machine Learning (ML) emerging as a prominent technique. However, ML benefits are often achieved using laboratory or reference datasets. These may differ from real-world industrial data, raising doubts about ML applicability in real-world settings. This work addresses this issue, showing that ML adoption for PdM in industry is low. Furthermore, using a Delphi study, key challenges hindering ML adoption are identified and prioritised. Interestingly, some relevant challenges (e.g. the need for training employees) are overlooked by the literature. Furthermore, to boost PdM adoption, we identified and prioritized potential countermeasures based on practitioner insights. It emerged that some countermeasures can tackle multiple challenges (e.g. training programs). Our findings benefit both scholars and practitioners. Scholars may focus on relevant challenges to facilitate ML adoption for PdM. Practitioners are provided with a set of effective countermeasures to cope with relevant challenges
Conventional or additive manufacturing for spare parts management: An extensive comparison for Poisson demand
Full text linkDue to the main peculiarities of spare parts, i.e. intermittent demands, long procurement lead times and high downtime costs when the parts are not available on time, it is often difficult to find the optimal inventory level. Recently, Additive Manufacturing (AM) has emerged as a promising technique to improve spare parts inventory management thanks to a ‘print on demand’ approach.
So far, however, the impact of AM on spare parts inventory management has been little considered, and it is not yet clear when the use of AM for spare parts inventory management would provide benefits over Conventional Manufacturing (CM) techniques.
With this paper we thus aim to contribute to the field of AM spare parts inventory management by developing decision trees that can be of support to managers and practitioners.
To this aim, we considered a Poisson-based inventory management system and we carried out a parametrical analysis considering different part sizes and complexity, backorder costs and part consumption. Moreover, we evaluated scenarios where the order-up-to level is limited to resemble applications with a limited storage capacity.
For the first time, the analysis was not limited to just one AM and one CM technique, but several AM and CM techniques were considered, also combined with different post-process treatments, for a total of nine different sourcing alternatives. In addition, the economic and technical performance of the different sourcing options were obtained thanks to an interdisciplinary approach, where experts from production economics and material science were brought together
Including mechanical requirements in a bi-objective nesting and scheduling model for additive manufacturing
No full textFollowing the increasing relevance of Additive Manufacturing (AM) as Manufacturing-as-a-service (Maas), the AM scheduling (and related nesting) problem has been increasingly investigated. Due to their business nature, Maas companies are interested in minimizing both the makespan and the total tardiness; however, most of the literature focuses only on one of them. This work fills this gap proposing a mixed-integer linear programming (MILP) model that minimizes both makespan and total tardiness. In doing so, for the first time in the literature, considerations on parts’ strength are included. During nesting procedures, indeed, parts can be oriented in different ways, with this choice affecting not only the total processing time (as considered by the literature) but also the strength achievable: if this is lower than what planned, parts might fail unexpectedly with detrimental consequences. Thus, this work ensures that parts are produced with the required strength. In doing so, we focus on a parallel unrelated AM batch scheduling problem for metallic parts.
