Integrating Experimental Design and Statistical Control for Quality Improvement

PurposeThe purpose of this paper is to focus the application of design of experiments (DOE) using industrial equipments, reinforcing idea that non‐statistical aspects in planning and conducting experiments are so important as formal design and analysis.Design/methodology/approachTwo case studies are presented to illustrate typical industrial applications and difficulties. Supported on these case studies and literature, this paper presents guidelines to planning, conducting and analysis involving technical and organizational aspects.FindingsSolving problems in industry, including in companies recognized as competent in the respective industrial sector, is not just a question of applying the right technique. Ceramic industry case study illustrates how important are non‐statistical issues in DOE application. Paint industry case study illustrates the strong relationship of the results with incorporating presented guidelines into practice. Moreover, both case studies consolidating a fundamental advantage of DOE: experimentation provides more knowledge about products, processes and technologies, even in unsuccessful case studies.Research limitations/implicationsUnsuccessful cases studies are very useful for identifying pitfalls and others limitations. This paper highlight difficulties aroused from non‐statistical aspects, although it is possible to find unsuccessful case studies due to statistical issues also. So, papers illustrating inadequate application of statistical techniques are welcome.Practical implicationsSuccessful DOE implementation depends on statistical and non‐statistical aspects. Although none of them shall be neglected, technical skills and technological knowledge about processes and products, management understanding of potential possibilities of statistical techniques and statistical fundamentals and knowledge about techniques of DOE must be ensuring to successful case studies in industrial setting.Originality/valueThis paper highlights non‐statistical aspects instead of the statistical ones. Tob overcome difficulties structured guidelines were designed to support DOE application in industrial setting.

One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations.

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Resistance spot welding (RSW) is one of the most commonly used form of manufacturing process. To efficiently search for the optimal RSW parameters in a new welding power control scheme to obtain the maximum tensile strength of welded joints, a design of experiments (DoE) using orthogonal arrays has been conducted. However the lack of success of conventional DoE is attributed to two facts that the weld quality is quantitatively measured and level-based evaluated, and the welding expulsion compromises the continuity of response surface, which is an underlying hypothesis of normal DoE analysis. Hereby a fuzzy analysis methodology for the experimentation design, referred as fuzzy orthogonal experiments (FOE) in this paper, has been proposed. With the fuzzification of factor levels and experimental objectives, the proposed method is able to explore the results of arbitrarily given factors without additional experiments, and identifies the varying tendency under condition of discontinuity experimental responses. The FOE application for a specifically planned RSW process presents a detailed FOE analyzing procedures, and yields the optimal parameter settings. The case study shows that FOE outperforms the traditional orthogonal DoE for its minimized experiment numbers and robust response tendency analysis.

Maximization of primary energy savings of solar heating and cooling systems by transient simulations and computer design of experiments

Earth observation satellite system (EOSS) is the main space platform collecting ground information. Optimization of EOSS is difficult, as it is a complex system referring a great deal of design variables and uncertain factors. Therefore, an optimization framework based on design of experiment and surrogate model is proposed. Design of experiment is used to generate simulation plan, which will greatly cut down cost of simulation. Then surrogate model is built to analyze simulation data and approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to solve the model. According to the framework, a case study is carried out. The final results illustrate the framework is useful and effective for the problem of EOSS optimization.

This paper provides a detailed evaluation of existing methods and techniques for optimizing 3D printing parameters across different additive manufacturing technologies. The optimization of printing parameters is essential to obtain superior mechanical properties, dimensional accuracy, surface quality, and production efficiency in 3D printed components. The review systematically examines methodological approaches, including statistical design of experiments (DOE), artificial intelligence and machine learning (AI/ML) applications, simulation-based optimization, and in-process monitoring and feedback control. The review focuses on material-specific optimization methods for polymers, metals, composites, and ceramics by analyzing process-specific parameters and material-dependent variables. The paper demonstrates real-world applications of optimization methodologies through case studies from aerospace, automotive, medical, and consumer goods manufacturing industries, which show substantial improvements in product performance, production speed, and cost-effectiveness.

