Efficient Product Development Research Articles

Enhanced Genetic and Imperialist Competitive Based Algorithms for Reducing Design Feedbacks in the Design Structure Matrix

Nowadays, finding an appropriate interrelated activities sequence in designing complex engineering products, leading to efficient product development, is known as a major concern for managers. Recently, a variety of efforts aim to help the design managers tackle this issue, and one of the most useful management tools among them is the sequencing analysis method in an activity-based design structure matrix (DSM). The sequencing analysis method is the process of reordering the DSM rows and columns to minimize the detrimental effects of feedback loops in the design process. Therefore, in this article, an enhanced imperialist competitive algorithm (ICA), an enhanced genetic algorithm (GA), and a hybrid ICA–GA method are presented to find an activity sequence in DSM with a minimum total feedback length, which is a proper criterion for diminishing the detrimental effects of the feedback loops. To this end, we have improved the ICA and GA methods utilizing two techniques: applying the operators dynamically and tuning the main parameters adaptively. Subsequently, some experiments are designed to evaluate the proposed methods’ performance in terms of the cost value, computational time, and convergence rate on a remote sensing satellite DSM as a case study and eight other DSMs. The performance results demonstrate the superiority of ICA over two other methods. Eventually, an exhaustive comparison with the four well-known methods involving Antares, efficient-simple GA, insertion-based heuristic, and insertion-based simulated annealing illustrates the superiority of the ICA in large-scale problems, yielding the best-known solution in a reasonable computational time.

Heteroprotein complex coacervation of ovalbumin and lysozyme: Phase behavior, microstructure and processing properties

Egg white protein is a complex network system formed by the aggregation and interaction of multiple proteins, in which two oppositely charged proteins can induce the formation of heteroprotein complex coacervate (HPCC). This investigation aims to probe the complexation between ovalbumin (OVA) and lysozyme (LYS) via the heteroproteins phase diagram model, exploring the relationship between differential processing properties and complex state, protein molecular structure and intermolecular interactions. Results showed that the characteristic pH (pHc, pHɸ1, pHmax, pHɸ2) intensely relied on the protein ratios. Consequences of ζ-potential and the phase diagram manifested that OVA-LYS complex formation was predominantly triggered by electrostatic interactions. With the increasing OVA-LYS mixing ratio, the characteristic pH of the complex moved towards a higher pH value. By comparing the microphase structure of different phase regions induced by pH, we found that the protein structure unfolded at low pH, elevating the foaming and emulsifying capacity. Moreover, further increases in pH value brought on larger insoluble complex formation, which enhanced the foaming stability while decreased the emulsifying stability. Noteworthy, the coacervates obtained in the narrow range of pH 6.5–9.5 presented the strongest gel strength and highest water holding capacity, which was considered to be an efficacious way to regulate the gel properties of egg white protein. On the whole, the findings of this paper developed prominent insight for understanding the interaction between heteroproteins, which would lay a theoretical foundation for improving the quality and efficiency of functional egg powder and product development.

Bridging the Gap between Geometry and User Intent: Retrieval of CAD Models via Regions of Interest

CAD model retrieval is a key functionality for increasing the efficiency of product design and development. A major challenge for content-based retrieval solutions is to bridge the semantic gap between geometric information and design intent. Current retrieval solutions lack capabilities to tailor search results to user interpretation and application context. This work presents an interactive retrieval system on heterogeneous CAD model collections that leverages user-defined regions of interest (ROIs) to express design intent in an intuitive manner. Our proposed system uses self-supervised learning techniques to capture nuanced semantic shape relationships and employs non-exhaustive search techniques to perform large-scale partial shape matching in real-time. We report significant performance increases over global retrieval on three mechanical component datasets. We also outline the scalability of the approach to large datasets and multi-modal data.

Discussion on Key Control Points and Common Problems of Molecular Diagnostic Reagents in the Process of Design and Development

Design and development process of molecular diagnostic reagents is critical to quality management system of in vitro diagnostic reagent. Based on the technical characteristics of molecular diagnostic reagents, the study analyzed the concerned key control points and common problems in the process of design and development from the view of registration quality management system. It aimed at offering technical guidance on design and development process of molecular reagents and registration quality management system to enterprises, thus improving the product development efficiency, optimizing the quality management system, and increasing the efficiency and quality of registration and declaration.

