Högskolan i Skövde

Improving energy efficiency in industrial manufacturing remains challenging despite substantial technical potential. This has resulted in a persistent energy efficiency gap, which is increasingly understood as a socio-technical issue driven by not only technology limitations but also organizational and informational barriers. This study investigates how energy waste is targeted in practice through an in-depth single case study of an automotive company. Fifteen energy efficiency measures (EEMs) were analyzed and classified by type of energy waste addressed, digital technologies applied, and organizational knowledge required. The results show that industrial efforts primarily focus on reducing idling energy losses, while fewer measures address more complex forms of energy waste, such as over-processing losses. Digital technologies are mainly applied and rolled out at lower maturity levels, emphasizing energy monitoring and visualization. Further, different types of organizational knowledge are associated with targeting energy waste: technical knowledge dominates isolated interventions, process knowledge supports standardized technology diffusion, and leadership knowledge is required for cross-functional coordination. The findings highlight that bridging the energy efficiency gap requires the alignment of technological solutions with organizational knowledge and routines. This study contributes empirical insights into how manufacturing companies can structure and prioritize energy efficiency efforts and provides a framework to support the implementation of energy efficiency measures in practice.

This paper presents an end-to-end generative artificial intelligence (Gen-AI) framework for automating the generation, adaptation, and evaluation of discrete-event simulation (DES) models in manufacturing. The approach integrates multiple large language models (LLMs) with a structured blueprint model and targeted human-in-the-loop controls to create executable simulation models from heterogeneous production data, implement targeted modifications, and interpret simulation outcomes. The workflow incorporates prompt engineering, zero- and one-shot implementations, and evaluator–optimizer loops. 21 experimental runs on two industrial case studies from a Swedish automotive manufacturer demonstrate that LLMs can support DES model generation and scenario exploration through a hybrid approach combining automation with human oversight. The results underline both the potential and current limitations of LLM-driven simulation, particularly regarding output consistency and generalizability. Future research should extend the method to more complex manufacturing systems and investigate the role of emerging autonomous Gen-AI tools in simulation-based decision support.

Traditional methods of data visualization and process monitoring are increasingly inadequate in fast-paced,data-intensive manufacturing environments. Extended Reality (XR) technologies, including Augmented Reality(AR), Virtual Reality (VR), and Mixed Reality (MR), have the potential to enhance human–machine inter-action and operational efficiency in Industry 4.0 framework. While previous research has demonstrated theeffectiveness of XR in areas such as assembly, training, maintenance, and human–robot interaction, limitedattention has been given to developing and evaluating XR systems for real-time machine data visualization.Most existing studies focus on demonstrating AR applications without rigorous comparative evaluations againstother XR technologies or traditional Human–Machine Interfaces (HMIs), often with limited user testing. Thisstudy addresses these gaps by developing and evaluating an XR application using Microsoft HoloLens 2 for real-time process control in a Learning Factory environment. A mixed-methods approach, including experimentaldesign, surveys, and time measurements, compared the XR system with conventional 2D HMIs. Data from22 participants were analyzed, focusing on alarm response times, usability, and preventive maintenance.The findings show that the XR system significantly improves alarm response times, increases frequencyof preventive refills, and enhances usability compared to traditional HMIs. However, challenges related toergonomics and limited field of view were noted. This study contributes to advancing smart manufacturing byshowcasing the potential of XR to improve human–machine interfaces and foster better interaction betweenmachines and operators.

Usability is a key factor for successfully integrating new technology to aid an operator in production. It is measured using three metrics: efficiency (productivity), effectiveness (quality), and user satisfaction. One prominent technology for operator support is augmented reality (AR), which is mostly handheld or head-mounted. A human-centered approach is required to align the AR integration with the operator’s capabilities. The underlying use case in this study is an energy dashboard visualized using AR and non-AR media, namely, a monitor, tablet, and HoloLens. The resulting media applications were evaluated for usability in terms of efficiency, effectiveness, and satisfaction in the within-study experiments by 16 participants. Overall, the results showed increased efficiency and satisfaction for traditional-monitor users and increased effectiveness for tablet users. Despite the participants’ lack of experience with AR, the AR applications performed comparably to the monitor and even slightly better in some aspects. With the ongoing development of AR software and hardware, AR can become increasingly useful for machine monitoring in production. However, to use AR for more comprehensive tasks, its strengths and weaknesses must be considered. 