Considering the multi-objective and NP-hard nature of the problem, an ε-constraint algorithm and a non-dominated sorting genetic algorithm-II (NSGA-II) are developed to solve the problem. Four different problem-specific decoding mechanisms are integrated into the NSGA-II to improve its search capability and solution-building performance. Their performances are evaluated through computational experiments, showing that the integrated mechanisms improve the performance of the NSGA-II. Finally, through numerical instances and analysis of the super Pareto front, we derive managerial insights on the impact of strength requirements and machines’ number and features on the objectives
A data-driven methodology for the periodic review of spare parts supply chain configurations
Full text linkConfiguring supply chains (SCs) is critical to spare parts retailers' success, entailing two key aspects: stock deployment into distribution centres (DCs) (i.e. inventory centralisation or decentralisation) and stock supply in each DC (how many spare parts to supply and how often). Given the unpredictability of spare parts demand, stock deployment and supply policies should be regularly reviewed, adapting to fluctuations in customer needs. A viable way to do this is to adopt a multi-criteria ABC criticality classification. However, the multi-criteria ABC criticality classification has often been used to plan stock supply policies in a single DC, but only once to plan spare parts deployment. Nevertheless, the available literature methodology presents major limitations, being not applicable in real companies. Therefore, this paper provides a novel methodology, called SP-LACE, which first reviews the configuration of spare parts SCs based on a multi-criteria criticality classification. Then, allows, for the first time, to evaluate the economic benefits of the reviewed SC configuration. SP-LACE was tested on two case studies and compared with the literature methodology. The results indicate that it provides economic benefits (in terms of total SC cost), overcoming the limitations of the literature methodology and ensuring high service levels
AZ31 Mg Alloy Processed by Equal Channel Angular Pressing for Bio-Medical Applications: The Role of Microstructure on SCC
No full textAdditive vs conventional manufacturing for producing complex systems: A decision support system and the impact of electricity prices and raw materials availability
Full text linkRecently, Additive Manufacturing (AM) has gained wide interest in the manufacturing sector, especially among original equipment manufacturers (OEMs). OEMs are interested in switching from conventional manufacturing (CM) technologies to AM to produce parts of their complex systems. However, this decision is not straightforward since the benefits of AM needs to be weighed against its drawbacks, and this requires OEMs to consider the whole system lifecycle. However, this is currently missing in the literature, and this work aims to fill this gap. Indeed, this work investigates when AM should be preferred over CM considering all the lifecycle phases into a cost objective function to minimize. Moreover, this work considers how changes in electricity prices and raw materials availabilities due to global events (e.g. pandemics, wars, ...) affects the decision on the optimal production technology (AM or CM). To do so, four situations in terms of electricity prices and raw materials availabilities have been considered, and a decision tree has been developed per each of them. The decision tree represents a decision support system that supports the identification of whether producing in AM is economically convenient over CM or not. Finally, the decision trees have then been applied to two case studies
Semiautomatic rapid upper limb assessment methods: validation of AzKRULA
No full textIn the Industry 5.0 era, optimising working posture is crucial to reduce musculoskeletal disorder risks. Rapid upper limb assessment (RULA) is a common evaluation method, but traditional approaches are often subjective, and wearable sensors can be costly and intrusive. Optical sensors offer a more practical alternative for industrial environments. This study compares the effectiveness of an in-house application, AzKRULA based on Microsoft Azure Kinect, with Siemens Jack Tat Suit software for RULA assessment. We evaluated 15 static postures with both AzKRULA and the Jack Tat Suit software, using expert assessments as a reference. The results showed a high level of agreement between AzKRULA, expert evaluations, and the commercial software, highlighting AzKRULA as a cost-effective, rapid tool for ergonomic assessment. Thus, AzKRULA can support ergonomists and health and safety managers in assessing upper-body ergonomic risks in repetitive tasks
Relating stress corrosion cracking behavior to microstructural and surface properties of biocompatible AZ31 alloy
Full text linkMagnesium (Mg) and its alloys have attracted significant attention as temporary implant materials due to their excellent biocompatibility with human physiology. In fact, Mg is essential to the human metabolism as a cofactor for many enzymes and Mg ions are well-known to facilitate tissue-healing. In addition, the mechanical properties (density, elastic modulus, yield strength and ultimate tensile strength) of Mg and its alloys resemble those of natural bone reducing the risk of the stress-shielding-related problems observed with other metallic implant materials such as stainless steel, titanium and Co-Cr alloys. However, despite their high potential, Mg and its alloys are not yet utilized in biomedical applications. This is due to the (1) rapid corrosion and degradation in the human body that leads to a loss of mechanical integrity before tissues have sufficient time to heal, (2) the evolution of hydrogen as corrosion product accompanied by hydrogen pocket formation that hampers healing or even cause the death of patients through the blockage of the blood stream and (3) the sudden fracture of implants due to the simultaneous action of the corrosive human-body-fluid and mechanical loads through corrosion-assisted cracking phenomena (stress corrosion cracking (SCC) and corrosion fatigue (CF)).
