Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization

The quest for manufacturing process improvement and higher levels of customer satisfaction mandates that organizations must be equipped with advanced tools and techniques in order to respond towards ever changing internal and external customer demands by maintaining the optimal process performance, lower cost and higher profit levels. A manufacturing process can be defined as a collection of activities designed to produce a specific output for a particular customer or market. To achieve internal and external objectives, significant process parameters must be identified and evaluated to optimize the process performance. This even becomes more important to deal with fierce competition and ever changing customer demands. This paper illustrates an integrated approach using design of experiments techniques and discrete event simulation (Simul8) to understand and optimize the system dynamic based on operational control parameter evaluation and their boundary conditions. Further, the proposed model is validated using a real world manufacturing process case study to optimize the manufacturing process performance. Discrete event simulation tool is used to mimic the real world scenario, which provides a flexible and powerful way to comprehensively understand the manufacturing process variations and allows controlled 'What-If´ analysis based on design of experiments approach. Finally, this paper discusses the potential applications of the proposed methodology in the cable industry in order to optimize the cable manufacturing process by regulating the operational control parameters such as dealing with various product configurations with different equipment settings, different product flows and work in process (WIP) space limitations

Parameter determination and experimental validation of a wire feed additive manufacturing model

“Laser metal deposition is an additive manufacturing method with great scope and robustness. The wire fed additive manufacturing method has great opportunities in space applications and other zero gravity manufacturing processes. Process parameters play an important role in controlling the complex phenomenon and obtaining an ideal manufactured part. These parameters can be efficiently determined using simulation tools which are highly essential in visualizing real world experiments, therefore saving time and experimental costs. The objective of this study is to develop a transient 3D model of laser aided wire feed metal deposition which realizes the heat transfer and fluid flow behavior of the melt pool and wire deposition with varying process parameters. The model was programmed in Python and a 1 KW Gaussian beam fiber laser was used to conduct experiments. Design of experiments was utilized to determine all possible levels of factors and experiments were conducted on Ti-6Al-4V alloy with and without wire deposition to establish the behavior of the critical outputs with varying parameters. The effect of laser exposure to the melt pool profile and deposit profile is obtained and the results are compared with the model. The comparison of simulation and experimental results shows that this model can successfully predict the temperature profile, fluid characteristics and solidified metal profile. The optimum input parameters based on material properties can be identified using this model”--Abstract, page iii

Digital design and thermomechanical process simulation for 3D printing with ABS and soyhull fibers reinforced ABS composites.

Recent demonstrations with fused filament fabrication (FFF) 3D printing have shown to produce prototypes as well as production components. Additionally, due to the FFF process platforms being low-cost and readily available there has been a high-demand to produce on-demand parts for various applications in automotive, in-space manufacturing and electronic industries. However, current limitations such as limited availability of advanced composites materials, and guidelines for design-for-manufacturing make the process prone to trial-and-error experiments both at the materials development, product design and manufacturing stage. In this work, new thermomechanical process simulations platform, Digimat-AM has been evaluated to address and demonstrate digital design and manufacturing of FFF process by performing simulation and experiments. With the use of Acrylonitrile butadiene (ABS) material and soyhull fibers reinforced ABS composite (ABS-SFRC) as a basis, an L9 Taguchi design-of-experiment (DOE) was setup by varying key process input parameters for FFF 3D printing such as layer thickness, melt temperature and extrusion multiplier were varied for three levels. A total of 9 DOE simulations and experiments were performed to compare part properties such as dimensions, warpage, and print time were analysed. Additionally, ANOVA analysis was performed to identify the optimum and the worst conditions for printing and correlate them with their effect on the mechanical properties of the printed samples. Furthermore, from the simulation results, a reverse warpage geometry, 3D model was generated that factors for part warpage, shrinkage, or other defects to enable 3D printing parts to design dimensions. Subsequently, using the generated reversed warpage geometry was used to perform 3D printed experiments and analyzed for part dimensions and defects. As a case study, a functional prototype [Two different geometries] was designed and simulated on Digimat-AM and using the above guide, 3D printing was performed to obtain part to specific dimensions. In addition to that, the thermomechanical properties of ABS-SFRC were needed to perform the Digimat simulation of geometries printed with ABS-SFRC. However, the materials property database of ABS-SFRC is very limited and experimental measurements can be expensive and time consuming. This work investigates models that can predict soyhull fibers reinforced polymer material composite properties that are required as input parameters for simulation using the Digimat process design platform for fused filament fabrication. ABS-SFRC filaments were made from 90%ABS 10% soyhull fibers feedstock using pilot scale filament extrusion system. Density, specific heat, thermal conductivity, and Young\u27s modulus were calculated using models. The modeled material properties were used to conduct simulations to understand material-processing-geometry interactions

