A Statistical Perspective on Highly Accelerated Testing

The Cambridge Engineering Selector (CES) is a tool that focuses on material, process, and shape combinations of product design. The Resource Consumption Model (RCM) is a tool that focuses on production process resources, resource costs, production volume, production capacity, and manufacturing cycle time. Both tools help identify viable manufacturing process alternatives to meet stated manufacturing objectives.

Gap Analysis and Optimization of Process Involved in Product Design and Development by Integrating Enterprise Resource Planning & Product Lifecycle Management

The modelling of nominal product geometry by computer-aided design tools has gained immense attention in industry during the last decades and is nowadays an integral part of the virtual product development process. However, the established geometry representation schemes for CAD imply severe drawbacks regarding the modelling of geometric part deviations, which are inevitably observed on every manufactured artefact. As a response, the concept of Skin Model Shapes, which stems from international standards for geometric product specification and verification, has been developed as a novel approach for the consideration of product shape variability. It employs discrete geometry methods and computational techniques, such as point clouds, surface meshes and geometric processing, to model shape variability and to facilitate the communication of geometric product information throughout the product design, manufacturing, and inspection processes. This paper highlights the foundations of this concept, demonstrates its potentials for the representation of product geometry considering geometric variations along the product lifecycle, and illustrates main applications in the context of computer aided product and process development. In this regard, a focus is laid upon recent results and contributions, such as contact modelling, tolerance analysis, and motion tolerancing based on Skin Model Shapes. Furthermore, challenges for future research, such as the application to complex shapes and compliant parts, as well as the consideration of novel manufacturing processes, are discussed.

Quality Function Deployment (QFD) has long been used as a successful design methodology in the heavy industrial and automotive industries. QFD helps designers utilize the ‘voice of the customer’, or customer requirements, to determine which engineering metrics or product specifications are the most essential [1]. This prioritization helps designers know what part of the product or process is most beneficial to focus on during design, resulting in products that better meet customer requirements and generate increased commercial success. QFD and most other design methodologies have rarely been applied to MEMS products [2]. In the case of QFD, the structure of the most common format of the tool dictates that engineering metrics should be related to parts characteristics in the second step of applying QFD. This causes difficulties in using the tool for MEMS as most MEMS do not have physical ‘parts’ that are assembled into a final device. Rather, MEMS have product specifications and a manufacturing process used to create the product. Generally there is a tight link between product and process in MEMS. This link has been utilized in creating a modified version of QFD that relates engineering metrics to design concepts, including product conceptualization and manufacturing process. The modified QFD utilizes aspects of Pugh Concept Selection, and differs from typical QFD primarily in consideration of product idea and manufacturing process in the early phases of product definition. The modified QFD was applied to a MEMS project whose goal was to develop a handheld device that allows users to control the selection and release of a variety of stored scents. The technique was also applied to a microscale heat exchanger for integrated circuits. The scent dispenser and heat exchanger were designed and prototyped at Stanford University in 2005 and 2006, respectively. The modified version of QFD gave insight early in the product definition phase on which design concept to pursue to prototype. Use of this and other design methodologies in the MEMS field could shorten the time it takes to progress through product development to volume manufacturing, and increase confidence in the marketability of the chosen design and manufacturing process. A case study demonstrating the effects of using modified QFD Phase II to assist in finding a good fit between technical capabilities and market application was performed by the author on an acoustic sensor technology [3].

This study aimed to examine the association between team stress level and adverse tracheal intubation (TI)-associated events during neonatal intubations. TIs from 10 academic neonatal intensive care units were analyzed. Team stress level was rated immediately after TI using a 7-point Likert scale (1 = high stress). Associations among team stress, adverse TI-associated events, and TI characteristics were evaluated. In this study, 208 of 2,009 TIs (10%) had high stress levels (score < 4). Oxygenation failure, hemodynamic instability, and family presence were associated with high stress level. Video laryngoscopy and premedication were associated with lower stress levels. High stress level TIs were associated with adverse TI-associated event rates (31 vs. 16%, p < 0.001), which remained significant after adjusting for potential confounders including patient, provider, and practice factors associated with high stress (odds ratio: 1.90, 96% confidence interval: 1.36-2.67, p < 0.001). High team stress levels during TI were more frequently reported among TIs with adverse events.

In accelerated life tests (ALTs), test units are run at higher stress levels in order to experience more failures. A Weibull regression model can, in this case, be used to infer failure behavior at the normal stress level. In practice, statistical analyses usually do not take batch differences into account even when they are present. To correctly include batch differences in the analysis, one needs to use a regression model with random effects. In this paper, we use such a model to show that ignoring batch differences in modeling can result in overly precise estimates of quantiles and probabilities of failure at the normal stress level, as well as overly precise predictions of the failure time for a new unit at the normal stress level.

Along with the rapid development of computer technology and CIMS technology, virtual prototyping in product design and manufacturing process are playing an increasingly important role. Based on the analysis the function of virtual prototype, the development platform architecture of virtual prototype for mechanical products is proposed in this paper. Furthermore, the integrated framework process, which is support the pump design, testing, operation, is analyzed in detail. The running case of virtual prototyping environment of pump assembly shows that the application of virtual prototyping technology provides a new idea and means for new product development and product performance improvement.

As electronic products rapidly advance and grow in complexity, the product design and manufacturing process has forced the global manufacturing industry to evolve faster ever before. International competition is intense and requires hi-tech companies to employ top-notch quality measures – the ability to design and manufacture anywhere, to meet customer requirements, and to shorten the product development lifecycle. As such, a digitalized factory strategy is more critical than ever before. The Siemens End-to-End Digitalization Solution for Electronic Manufacturing helps manufacturers make the digital transition and reap the benefits of intelligent manufacturing.