In the past years, several approaches have been developed to improve the corrosion resistance of Mg and its alloys. These approaches can be divided into two main groups, one characterized by the modification of the bulk and the other by the modification of the surface. Among the former, Severe Plastic Deformation (SPD) techniques, such as Equal Channel Angular Pressing (ECAP), have attracted attention as possibility for inducing a very fine and homogeneous microstructure throughout all the samples. The latter group relies on surface modifications obtained by mechanical processing (e.g. cryogenic machining) or by the protection through coatings deposited by various techniques (e.g. sputter and Atomic Layer Deposition (ALD)). However, the assessment of the effectiveness of the different approaches in improving the resistance of Mg and its alloys to corrosion-assisted cracking phenomena is still underexplored.
In an attempt to understand the fundamental mechanisms linking the microstructural and surface properties to the SCC susceptibility, this thesis investigates how selected procedures initially intended for improving the corrosion resistance of Mg and its alloys impact the SCC susceptibility of AZ31 alloys in Simulated Body Fluid (SBF) at 37 °C. The procedures selected from an extensive literature review investigating the different procedures used to improve the corrosion behavior and the mechanisms regulating the SCC phenomenon were ECAP, cryogenic machining and coatings obtained by means of ALD.
1, 2 and 4 passes of ECAP were carried out on an AZ31 alloy and samples subjected to one pass of ECAP have been shown to be less susceptible to SCC compared to the material in the as-received condition (the elongation to failure was increased by 150%) due to the improved corrosion resistance as a consequence of a reduced grain size. The reduced SCC susceptibility after one pass of ECAP was also confirmed by the morphology of the fracture surfaces that reveals an increased ductility compared to the as-received material. However further ECAP processing (2 and 4 passes) are reported to worsen the SCC susceptibility due to an increased brittleness of the material as a consequence of an increased amount of hydrogen evolved. This is due to the unfavorable texture evolution, as confirmed by the mechanical characterization (tensile tests and hardness measurements).
AZ31 samples were machined under cryogenic cooling and afterwards subjected to Slow Strain Rate Tests (SSRTs) at a strain rate of 3.5·10-6 s-1 to evaluate the SCC susceptibility. Cryogenic machined samples were characterized by lower SCC susceptibility than dry cut samples (the elongation to failure was increased by 28%) as a consequence of their improved corrosion performances due to the presence of a wider nanocrystalline layer, resulting in a faster formation of passivating surface oxides, and to the presence of compressive residual stresses instead of tensile.
Being ALD a recently developed technique still underexplored in terms of corrosion and biological properties, it was compared to sputter technique in terms of corrosion protectiveness and the induced biocompatibility of three different coatings were evaluated. The ALD technique has been shown to provide the better corrosion protection (assessed by means of potentiodynamic polarization curves and hydrogen evolution experiments) both in case of smooth and rough surfaces due to an increased surface integrity (observed by SEM and XPS analyses). In addition, in the case of 3D porous structures, the improvements provided by the ALD technique were even higher as a consequence of the line-of-sight limitation of sputtering (confirmed by means of SEM analyses). In addition, the biocompatibility of TiO2, ZrO2 and HfO2 coatings obtained by means of ALD have been investigated by means of MTS assay on L929 cells and the HfO2 coatings were shown to provide the best biocompatibility due to the highest corrosion resistance. This can be reasoned by their lower wettability and their higher electrochemical stability and surface integrity (in terms of cracks and pores). TiO2, though generally considered a biocompatible coating, was found to provide the lowest improvements in terms of corrosion resistance and cell viability. Interestingly, TiO2 coatings are characterized by grade 3 cytotoxicity after 5 days of culture due to their high corrosion rate, which does not meet the demands for cellular applications. These results indicate the strong link between biocompatibility and corrosion protection and signify the need of considering the latter when choosing a biocompatible coating to protect temporary Mg based alloys before implantation.
Finally, the SCC susceptibility of TiO2 and ZrO2 ALDed coated AZ31 alloys have been evaluated and the ZrO2 coated samples were reported to have the lowest SCC susceptibility. In fact, the elongation to failure of the TiO2 coated samples were increased by 125% and that of ZrO2 coated samples by 220%. The different SCC susceptibility was attributed to the improved corrosion of the ZrO2 coated samples compared to the TiO2 coated samples as a consequence of four main aspects, i.e. different cohesive energies, different wettability, different defect densities and sizes and different mechanical properties
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