Automotive robotic body shop simulation for performance improvement using plant feedback

The paper presents a new game theoretic approach towards the validation of discrete event air combat simulation models. In the approach, statistical techniques are applied for estimating game models based on simulation data. The estimation procedure is presented in cases involving games with both discrete and continuous decision variables. The validity of the simulation model is assessed by comparing the properties of the estimated games to actual practices in air combat. The approach enhances existing methods for the validation of discrete event simulation models by incorporating the inherent game setting of air combat into the analysis. The estimated games also provide a novel game theoretic perspective to simulation metamodeling which is used to facilitate simulation analysis. The utilization of the game theoretic approach is illustrated by analyzing simulation data produced with an existing air combat simulation model.

A Simulation Study on the Effect of Reconfiguration Strategy in an Automotive Body Shop Considering the Change of Product-mix

Resistance spot welding with its characteristic “dirty electrical load” is adistinctive engineering problem. The wide range of its industrial applications has made the subject an exciting research domain. Owing to the in term it tence of a welding operation, random overlapping of welding pulses, its sensitivity to voltage variation and application-specific quality control requirements, the electrical distribution system for welding loads need to be tied strongly with the process-related requirements. One of the most important applications of resistance welding is an automotive body shop, also called weld shop. This is where various units of a car body are welded together to form a shell that begins to look like a car. This paper focuses on explaining the nature of welding load and process requirements of a weld shop, leading towards the design and development of a Microsoft Excel based tool for optimal sizing of high current busway distribution system for resistance spot welding machines.This tool can be effectively used as a desktop application for efficient design development and comprehension of various design criteria. Besides, numerical based calculations have been provided with sound reasoning, progressively developing the proposed design criteria.

A modern lightweight design with dissimilar materials such as metals in combination with fiber-reinforced polycaprolactam represents a possible lightweight variant in the automotive body shop. One of the issues arising from the use of polycaprolactam is its hygroscopy. The collected moisture evaporates out during the curing of the adhesive in the paint drying process and causes the adhesive to foam up. This leads to a decrease of the structural bonding strength. Many different factors, such as waterjet cutting processes, transportation and storage in undefined environmental conditions before the gluing process, as well as the flushing bath of the painting pretreatment, can contribute to the component humidity.Based on the common lap-shear test, this essay presents the influence of the substrate humidity on the bonding strength as well as possible pretreatment methods to avoid or decrease the negative influence of the substrate humidity. Using infrared radiation or atmosphere plasma localized at the bonding spot, the substrate begins to dry resulting in a bond with better bonding strength. For the future it is planned to test other options like laser substrate drying, adhesive modifications with regard to viscosity and moisture catcher enrichment.

This research explores how global cosmetic players sense emerging market demand for new technologies and products, seize opportunities through the acquisition of core competencies that they needed, and transform their global value chain. The aim of this paper to assess the prerequisites of reciprocal synergies in merger and acquisition (M&A) deals pursuing global growth. To achieve this aim, the author asked a research question: what is the best way to measure the competence-based synergies as added market value in M&A deals? To answer this question, the author researched the latest theoretical findings on the antecedents of synergy in the merger and acquisition processes. The valuation of reciprocal synergies with real options was discussed with a focus on input variables’ values. Based on in-depth content analysis, the ARCTIC (A—Advantage, R—Relatedness, C—Complexity of Competence, T—Time of Integration, I—Implementation Plan, C—Cultural Fit) framework was developed and tested. The author selected three case studies to test the methodology empirically, namely, L’Oréal’s Body Shop acquisition in 2006 and divestiture in 2017, the acquisition of The Body Shop by Brazilian’s Natura Group in 2017, and the acquisition of Avon Products by Natura that was announced in 2019. The model for the valuation of reciprocal synergies used and discussed real options with a special focus on input variables’ values.

Large displacements of coiled springs are affected by both contact between coils and geometric non-linearities. The two phenomena also interact with each other and the only technology that efficiently accounts for both is Finite Elements Method (FEM). Such analysis can become very expensive, especially when a great number of elements and contacts are involved, as it is often the case. A discrete 3D model for fast and efficient simulation of non-linear springs is presented. The discrete model is based on equivalent beam theory and each node is associated with a contact region placed on the helix. Thus contact is accounted on its real occurrence and fast non-linear solution procedure is implemented. A study case is presented and comparison with FEM is discussed.