Evaluating the impact of design decisions on the financial performance of manufacturing companies

Product design decisions can have a significant impact on the financial and operation performance of manufacturing companies. Therefore good analysis of the financial impact of design decisions is required if the profitability of the business is to be maximised. The product design process can be viewed as a chain of decisions which links decisions about the concept to decisions about the detail. The idea of decision chains can be extended to include the design and operation of the 'downstream' business processes which manufacture and support the product. These chains of decisions are not independent but are interrelated in a complex manner. To deal with the interdependencies requires a modelling approach which represents all the chains of decisions, to a level of detail not normally considered in the analysis of product design. The operational, control and financial elements of a manufacturing business constitute a dynamic system. These elements interact with each other and with external elements (i.e. customers and suppliers). Analysing the chain of decisions for such an environment requires the application of simulation techniques, not just to any one area of interest, but to the whole business i.e. an enterprise simulation. To investigate the capability and viability of enterprise simulation an experimental 'Whole Business Simulation' system has been developed. This system combines specialist simulation elements and standard operational applications software packages, to create a model that incorporates all the key elements of a manufacturing business, including its customers and suppliers. By means of a series of experiments, the performance of this system was compared with a range of existing analysis tools (i.e. DFX, capacity calculation, shop floor simulator, and business planner driven by a shop floor simulator)