Understanding delay conditions and making accurate predictions are essential for optimizing turnaround and taxi times, which in turn reduces fuel consumption and lowers CO2 emissions in airport operations. However, while existing research has explored the impact of various prediction models on airport operations, it often overlooks the performance of Collaborative Decision Making (CDM) variables when discussing delay conditions. The implementation of CDM at major European airports has led to a milestone-based approach within airport operations, particularly in the turnaround operations, segmenting these operations with unique features. The purpose of this paper is to systematically investigate the efficacy of various machine learning techniques, such as linear regression, regression trees, random forests, elastic nets, and multi-layer perceptrons (MLP), in accurately predicting delay categories within the CDM framework. For this purpose, we analyzed CDM operational data from Madrid Airport, with at least 166,185 flight observations. Our findings illustrate a training methodology on how different models vary in prediction accuracy when applied to CDM operational data. We applied the SHAP (SHapley Additive exPlanations) method for feature importance analysis of all our independent variables to interpret the output of our machine learning models. Our results indicate that linear regression and elastic nets are the most effective machine learning models for achieving high prediction accuracy within the CDM framework. To test their robustness, we extended the analysis with predictions for better schedule times for taxi times on arrival and depature for selected runways using a different dataset. Our results contribute by showcasing a training methodology, highlighting how elastic net model as the best-performing model can be adopted for turnaround operations. In conclusion, we discuss the implications of our results for runway demand policies and use of airport resources such as gate & runaway allocation. 

Rising energy costs, energy supply uncertainties, and the sustainability crisis have intensified the need for energy efficiency in industrial manufacturing. This adds complexity to balancing traditional production goals such as productivity, quality, and cost. While prior studies address energy-intensive processes or throughput bottlenecks, they often lack integrated decision-support for evaluating optimal trade-offs. To address this gap, this study proposes a novel simulation-based multi-objective optimization framework combined with a knowledge discovery module, demonstrated in an industrial case study. The framework systematically identifies energy and productivity losses, evaluates improvement strategies to determine optimal trade-off solutions, and extracts actionable rules to guide decision making. Case study results show a 23.9% reduction in specific energy consumption and a 27.9% increase in throughput, while emphasizing the need to balance inventory levels. The approach offers a robust, data-driven method for supporting energy-efficient manufacturing. Future research will explore integration with real-time monitoring and extension to additional objectives such as costs and emissions.

The formulation and selection of parameters and sequences in crankshaft production present challenges that are both demanding and time-intensive. This study introduces an innovative approach to intelligent process planning in crankshaft machining lines using multi-turret machines. Emphasis is placed on automating process planning through multi-objective optimization of critical decisions such as process parameters, operation sequencing, and tool positioning on turret magazines. The principal objectives addressed include minimizing machining and non-machining time, reducing costs by optimizing tool life, and enhancing product quality through optimal surface roughness. By automating these decision points, the proposed framework reduces manual intervention and aligns with Industry 4.0 goals for adaptive, data-driven manufacturing. Additionally, we discuss the potential future incorporation of artificial intelligence agents to dynamically refine parameters and enable adaptive planning.

Enhancing data visualization on the shop floor provides support for dealing with the increasing complexity of production and the need for progressing towards emerging goals like energy efficiency. It enables personnel to make informed decisions based on real-time data displayed on user-friendly interfaces. Augmented reality (AR) technology provides a promising solution to this problem by allowing for the visualization of data in a more immersive and interactive way. The aim of this study is to present a framework to visualize live and historic data about energy consumption in AR, using Power BI and Unity, and discuss the applications' capabilities. The study demonstrated that both Power BI and Unity can effectively visualize near-real-time machine data with the aid of appropriate data pipelines. While both applications have their respective strengths and limitations, they can support informed decision-making and proactive measures to improve energy utilization. Additional research is needed to examine the correlation between energy consumption and production dynamics, as well as to assess the user-friendliness of the data presentation for effective decision-making support. 

Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices. 

This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.