In a petrochemical plant, training new operators to operate plant safely and consistent is a need. Conventional training for operators relies on senior operators' experience. When experience operator leaves or resigns, it will take longer duration to train new personnel to gain the equivalent experience of previous or senior operators. The operators control the plant by using Distributed Control System (DCS) as an interface and ensure the reliability and productivity of plant sustains. The more challenges they face, the more experience they gain. Most of the operators are trained by real time events they faced on the job but errors or plant upsets should be avoided. It is estimated that in the United States alone, 10 billion USD is lost annually in the process industry caused by errors when faced with plant upsets. Operator training plan is therefore the crucial step to reduce the losses as well as the risk of operating the plant. Operator Training Simulator (OTS) is an alternative solution to safely provide the operators with various realistic operational conditions of the plant, including start-up, shutdown, and emergency conditions. This article discusses on the methodology of building an OTS for Ethylene plant, that are beneficial for educating operator response when faced with critical process upsets and succeeding quicker start-up. A dynamic process model was developed to give accurate dynamic behaviour of the plant process mainly during start-up, shutdown and upset conditions. Testing and verification processes of the models such as Model Acceptance Test (MAT), Factory Acceptance Test (FAT) and Site Acceptance Test (SAT) were deliberated. Few issues or problems faced during the testing were also discussed. Based on the experience of developing the simulator, process model was developed using mass and energy balance method does not reflect the actual plant conditions. It might be suitable for commissioning a new plant. However, once the plant is started all the parameters need to be realigned to meet the actual plant data. Process model for existing plant should always apply the actual plant data as a primary data. Especially during MAT, FAT and SAT processes, experience of the panel man is the best input data to get the best output of the simulations.

Machine Learning is used in this paper for noise-diagnostics to detect defined anomalies in nuclear plant reactor cores solely from neutron detector measurements. The proposed approach leverages advanced diffusion-based core simulation tools to generate large amounts of simulated data with different types of driving perturbations originating at all theoretically possible locations in the core. Specifically the CORE SIM+ modelling framework is employed, which generates these data in the frequency domain. We train using these vast quantities of simulated data state-of-the-art machine and deep learning models which are used to successfully perform semantic segmentation, classification and localisation of multiple simultaneously occurring in-core perturbations. Actual plant data are then considered, provided by two different reactors, including no labels about perturbation existence. A domain adaptation methodology is subsequently developed to extend the simulated setting to real plant measurements, which uses self-supervised, or unsupervised learning, to align the simulated data with the actual plant data and detect perturbations, whilst classifying their type and estimating their location. Experimental studies illustrate the successful performance of the developed approach and extensions are described that indicate a great potential for further research.

This paper presents a new game-theoretic approach toward the validation of discrete-event air combat (AC) simulation models and simulation-based optimization. In this approach, statistical techniques are applied for estimating games based on data produced by a simulation model. The estimation procedure is presented in cases involving games with both discrete and continuous decision variables. The validity of the simulation model is assessed by comparing the properties of the estimated games to actual practices in AC. These games are also applied for simulation-based optimization in a two-sided setting in which the action of the opponent is taken into account. In optimization, the estimated games enable the study of effectiveness of AC tactics as well as aircraft, weapons, and avionics configurations. The game-theoretic approach enhances existing methods for the validation of discrete-event simulation models and techniques for simulation-based optimization by incorporating the inherent game setting of AC into the analysis. It also provides a novel game-theoretic perspective to simulation metamodeling which is used to facilitate simulation analysis. The utilization of the game-theoretic approach is illustrated by analyzing simulation data obtained with an existing AC simulation model.

Ammonia is one of the alternative marine fuels to reduce greenhouse gas emissions for international shipping. Ammonia bunker supply chain includes ammonia production, storage, transportation, loading and bunkering operations and consumption process. Current research topics include ammonia bunkering supply and demand configurations, ammonia safety study for marine applications, ammonia bunkering port selection, and its bunker supply chain resilience. This study aims to analyse the adoption of ammonia as bunker fuel from the bunker supply chain perspective, comprising five studies: Study 1 conducts a systematic literature review on ship bunkering from perspectives of bunkering management, alternative fuel bunkering and bunkering operational risk. A concept of sustainable, safe and smart (3S) bunkering is proposed for multi-fuel bunkering, digital bunkering, and risk-based approaches for implementing alternative marine fuels. Study 2 describes the development of a discrete event simulation model for bunker supply chain, emphasising how ammonia bunkering affects the operational and economic performance of the system. The results show that the increase in bunkering service time compared with pure MFO bunkering is up to 113.7% for ammonia-MFO dual fuel bunkering, and up to 61.6% for pure ammonia bunkering. Ammonia bunkering flow rate is the most sensitive parameter for bunkering service time, with an effect of up to 51.3% decrease when it is increased by 50%. Moreover, the increase in annual operational cost for a bunker supplier is up to 19.1% under the condition of ammonia-MFO dual fuel bunkering. The number of ammonia bunker supply vessels is the most sensitive parameter for annual operational cost, and if it is increased by 50%, the increased annual operational cost variability is as high as 15.2%. Studies 3 and 4 investigate the operational risk of accidental ammonia release in bunkering ports, considering various bunkering supply scenarios, release events, meteorological conditions, seasons, and geographical locations. Findings indicate that under the risk assessment criteria of 1100 ppm maximum cloud footprint, wind speed emerges as the most influential factor for both small and medium release scales, exhibiting sensitivities of up to -4 and -13.25, respectively. In the case of large release scales, hose diameter emerges as the most important factor, exhibiting a sensitivity of +33.77 to 1100 ppm maximum cloud footprint and a sensitivity of +26.17 to the 3% lethality footprint. Moreover, study 4 shows the data-driven framework, including a user layer, simulation-based knowledge layer and decision layer. An integrated approach incorporating energy balance modelling, Gaussian dispersion modelling and an adaptive neuro-fuzzy inference system (ANFIS) is developed. The synergy effects of different parameters are also presented. Study 5 investigates the ripple effect of ammonia supply disruptions on an ammonia bunkering port, considering ammonia bunker demand dynamics. Low-frequency and high-impact disruption types are discussed in this study. A Coloured Petri Nets model shows the path of spreading disruptions, their interrelationships and their effects on the system performance. The cost, lead time and GHG emission reduction are selected as indicators to evaluate the system performance. This PhD work contributes to the field of ammonia as a marine fuel, advancing both scientific knowledge and practical applications. It fills critical gaps in the literature on ammonia bunkering in the maritime sector and is the first to examine the combined impact of various factors related to the ammonia bunker supply chain, operational risk, and disruption risk. The proposed models, including a discrete event system simulation model, Gaussian dispersion model, simulation-based ANFIS model, and Petri Nets model, can be applied to analyse other types of fuels. Additionally, the insights and discussions on the risks associated with ammonia bunkering provide valuable references for stakeholders in making decisions about bunkering supply configurations and risk mitigations.

