The paper describes the use of immersive technologies and Digital Twins in the architecture, engineering, and construction (AEC) industry. Immersive technologies have immense potential to improve interdisciplinary collaboration as they utilize a visual representation of virtual objects in their real-world perspective. This immersion facilitates effective communication between all parties involved, e.g. to identify errors during the planning and construction of buildings. A specific example of such interdisciplinary collaboration is kitchen planning. The architect draws the house and room plans, the kitchen planner plans the kitchen according to the homeowner’s wishes and the craftsmen then carry out their installations according to the plans for electricity, water, and heating. All plans are based on flat floors and perpendicular walls. However, this is rarely the case in a real building. The challenge with the exact visual representation of virtual objects in the real world using AR therefore lies in the positioning of objects such as a cupboard in relation to the non-orthogonal coordinate systems that result for each room from the real floors and walls. The paper presents an assistance system that integrates immersive technologies and Digital Twins. It is a solution approach for the transformation of object properties based on orthogonal coordinate systems into the non-orthogonal coordinate systems prevalent in reality. With the assistance system, the kitchen planner can create his plans using virtual reality (VR), customers can experience their kitchen virtually and the craftsmen can then be guided using augmented reality (AR), e.g. by precisely localising drilling positions for cabinets.
This study introduces a human-centered and transdisciplinary engineering-centered “4D” design model, comprising Discovering Service Touchpoints, Defining Service Sore Points, Depicting User Experience, and Design Idea Development phases, which are based on service encounter discovery and peal-end rule. The systematic travel experiment involving 30 volunteers across six city tours was conducted to analyze user behaviors and needs. Emotional change curves are utilized to optimize peak and end of travel experiences. The findings validate the 4D model for redefining service encounters and leveraging the peak-end rule for the improvement of public transport services.
Snowmaking and snow storing are increasingly used as climate adaptation strategies in ski resorts all over the world, including in the Arctic. While the decrease of the number of snow cover days is slower than in the Alps, snow security is decreasing particularly at the beginning of the skiing season in October-November. As there is up to 30-times difference between minimum and optimal conditions in the energy and water consumption in snowmaking, it makes sense to optimize the timing of snowmaking to ensure that snowmaking will not turn into maladaptation. Climate services are user-friendly ways of providing relevant climate information for end-users. Our team co-designed a climate service prototype for winter tourism centers in Northern Finland in 2017-2020 by a transdisciplinary co-design process involving climate science, modelling, tourism research, and practitioners including snowmaking professionals and environmental experts from a pilot enterprise. The versatile co-design methods utilized included e.g. visual methods and workshops, and co-evaluation of the prototype. The resulting climate service prototype SnowApp provides a reliable 4-week forecast on snowmaking conditions and hence it is a decision-support tool for ski resort management. The prototype is applicable in other geographical locations, too, and for other snow dependent businesses.
Predicting product quality is a crucial element in smart manufacturing. Then, the development of robust learning models is essential based on historical data relevant to their respective products and production processes. The challenge of quality prediction models lies in the limited availability of production batches and their associated historical data in highly customized products like large power transformers, precision machine tools, and other industrial equipment. Despite the small dataset, ensuring the quality of the final product remains paramount. This study introduces an innovative transfer learning approach integrating adaptive machine learning and non-linear regression. The goal is to accurately predict the quality of highly customized products using datasets limited by the constraints of the primary suppliers with smaller production scales. The research employs a case study and dataset from the production of large power transformers. The input data for training and testing the predictive model include key power transformer parameters, such as core loss values and power loss. The approach utilizes transfer learning that transfers knowledge gained from one task (e.g., predicting the quality outcome of 50 kVA transformers) to a similar task (e.g., predicting the quality outcomes of 25 kVA and 100 kVA transformers). The proposed method enhances the model’s performance and generalization capabilities. Subsequently, the model is fine-tuned rapidly without compromising accuracy. This paper contributes to a comparative analysis with previous research, demonstrating the effectiveness and superiority of the proposed method. Manufacturers can leverage this approach to predict the quality of complex, small-batch, and highly customized industrial products. Ultimately, this method aids in improving production quality and reducing costs.
The dominant transport mode for school journeys is safe, comfortable and affordable private automobiles. The transport system consists of well-engineered roads. Carers know that the car is usually the safest and most convenient option for the school run. Traffic congestion, air pollution, loss of exercise and increased risk to pedestrians and cyclists are thoroughly researched externality costs of the automobile school run. Net zero emission targets impose additional tensions for schools and local authorities to make rapid changes. Travel demand management research has been aiming to keep motorised vehicles away from schools, and to encourage physically active modes for the school run. But changing the infrastructure, travel behaviour and culture of a school community presents a wicked problem that requires transdisciplinary systems approaches from engineering and social science. This article reports on the Transition Engineering co-design process with incumbent stakeholders. The result is a novel learning and teaching programme which also achieved safe and sustainable school transport. The programme enables students to engineer the changes they require, communicate their needs to decision makers, and achieve net zero with fair provision. The school culture shifted to finding their own way to net zero school transport, contributing to wider community travel demand management efforts. The protocol of the design process, the co-design results, and a reflection on the preliminary stakeholder feedback are presented. The article addresses the wider challenge of how transdisciplinary transition engineering can deliver safety and sustainability of incumbent engineered systems, while navigating real-world social and economic dynamics.