Supporting product lifecycle collaboration and knowledge-related evaluation: an active-passive collaboration mechanism and fuzzy evaluation method

Collaboration is essential to improve the efficiency of product research and development (R&D), shorten the R&D cycle, and reduce the R&D costs in complex product lifecycle model management (CPLMM). However, disorganized processes and the unreliability of the result evaluation remain enormous challenges for efficient collaboration. This article proposes an active-passive collaboration mechanism to enable a regulated collaboration system, which can direct the self-organized collaboration of stakeholders. C-D-Petri Net is presented for the formal collaboration process modeling. The result evaluation in active-passive collaboration involves multi-source knowledge across disciplines and phases. To address the unreliable collaboration evaluation (Co-evaluation) caused by insufficient evaluation knowledge and weak correlation between expertise and evaluation task, the collaborative fuzzy comprehensive evaluation (CFCE) model is established to support Co-evaluation actions, and its core improvement lies in the definition and introduction of collaboration volume. Finally, a simulated aircraft horizontal tail control system is regarded as an engineering application case to demonstrate and verify the effectiveness of the proposed method.

Prediction of cutting forces in drilling of aluminum 6061-T6 alloy

Drilling is the most common machining operation and arguably the most crucial of the traditional machining processes. Cutting force predictions for every given set of cutting conditions are crucial for efficient product development. It has been hypothesized that the force produced during cutting is responsible for the majority of the issues seen in hole creating processes like drilling. Calculating forces accurately also aids in the development and assessment of cutting equipment and fixtures. The purpose of this article is to examine the effect of significant machining factors like thrust force and torque on the drilling operations of Aluminum 6061-T6 alloy. Theory and experiment will be compared with different types of drill bits. An average error of 5.81% was found in the Axial Force (N) and 0.85% was observed in Torque (N-cm). The error % of desired responses is found to be accepted.

A systematic review of knowledge management and new product development projects: Trends, issues, and challenges

This research aims to show that knowledge management is integral to business strategy and can lead to more efficient new product development in high-tech companies. Organizations have been increasingly focused on knowledge management methods as they have realized how important it is to manage knowledge to stay competitive in their marketplaces. Knowledge Management (KM) is responsible for a company's efficiency, effectiveness, and innovation. Project outcomes, individual outcomes, and organizational outcomes are linked to knowledge in New Product Development (NPD). More than 28,548 KM papers published in the previous 22 years were examined in this research using Scopus and Web of Science; the original sample was narrowed down to items that contributed to KM literature. The R Studio and VOS Viewer software executed the descriptive statistics and scientific mapping approaches using co-citation analysis. The descriptive analysis involved studying publishing patterns over time, the geographical localization of the contributing institutions, journals, the most prolific authors, the top-performing institutions, and the most-cited papers. Scholars and practitioners have been paying close attention to knowledge management and organizational performance in recent years. Once implemented, the integrated approach may significantly influence organizational processes and performance. This study examines both KM ideas in NDP initiatives. Several intriguing discoveries are presented, with extensive explanations of their future direction, a conceptual framework for the study, and practice based on the literature.

Construction of nano slow-release systems for antibacterial active substances and its applications: A comprehensive review.

At present, nano-carrier materials with antibacterial activity are of great significance. Due to the widespread resistance of many pathogenic microorganisms, it has seriously threatened human health. The natural antimicrobial substances extracted from fruits and vegetables can significantly improve their stability combined with nano-carrier materials. The resistance of pathogenic microorganisms will be substantially reduced, greatly enhancing the effect of active antimicrobial substances. Nanotechnology has excellent research prospects in the food industry, antibacterial preservation, food additives, food packaging, and other fields. This paper introduces nano-carrier materials and preparation techniques for loading and encapsulating active antibacterial substances in detail by constructing a nano-release system for active antibacterial substances. The antibacterial effect can be achieved by protecting them from adverse external conditions and destroying the membrane of pathogenic microorganisms. The mechanism of the slow release of the bacteriostatic active substance is also described. The mechanism of carrier loading and release is mainly through non-covalent forces between the bacteriostatic active substance and the carrier material, such as hydrogen bonding, π-π stacking, van der Waals forces, electrostatic interactions, etc., as well as the loading and adsorption of the bacteriostatic active substance by the chemical assembly. Finally, its wide application in food and medicine is introduced. It is hoped to provide a theoretical basis and technical support for the efficient utilization and product development of bacteriostatic active substances.