Using discrete simulation modeling to study large-scale system reliability/availability

The purpose of making efficient and flexible manufacturing systems is often related to the possibility to analyze the system considering at the same time a wide number of parameters and their interactions. Simulation models are proved to be useful to support and drive company management in improving the performances of production and logistic systems. However, to achieve the expected results, a detailed model of the production and logistic system is needed as well as a structured error analysis to guarantee results reliability. The aim of this paper is to give some practical guide lines in order to drive the error analysis for discrete event stochastic simulation model that are widely used to study production and logistic system.Keywordsdiscrete eventsimulation modelerror analysisstochastic model

This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and risks associated with managing passenger flows within airport terminals. By simulating different scenarios, the model aims to provide valuable insights into how to effectively handle crowd dynamics and enhance overall terminal efficiency, safety, and sustainability. This case study was conducted at Henri Coanda International Airport, ARENA 12 simulation software being used in order to model the passenger flows within the airport terminal. Two scenarios were considered: The first one involves maintaining a fixed number of security and check-in desks for the two airline groups. In contrast, the second scenario allows for a variable number of security and check-in desks for the same airline groups. By optimizing resource allocation and minimizing waiting time, this model contributes to more sustainable airport management operations. Three measures of performance (MOPs) were selected to assess the system activity: the average passenger waiting time, the average passenger number queue length, and the average utilization rate. Comparing the results, we concluded that the second scenario shows a relative improvement in almost all performance measures when compared to the first scenario.

The “carbon neutral power generation” policy of the European Union requires the phasing out of fossil fuel power plants. These plants still play a crucial role in the energy mix in many countries; therefore, efforts are put forward to lower their CO2 emissions. The available solution for an existing coal plant is the implementation of biomass co-firing, which allows it to reduce twice its carbon footprint in order to achieve the level of natural gas plants, which are preferable on the way to zero-emission power generation. However the side effect is a significant increase in the bulk fuel volumes that are acquired, handled, and finally supplied to the power plant units. A necessary extension of the complex logistic system for unloading, quality tagging, storing, and transporting biomass may increase the plant’s noise emissions beyond the allowed thresholds. For a comprehensive assessment of the concept of expanding the power plant’s biofuel supply system (BSS), a discrete simulation model was built to dimension system elements and verify the overall correctness of the proposed solutions. Then, a dedicated noise emission model was built for the purposes of mandatory environmental impact assessment procedures for the planned expansion of the BSS. The noise model showed the possibility of exceeding the permissible noise levels at night in selected locations. The new simulations of the BSS model were used to analyze various scenarios of biomass supply with regard to alternative switching off the selected branches of the whole BSS. The length of the queue of unloaded freight trains delivering an average quality biomass after a period of 2 weeks is used as a key performance parameter of the BSS. A queue shorter than 1 freight train is accepted. Assuming the rising share of RESS in the Polish energy mix, the thermal plant’s 2-week average power output shall not exceed 70% of its maximum capacity. The results of the simulations indicate that under these constraints, the biofuel supplies can be sufficient regardless of the nighttime stops, if 50% of the supplied biomass volumes are delivered by trucks. If the trucks’ share drops to 25%, the plant’s 2-week average power output is limited to 45% of its maximum power. The use of digital spatial simulation models for a complex, cyclical-continuous transport system to control its operation is an effective method of addressing environmental conflicts at the design stage of the extension of industrial installations in urbanized areas.

Research on the generation mechanism and interference of surface texture in ultrasonic vibration assisted milling