Cost management and quality analysis methods currently contribute to the decision support in product design and manufacturing process. One of the objectives is to evaluate a process plan in order to choose the most efficient solution in economic and qualitative terms. This study proposes a methodology based on Failure Mode and Effect Analysis (FMEA) with the financial aspects of risk, and Activity-Based Costing (ABC) in the purpose of economical evaluating the process plan. It shows how ABC and cost-based FMEA concepts can be adapted to measure quality-related costs and prioritize quality improvement efforts. Information modeling for this methodology is provided; and a case example is presented to illustrate its application.

PurposeThe purpose of this study was to identify the major quality features of wearable technology embedded products that have the greatest impact on consumer satisfaction using the Kano model, an organized approach to specify consumer requirements and expectation through a preference classification technique.Design/methodology/approachUsing a quantitative research method, an online survey was conducted with a convenience sample of US consumers aged between 19 years old and over. A total of otal 471 useable data were obtained and used for the data analysis.FindingsThis study identified that the 11 quality features of wearables belong to one-dimensional quality category among the five Kano categories, although the impact of each quality feature's performance on consumer satisfaction varies. The results also showed that the performance level of durability, long battery life, usability, product safety, comfortability and reasonable price has the greatest impact on consumer satisfaction of wearables.Research limitations/implicationsThis study has implications for future research by integrating the Kano model with other design and product development related theoretical models when designing, developing and evaluating various wearable products.Originality/valueThis study quantified the key quality features of wearables using the Kano model, which can be a great measurement tool for future researchers to adopt in their studies. The findings of this study help designers, developers and producers of wearables to prioritize the quality features during the product design, development and manufacturing process.

Modern scientific sources point out a close connection between depression, anxiety, psychological distress and low quality of life. The purpose of this study is to assess the impact of sleep quality on the quality of life in patients with anxiety disorders of neurotic origin. Examination of 81 patients with anxiety disorders of neurotic origin and dyssomnic disorders was carried out using clinical-psychopathological, psychodiagnostic and statistical research methods. The psychodiagnostic method included determining the level of stress load according to the PSM-25 questionnaire, the level of anxiety and depression by the HADS scale, the severity of the mental and somatic component of anxiety according to the HAM-A scale, assessing the quality of life according to the "SF-36" scale, studying sleep quality disorders according to the PSQI questionnaire.&#x0D; Depending on the level of psychological stress based on the PSM-25 scale, 3 groups were formed: group 1 included 25 (30.7%) people with a low level of stress, group 2 included 29 (35.8%) individuals with an average stress level, and group 3 included 27 people (37.4%) with a high stress level. Analysis according to the HADS scale has shown that anxiety symptoms prevail over depressive symptoms in all examined subjects. According to the HAM-A scale, the average score of the somatic and mental components in the group 1 is significantly lower compared to the group 2 (p&lt;0.001) and the group 3 (p&lt;0.001).&#x0D; This study has demonstrated that as the level of stress increases, there is a tendency for quality of life indicators to decrease in all spheres of functioning. Significantly worse sleep quality scores were found in patients with anxiety disorder and high mental stress level compared to those with moderate or low levels. There is a strong negative correlation between indicators by the "vital activity" scale and such components of sleep quality as "daytime dysfunction", "sleep latency" and the total score of sleep quality, between indicators by the scales "role functioning due to physical condition" and "daytime dysfunction". It has been found out that with increasing pain intensity, all indicators of sleep quality decline. The data obtained should be taken into account in developing patient-centred treatment plan for this group of patients.

With the rapid development of smart manufacturing, some challenges are emerging in the production management, including the utilization of information technology and the elimination of dynamic disturbance. A digital twin-driven production management system (DTPMS) can dynamically simulate and optimize production processes in manufacturing and achieve real-time synchronization, high fidelity, and real-virtual fusion in cyber-physical production. This paper focuses on establishing DTPMS for production life-cycle management. First, we illustrate how to integrate digital twin technology and simulation platforms. Second, a framework of DTPMS is proposed to support a cyber-physical system of production workshop, including product design, product manufacturing, and intelligent service management. Finally, the proposed DTPMS is applied to the production process of a heavy-duty vehicle gearbox. The experimental results indicate that the defective rate of products and the in-process inventory are reduced by 34% and 89%, respectively, while the one-time pass rate of product inspection is increased by 14.2%, which demonstrates the feasibility and effectiveness of the DTPMS.

In product design and manufacturing process planning, we prefer the “best” product design out of all feasible designs, which is called the optimal design. Optimization techniques have become the major quantitative tools in engineering design and decision-making process. Process optimization is a process that adjusts a current process to optimize some specified sets of parameters without violating constraints. The most common goals are minimizing cost while maximizing profit and efficiency. This chapter introduces the formulation of a design problem as an optimization statement incorporated with the design constraints. Essential computational methods for solving the optimization statements with demonstrated examples are also discussed.

Differences in stress levels between junior high school students and college students during online learning

Burnout is a challenging issue among surgeons. Many factors have been linked to burnout, such as an inadequate work-life balance, high stress levels, and the frequency of on-call, overnight shifts. There is a lack of research on factors in surgeons’ daily lives associated with burnout. This study observed trauma surgeons with daily surveys assessing stress and fatigue over 9 days (6 day shifts, three night shifts). This study analyzes surgeons’ stress and fatigue levels. The study population was compared to the population of US critical care surgeons to ensure representativeness based on age, sex, and rates of burnout. Overnight shifts showed high stress and fatigue levels relative to off days, day shifts, the 3 days before, and the 3 days after overnight shifts. Days performing surgery were also more stressful and fatiguing than non-surgery work days and off days. Fatigue and stress levels were minimized during off days.