The Relationship between Structure and Performance of Different Polyimides Based on Molecular Simulations.

A polyimide (PI) molecular model was successfully constructed to compare the performance of PIs with different structures. In detail, the structure of the cross-linked PI resin, the prepolymer melt viscosity, and the glass-transition temperature (Tg) were investigated using molecular simulations. The results indicate that benzene ring and polyene-type cross-linked structures dominate the properties of the PIs. Moreover, the prepolymer melt viscosity simulations show that the 6FDA-APB and the ODPA-APB systems have a low viscosity. The results for the Tg and the distribution dihedral angle reveal that the key factor affecting bond flexibility may be the formation of a new dihedral angle after cross-linking, which affects the Tg. The above results provide an important reference for the design of PIs and have important value from the perspective of improving the efficiency of new product development.

A contradiction solving method for complex product conceptual design based on deep learning and technological evolution patterns

Contradictions caused by the various design constraints present increasing challenges to efficiency and innovation in product development. TRIZ provides Inventive Principles (IPs) and Contradiction Matrix that are the most frequently applied in conflict resolution. However, the high-level abstraction and subjective selection of IPs inhibit achieving the transformation process from paradoxical states to physical structures. To fill this gap, a contradiction solving method by integrating deep learning and technological evolution patterns for product conceptual design is proposed, which illustrates the mechanism of contradiction transition from the perspective of system evolution and supplies a systematic and model-based design approach. Firstly, generic engineering parameters are extracted to define the underlying contradictions transformed from critical defects which are found out through function modeling and root-conflict analysis. Then, a fully-connected deep neural network with excellent performance is developed to uncover the non-linear relationships between engineering parameters and evolution patterns. Finally, an evolution tree based on the predicted patterns is constructed to visualize transformation potentials of a technical system and help designers generate innovative specific solutions. In addition, a case study concerning design conflict resolution for beat-up system of three-dimensional tubular weaving machine is used to validate the adaptability and reliability of the proposed approach.

Home Automation using Android

Automation has become the key for smart homes, Implementation of such a system leads to energy conservation and also provides the user with a comfortable and convenient way to control their home appliances. Automation can also be employed to help disabled people with their daily tasks. This paper presents such a system which uses sensors to automate our home appliances. A connection to this system can be established via Bluetooth using applications developed with the Android operating system. Automation was done to reduce industrial accidents as and when people are get exposed to dangerous situations by the help of automation, we can have more effective and efficient product development, With the help of the automation, we can reduce manual labor and reduce the accidents associated with the high voltage devices. This project was developed to create simple but reliable and in expensive home automation model with the help of mobile phone. As the mobile phone has become irreplaceable, the mobile phone is used to control the appliances. With the help of mobile phone, home automation is done easily. A complete but a simple automation model using mobile phone is done in this project.

Top-Down Validation Framework for Efficient and Low Noise Electric-Driven Vehicles with Multi-Speed Gearbox

The shift towards e-mobility is resulting in new technological challenges. Thus, new, more efficient product development methods and a better product understanding are required. During product development, validation is essential both to achieve significantly increased knowledge of the system in question and to ensure that customers’ expectations of characteristics are met. Based on existing top-down validation approaches, this article discusses an innovative both-ends-against-the-middle-approach (BEATM) developed by the author. A validation framework which combines physical and virtual elements is presented. By way of example, a development approach for the toothing validation layer of an electric vehicle powertrain with a multi-speed gearbox is introduced.

Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms.

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.

The Role of PD Biomarkers in Biosimilar Development - To Get the Right Answer One Must First Ask the Right Question.

The potential for pharmacodynamic (PD) biomarkers to improve the efficiency of biosimilar product development and regulatory approval formed the premise for the virtual workshop Pharmacodynamic Biomarkers for Biosimilar Development and Approval hosted by the US Food and Drug Administration (FDA) and Duke Margolis, September 2021. Although the possibility of PD biomarkers replacing the to-date routine comparative phase III confirmatory study currently expected by the FDA was discussed, the motivation and feasibility for biosimilar sponsors developing such markers and the regulatory risks entailed largely were not. Even more fundamental is the already established greater comparative value of the pharmacokinetic (PK) study as the most sensitive clinical assay for detecting subtle differences between two products. Consequently, the comparative analytical assessment and the head-to-head PKs will have already answered the core questions as to the biosimilarity of the candidate product to its reference. No further actionable information is obtained with either a PD study or a comparative clinical phase III study even as they may provide some reassurance of what is already known. When a suitable PD biomarker is available for the originator reference product they have already been used for biosimilar development. We must carefully consider the core requirements and timelines inherent in biosimilar development and how they occur in parallel rather than in the series we see for originator products. In order to improve the efficiency of biosimilar development, we need to ask the right questions based on a full understanding of how biosimilars have been developed to date and can be in the future.

