Export shipments arriving late at the freight building of KLM Cargo at Schiphol Airport is a trigger to deviations in the standard acceptance process. These Late Shows are currently handled ad-hoc making it difficult to plan and predict these events. In addition, shipments arriving on time is currently not a criterion for acceptance, while a shipment should depart on the flight planned at the moment of acceptance or the quality of the process deteriorates. By conducting a data analysis to quantitatively identify the characteristics of the Late Shows, and by conducting stakeholder interviews to understand the current process and discuss the future process, this research tried to design the operational process of the Late Shows to improve the operational excellence and quality of the acceptance process. The research shows that currently, late shipments are often still tried to be build up for the planned flight. It is found that 13% of these shipments do eventually not depart on the planned flight, while being accepted by KLM Cargo, deteriorating the quality of the process. The research concludes that the design of the Late Show process should include a check on whether the shipment was delivered on time, before acceptance of the shipment. By only accepting the shipment once it is decided that the planned flight is achievable or when it is rebooked to another flight, it is assured that the Late Show will be on time at the buildup buffer for the booked flight.
As a logical consequence of the advancements in automation of production of composite aircraft structures, more attention is paid to the automation of maintenance. Current repair procedures involve manual labour and exposure to harmful particles (such as dust, vapours) while final quality and evidencing depends largely on the skills of repair technicians. The current study aims to automate composite repair procedures for the aviation sector with the objective to counter these disadvantages. Main research question: ‘What is required for a robot system to assist in composite repairs’This research is part of a larger, SIA-RAAK funded project FIXAR, running in three Universities of Applied Sciences in the Netherlands and a cluster of knowledge institutions and industry partners.In the repair process of aircraft structures, repair by means of scarf or lap joints is common practice. First paint layers must be removed to inspect the area and prepare for further repair. Then damaged material is removed. Material is replaced and the repair is finished and painted. Tasks within the repair process that are considered dull or harmful are sanding and material removal. Current investigation focussed on automation of these tasks.
De technische en economische levensduur van auto’s verschilt. Een goed onderhouden auto met dieselmotor uit het bouwjaar 2000 kan technisch perfect functioneren. De economische levensduur van diezelfde auto is echter beperkt bij introductie van strenge milieuzones. Bij de introductie en verplichtstelling van geavanceerde rijtaakondersteunende systemen (ADAS) zien we iets soortgelijks. Hoewel de auto technisch gezien goed functioneert kunnen verouderde software, algorithmes en sensoren leiden tot een beperkte levensduur van de gehele auto. Voorbeelden: - Jeep gehackt: verouderde veiligheidsprotocollen in de software en hardware beperkten de economische levensduur. - Actieve Cruise Control: sensoren/radars van verouderde systemen leiden tot beperkte functionaliteit en gebruikersacceptatie. - Tesla: bij bestaande auto’s worden verouderde sensoren uitgeschakeld waardoor functies uitvallen. In 2019 heeft de EU een verplichting opgelegd aan automobielfabrikanten om 20 nieuwe ADAS in te bouwen in nieuw te ontwikkelen auto’s, ongeacht prijsklasse. De mate waarin deze ADAS de economische levensduur van de auto beperkt is echter nog onvoldoende onderzocht. In deze KIEM wordt dit onderzocht en wordt tevens de parallel getrokken met de mobiele telefonie; beide maken gebruik van moderne sensoren en software. We vergelijken ontwerpeisen van telefoons (levensduur van gemiddeld 2,5 jaar) met de eisen aan moderne ADAS met dezelfde sensoren (levensduur tot 20 jaar). De centrale vraag luidt daarom: Wat is de mogelijke impact van veroudering van ADAS op de economische levensduur van voertuigen en welke lessen kunnen we leren uit de onderliggende ontwerpprincipes van ADAS en Smartphones? De vraag wordt beantwoord door (i) literatuuronderzoek naar de veroudering van ADAS (ii) Interviews met ontwerpers van ADAS, leveranciers van retro-fit systemen en ontwerpers van mobiele telefoons en (iii) vergelijkend rij-onderzoek naar het functioneren van ADAS in auto’s van verschillende leeftijd en prijsklassen.
Omdat de composieten industrie een zeer snel groeiende industrie is, is de vraag naar kosteneffectieve onderhoudsmethoden groeiende. Deze toenemende vraag kan beantwoord worden met behulp van geautomatiseerde composieten reparatie. Het idee is om een robot arm uit te rusten met defect-detectie-systeem en een frees om een gevonden defect uit het materiaal te frezen. Dit idee is gebaseerd op een eerder onderzoek wat is uitgevoerd bij Inholland met als onderwerp het automatisch verwijderen van materiaal met een robot (RAAK2014-1-26M). Het commercieel potentieel is groot aangezien weinig tot geen van deze automatische reparaties worden aangeboden en de vraag steeds groter wordt aangezien composieten steeds meer worden toegepast. Na het onderzoek zal al dan niet een octrooi aanvraag worden verricht om vervolgens het onderzoek te publiceren. De doelstelling is het inzicht verkrijgen in de economische en technische haalbaarheid van dit product. Deze twee onderwerpen zullen worden onderzocht door twee afstudeer stagiaires en begeleid worden door Ruben van den Brink. Daarnaast kunnen een aantal deskundigen aanwezig in het laboratorium van Inholland Composites ook geraadpleegd worden. Hier is specialistische vakkennis aanwezig waarmee eventuele risico’s op expertise-tekort worden gemitigeerd.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