In integrated modeling and simulation of space exploration architectures and campaigns, the challenge is to effectively explore the breadth of the trade space while reaching sufficient depth to gain insight. The use of Design of Experiments (DOE) in this field can enable more efficient parametric studies thereby allowing more resources to be focused on detailed analysis of interesting cases. This paper presents an approach based on DOE with orthogonal arrays and presents a recent integrated modeling and trade study effort within the Constellation Program (CxP) Integrated Design Analysis Cycle (IDAC-3) as a case study 1 . The DOE analysis showed that the three largest factors impacting the campaign cargo margins are spares demand uncertainty (K'-factor), lunar lander cargo capacity and consumables demand uncertainty. A related contingency analysis showed that once outpost construction is completed, from a logistics perspective the campaign can absorb failed cargo flights for up to 3 years (6 flights).

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.

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Production System (PS) is the process of planning, organizing, directing and controlling the tactical and strategic planning of the different components of the company, to transform inputs into finished products which must be effectively managed.the present study aimed to increasing the efficiency and determining the useful methods to evaluate and optimize the performance in different part of PS. To this end, an integrated Discrete-event Simulation (DES), Design of Experiments (DOE), Data Envelopment Analysis (DEA), and Multi-attribute Decision Making (MADM) models were implemented to analyze and optimize the real PS process. In the case study of the automobile manufacturing industry in Iran, the accurate analysis was applied to the proposed approach and its different aspects were considered as well. The results indicated that the proposed approach is a practical way for evaluating and optimizing the performance of different part of PS, compared to previous models and helps the manufacturing companies to make efficient decisions regarding increasing productivity while decreasing the essential problems.

Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure the consistency and sustainability of the enhancement methods. In this study, a Plan–Do–Check–Act (PDCA) framework is implemented for a case study in the food industry under which a multi-response optimization problem is investigated. The design of experiment (DOE) is used to examine the effect of process parameters on the quality responses by using the Taguchi method to find the optimal setting for each parameter. An orthogonal array (OA) and signal-to-noise (SNR) ratio is employed to investigate the performance characteristics. Each performance characteristic is then converted into a signal-to-noise ratio, and all the ratios are then fed into a fuzzy model to produce a single comprehensive output measure (COM). The average COM values for various factor levels are calculated, and the level that maximizes the COM value for each factor is identified as the optimal level. Results indicated the effectiveness of the applied method to find the optimal factor levels for the multi-response optimization problem under study. The global optimal factor levels that are driven from the fuzzy logic for the studied parameters are 1250, 40, 7.5, and 1:2, for the speed, frying time, cooking time, and the coating ratio, respectively. Means of all the studied quality characteristics were closer to the target values when compared with the initial factors’ settings.

The work aims at optimize the wear and mechanical properties of natural composite fibers through the Taguchi method, a robust statistical approach, which is extensively used in design of experiments and quality improvement. Natural fibers like jute, and hemp have attracted a lot of attention recently due to their environmental advantages, biodegradability, and low cost. The focus is on incorporating nanoparticles like graphene and titanium dioxide (TiO2) to create nanocomposites with enhanced performance. However, though these fibers are promising, the mechanical properties like tensile strength, hardness, and wear resistance usually fail to meet the criteria compared to synthetic alternatives. Thus, they cannot be applied on a large scale in high-performance industriesThe Taguchi method is used to optimize the processing parameters that influence the wear and mechanical performance of natural composite fibers. The Taguchi experimental design is used to determine the optimal levels of these factors through a systematic approach that minimizes variability and maximizes performance. The results show that some processing parameters have a significant effect on both wear resistance and mechanical properties, thus providing insights into how the performance of natural composite fibers may be improved. Overall, the results of the study show the effectiveness of the Taguchi method to optimize the design of natural fiber composites, pointing to the prospects of enhanced durability and sustainability in further industrial applications.

Industry is using more than 30% of the total energy consumed in industrialized countries and manufacturing is one of the most energy-intensive industrial activities. Because of the current global energy crisis, reducing energy consumption in manufacturing has become one of the top research priorities. This paper aims to review the existing technologies that directly improve the energy efficiency in manufacturing plant as well as identify future research opportunities. Four levels to study energy saving technologies are systems, process, equipment, and facility. Two industrial energy monitoring case studies are presented where idle energy is observed to contribute up to 63% of the total energy consumption.

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