To Investigate the Failure in Sensor Assembly Due to Temperature Variational Loading, and to Provide Optimum Material Solution to Avert This Failure

<div class="section abstract"><div class="htmlview paragraph">Engineering, automotive and consumer products include several components, which are connected to each other for their operational purpose and thus form assemblies. Sensors, which are usually formed by assembling several components with each other, have become an integral part of the systems around the world. The components forming these assemblies are made up of various materials like polymers, metals, metal alloys, composites etc. They are connected to each other and then are usually over molded by a thermosetting material which firmly holds these components so that no relative motion is possible among them. Testing was done on a similar speed sensor assembly. When the assembly was mounted in the test rig and step wise temperature variations were applied on it to represent operational loads, failure observed in the assembly in the form of multiple cracks at different locations. This paper investigates the reasons for failure and provides optimal material solution to avert the failure. Initial testing was followed by Finite Element Analysis, which verified these multiple failure locations. By carefully observing the FE analysis results, it was identified that the reason for failure was the substantial difference between the expansion of certain components, particularly at high temperatures. With the extensive research in the area of thermosetting and thermoplastic materials, and with the subsequent parametric study with the help of FE analysis, the optimal material combination was found which in turn led to avert the failure. Subsequent testing with the above identified optimal material combination validated the results given by FE analysis and no further failure was observed. This work enabled in establishing efficient product development process by reducing multiple loops of testing.</div></div>

Critical success factors for open source innovation in pharma industry: learning from two case studies

PurposeThe study aims to add to the body of knowledge of open source tangible product management (also called open design). The objective is also to develop a guideline for efficient open source tangible product development and adoption.Design/methodology/approachThe exploratory research design using secondary data (like newspapers, magazines, research articles, bogs, papers, etc.) is used to analyze open source tangible product design challenges and enablers. The success stories of Open Source Software projects (OSS) were studied for identification of critical success factors and further their relevancy was tested in the two popular cases of open source drug discovery (malaria and tuberculosis)FindingsOpen innovation has become a part of competitive strategy of current businesses. It requires an efficient intellectual property protection regime for its implementation. However, in a market dominated by proprietary benefits, the open source technology development can serve as remedy for innovation needs of neglected sectors. The OSS literature revealed managing two classes of factors, namely technology sponsor level factors and environmental factors for efficiency and effectiveness. The case study analysis in the context of applicability of these OSS critical factors showed their limitations in open source tangible products, and highlighted understanding additional challenges and remedies.Research limitations/implicationsOpen source innovation is a collaborative effort involving inputs from various/diverse players, hence monitoring the effort and motivation level of the contributors is a cumbersome task. Only the information that is available online and in print media is taken as research inputs in this work. Also the data taken were from two case studies; a lot more case studies in the open design domain can progress the theory. The implications of this study are far-reaching in the areas where profit motivated proprietary efforts lack in addressing societal need. It provides guidelines for addressing those unmet needs by developing products in a collaborative way without intellectual property hurdles.Originality/valueThe essence of open design is becoming more vital, and there is a pressing need to build theory to support it, which still is elusive and dispersed. The study fills the gap using secondary data and case study approach.

Design of Brand Business Model Based on Big Data and Internet of Things Technology Application.

Appropriate business models are very important for enterprises. With the advent of the new century, the rapid development and application of technologies such as the Internet of Things and big data have become more and more widely used, which provides great convenience for people's daily life. With the rise and development of the Internet of Things and big data technology, human work and life have become easier and easier, and it has also greatly changed human production and development. Under the fierce market competition, enterprises pay more and more attention to the independent research and development capabilities of their own brand products. With the rapid development of the Internet age, the status of brands in the improvement of business models is also increasing. The brand business model design must not only meet the ever-changing market consumer demand but also conform to the ever-changing fashion trends. The article briefly analyzed the development and application of the Internet of Things and big data technology and optimized the design method of the brand business model by using big data technology in design of the brand business model. On this basis, a decision support method for a brand business model based on a data warehouse was proposed to achieve scientific, efficient, and accurate brand product development. In order to understand consumers' consumption habits and awareness of brands, the article randomly selected 400 people to conduct a survey. It was found that 45% of male respondents preferred commercials with simple and elegant colors; 38% of female respondents preferred commercials with minimalist styles. Modern consumers are mainly pragmatic, and the design of brand business models should be developed in this regard.