A Cloud/HPC Platform and Marketplace for Manufacturing SMEs

Information and Communication Technology (ICT) is essential for the digitalization of the manufacturing sector. However, less than 25% of manufacturing companies in Europe profit from ICT-enabled solutions. In order to boost the competitiveness of European manufacturers (especially Small and Medium-sized Enterprises – (SMEs), innovative solutions need to consider both technological and commercial scalability from the very early stages of the design process throughout the full implementation and utilisation of the solution. From this perspective, cloudification of services has become the ideal enabler in manufacturing digitalization. Successful previous European initiatives have already demonstrated the benefits of cloudifying engineering services, by combining High Performance Computing (HPC) resources, computational tools, and cloud computing platforms. CloudiFacturing is an EU funded Innovation Action project that brings and progresses advanced ICT in the field of Cloud/HPC-based modelling and simulation, data analytics for online factory data, and real-time support to European manufacturing SMEs, contributing to their competitiveness and resource efficiency via optimizing production processes and producibility. CloudiFacturing is developing a generic, workflow oriented platform (Figure 1) that enables secure deployment and execution of workflow-based Cloud/HPC applications. These applications are deployed in a central workflow repository (EMGREPO) that accommodates for various heterogeneous workflow/application types (e.g. Flowbster, SemWES, CloudBroker). Additionally, a central data transfer and browsing component (EMGDATA) facilitates data sharing between the various workflow engines at execution time. Workflows and applications are executed via the Workflow and Applications Mediator (EMGWAM) component that enables the execution of pre-prepared workflows as black boxes and also facilitates their combination into higher level meta-workflow pipelines. In order to facilitate commercial utilization, the CloudiFacturing Platform comes with a central billing systems (EMGBC) and advanced security solutions for single sign-on and user authentication/authorization (EMGUM). On top of the platform, the project also develops a Digital Marketplace (the EMGORA – Engineering and Manufacturing Agora) for manufacturing companies, independent software vendors, and consultancy and training providers. The marketplace provides seamless access to the underlying services of the platform, enabling the publication, execution, billing and management of workflow-based applications for the manufacturing sector. Additionally, the marketplace also serves as a generic community hub where a wide range of activities, for example domain specific information exchange, value added services or training courses and material can be found. CloudiFacturing demonstrates the technical and economic feasibility of its platform and marketplace on the basis of more than twenty cross-national application experiments involving manufacturing companies, independent software vendors, technology consultants, digital innovation hubs and resource providers, in three consecutive waves. The first wave of these experiments has just finished and the second wave kicked-off in February 2019 with seven new experiments. During the first wave, the 15 involved companies reported significant expected impact figures as a result of the implemented technological solution. Such impact measures included, for example, the creation of 18 new products or services within one year and 80 within five years, 1.9 million Euro turnover increase within one year and 8.5 million within five years, and the creation of 13 new jobs within one year and 60 within five years. Typical application areas include improving quality control and maintenance at manufacturing SMEs using big data analytics and digital twins, optimizing efficiency of truck components manufacturing processes via discrete event simulation, numerical modelling and simulation of heat treating processes in the aluminium industry, or optimizing design and production of electric drives. The development of the CloudiFacturing Platform and Marketplace is currently ongoing. This presentation will provide a short overview of these components and it will also summarise the results of the first wave of application experiments

Design of experiments for non-manufacturing processes : benefits, challenges and some examples

Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in non-manufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments

Simulation reduction using the Taguchi method

A large amount of engineering effort is consumed in conducting experiments to obtain information needed for making design decisions. Efficiency in generating such information is the key to meeting market windows, keeping development and manufacturing costs low, and having high-quality products. The principal focus of this project is to develop and implement applications of Taguchi's quality engineering techniques. In particular, we show how these techniques are applied to reduce the number of experiments for trajectory simulation of the LifeSat space vehicle. Orthogonal arrays are used to study many parameters simultaneously with a minimum of time and resources. Taguchi's signal to noise ratio is being employed to measure quality. A compromise Decision Support Problem and Robust Design are applied to demonstrate how quality is designed into a product in the early stages of designing

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Computer experiment - a case study for modelling and simulation of manufacturing systems

Deterministic computer simulation of physical experiments is now a common technique in science and engineering. Often, physical experiments are too time consuming, expensive or impossible to conduct. Complex computer models or codes, rather than physical experiments lead to the study of computer experiments, which are used to investigate many scientific phenomena. A computer experiment consists of a number of runs of the computer code with different input choices. The Design and Analysis of Computer Experiments is a rapidly growing technique in statistical experimental design. This paper aims to discuss some practical issues when designing a computer simulation and/or experiments for manufacturing systems. A case study approach is reviewed and presented

Impact of model fidelity in factory layout assessment using immersive discrete event simulation

Discrete Event Simulation (DES) can help speed up the layout design process. It offers further benefits when combined with Virtual Reality (VR). The latest technology, Immersive Virtual Reality (IVR), immerses users in virtual prototypes of their manufacturing plants to-be, potentially helping decision-making. This work seeks to evaluate the impact of visual fidelity, which refers to the degree to which objects in VR conforms to the real world, using an IVR visualisation of the DES model of an actual shop floor. User studies are performed using scenarios populated with low- and high-fidelity models. Study participant carried out four tasks representative of layout decision-making. Limitations of existing IVR technology was found to cause motion sickness. The results indicate with the particular group of naïve modellers used that there is no significant difference in benefits between low and high fidelity, suggesting that low fidelity VR models may be more cost-effective for this group