Anomaly Detection of Servomotors Subject to Highly Accelerated Limit Testing

Companies utilize highly accelerated limit testing (HALT) to ensure efficient product development by accelerating loading conditions in the qualification process. The aim of qualitative accelerated testing such as HALT is to properly identify early behavioral anomalies. To this end, this study utilizes machine learning techniques for detecting anomalies in servomotors in electronic products subjected to HALT. A case study was conducted using a programmable robot kit with 12 servomotors. HALT comprises five types of stress: thermal conditioning (cold and heat), rapid thermal change, vibration, and combined stresses. The anomalous behavior of a servomotor can be identified using a k-nearest neighbor algorithm and verified by inspection using the loading conditions and electrical responses. In addition, anomalous behaviors among servomotors and a control board are assessed using a Gaussian graph model approach. Changes in the Gaussian graph are assessed as anomaly scores using Kullback–Leibler divergence. The anomaly score increased earlier than the operating limit defined by inspection, i.e., the deviation from the initial position of the shaft. The machine learning algorithm successfully identified the failure precursor of the unit. The proposed approach of HALT with the machine learning algorithm supports prognostic health management of servomotors.

Numerically robust co-simulation using transmission line modeling and the Functional Mock-up Interface

Modeling and simulation are important tools for efficient product development. There is a growing need for collaboration, interdisciplinary simulation, and re-usability of simulation models. This usually requires simulation tools to be coupled together for co-simulation. However, the usefulness of co-simulation is often limited by poor performance and numerical instability. Achieving stability is especially hard for stiff mechanical couplings. A suitable method is to use transmission line modeling (TLM), which separates submodels using physically motivated time delays. The most established standard for tool coupling today is the Functional Mock-up Interface (FMI). Two example models in one dimension and three dimensions are used to demonstrate how the next version of FMI for co-simulation can be used in conjunction with TLM. The stability properties of TLM are also proven by numerical analysis. Results show that numerical stability can be ensured without compromising on performance. With the current FMI standard, this requires tailor-made models and custom solutions for the interpolation of input variables. Without using custom solutions, variables must be exchanged using sampled communication and extrapolation. In this case, stability properties can be improved by reducing communication step size. However, it is shown that stability cannot be achieved even when using unacceptably small communication steps. This motivates the need for the next version of FMI to include an intermediate update mode, where variables can be interchanged in between communication points. It is suggested that the FMI standard should be extended with optional callback functions for providing intermediate output variables and requesting intermediate input variables.

WHO informal consultation on revision of guidelines on evaluation of similar biotherapeutic products, virtual meeting, 30 June – 2 July 2021

The WHO informal consultation was held to promote the revision of WHO guidelines on evaluation of similar biotherapeutic products (SBPs) adopted by the Expert Committee on Biological Standardization (ECBS) in 2009. It was agreed in the past consultations that the evaluation principles in the guidelines are still valid, but a review was recommended to provide more clarity and case-by-case flexibility. The opportunity was therefore taken to review the experience and identify areas where the current guidance could be more permissive without compromising its basic principles, and where additional explanation could be provided regarding the possibility of reducing the amount of data needed for regulatory approval. The meeting participants applauded the leading role taken by the WHO in providing a much-needed streamlined approach for development and evaluation of SBPs which will provide efficient and cost-effective product development and increase patient access to treatments. It was recognized that the principles as currently described in the draft WHO guidelines are based on sound science and experience gained over the last fifteen years of biosimilar approvals. However, since these guidelines when finalised will constitute the global standard for biosimilar evaluation and assist national regulatory authorities in establishing revised guidance and regulatory practice in this complex area, it was felt that further revision and clarity on certain perspectives in specific areas was necessary to dispel uncertainties arising in the current revised version. This report describes the principles in the draft guidelines, including topics discussed and consensus reached.