Modular Product Research Articles

Upgradeable Mechatronic Systems - An Approach to determine changing Product Properties using Foresight

In light of the increasing complexity and dynamic of the market environment, there is a need to consider future requirements during the development of upcoming product generations. This applies in particular to modular product architectures as their elements are used in several products and over a longer period. To overcome this challenge and to develop more sustainable products, upgrading can be a solution. Based on identified options for action, this paper proposes an approach to determine changing product properties based on the Model of PGE – Product Generation Engineering. Depending on the potential of the individual characteristics of a product property in its future environment, the properties can be classified as static or dynamic time-dependent for later upgrade planning. The approach was applied in a case study and a research project. Its applicability and usability could be demonstrated. Results from the application of the approach can be used in development projects to define upgrade packages for the upcoming product generation or to optimize the modularization of future products.

Biometric Authentication and Correlation Analysis Based on CNN-SRU Hybrid Neural Network Model.

With the continuous development of computer technology, many institutions in society have higher requirements for the efficiency and reliability of identification systems. In sectors with a high-security level, the use of traditional key and smart card system has been replaced by the identification system of biometric technology. The use of fingerprint and face recognition in biometric technology is a biometric technology that does not constitute an infringement on the human body and is convenient and reliable. The biometric technology has been continuously improved, and the existing biometric technologies are based on unimodal biometric features. The unimodal biometric technology has its own limitations such as proposing single information and checking data affected by the environment, which makes it difficult for the technology to play its advantages in practical applications. In this paper, we use CNN-SRU deep learning to preprocess a large amount of complex data in the perceptual layer. The data collected in the perceptual layer are first transmitted to CNN convolutional neural network for simple classification and analysis and then arrives at the LSTM session to update again and optimize the screening to improve the biometric performance. The results show that the CNN-LSTM, CNN-GRU, and CNN algorithms show a decreasing trend in accuracy under the three error evaluation criteria of RMSE, MAE, and ME, from 0.35 to 0.07, 0.58 to 0.19, and 0.38 to 0.15, respectively. The recognition rate of multifeature fusion can reach 95.2%; the recognition efficiency of the multibiometric authentication system and accuracy rate has been significantly improved. It provides a strong guarantee for the regional standardization, high integration, generalization, and modularization of multibiometric identification system application products.

Effects of step-by-step line balancing in apparel assembly line

The mixed work assignment technique was applied to the modular production concept to solve the apparel assembly line balancing problem. Specifically, an algorithm was implemented to generate workstations by first classifying tasks into modules through an analysis of the manufacturing process and assigning grouped tasks in the single task–multiple workers and multiple tasks–single worker assignment methods. Then, worker assignment was sequentially performed considering the skill level of the worker. A simulation of producing 100 men’s shirts was performed. Owing to line balancing, the total production time was considerably lower than that reported in previous studies. The enhanced performance was attributable to the fact that unlike previous studies that limited the apparel assembly line balancing problem to worker assignment, line balancing was performed through both task and worker assignment in this study. The results demonstrated that task assignment must be considered in the design stage in the apparel assembly line balancing problem.

CONCEPTUALIZATION OF MODULAR PRODUCTS FOR THE PROSUMER. A DESIGN WORKSHOP

Modular design has proven to bring numerous advantages in the design and development of new products, but its relationship with the end user has been limited in practical scope. Previous studies analyze its applicability for prosumer users, but it has not yet been tested as a design method. This work presents a first application in a design workshop carried out during the conceptualization phase of a real project. The objective was to design a multifunction portable cart that complies with the characteristics of modular design and is focused on prosumers. This conceptualization was developed by designers and prosumers with the purpose of designing modular products for the prosumer. To do this, the key terms and the design method were explained to the participants. Subsequently, two group exercises were carried out to devise new applications and propose limitations and solutions. The workshop concludes with the individual conceptualization of the product, giving rise to 22 design concepts that were evaluated by the participants. Due to the lack of consensus, a second evaluation was carried out by the three coordinators of the workshop. The highest rated concept was fully evolved and developed, including manufacturing. The results offer conclusions on how the participants applied the method, what were the most applied characteristics of modular design, what manufacturing tools were proposed for prosumers and how to approach the method to non-specialized users. Keywords: design method, prosumer, modular design, workshop, conceptualization

Methodology for the Design of Interdisciplinary Modules for Mechatronic Modular Product Platforms

Advancing globalization and digitization are continuously changing the competitive conditions in the manufacturing industry. Globalization leads to an increasing number of competitors and results in the necessity for a stronger focus on individual customer needs. At the same time, digitization is leading to an expansion of mechanical products with components from the disciplines of electronics and software. This results in mechatronic products whose components generate a high degree of internal variety due to a large number of interdependencies, thus increasing the complexity of product development. By using modular product platforms, the variety of the products offered can be optimized. However, current approaches for developing modular product platforms are mainly designed for mechanical products, so that the consideration of interfaces between components from different disciplines is not taken into account comprehensively in module design. As a result, the consideration of further disciplines becomes necessary in module design in order to be able to optimize the internal and external variety of the products offered. Thus, this paper presents a concept for interdisciplinary module design in mechatronic modular product platforms. For this purpose, the individual components and their interfaces have to be identified and operationalized. Subsequently, the module design is optimized using a cluster algorithm.

Application of Reconfiguration Process for Matrix Manufacturing System in an Industrial Use Case

The increasing need for changeable and, at the same time, productive production systems in variant-rich series production has generated the concept of so-called matrix manufacturing systems (MMS). A MMS is a cyber-physical modular production system composed of autonomous process modules. Flexible interlinking results in order-specific material flows, which enable the simultaneous production of different products and variants in the same production system. Cyber-physical elements continuously optimize the material flows, ensuring high productivity.. Beyond this flexibility aspect, modularity enables deeper changes via so-called reconfigurations. Reconfiguration requirements arise from the need to change the range of functions or capacities as well as from changes in the arrangement or the replacement of working methods. This way, the four goals of production variability, quality, speed, and profitability can be optimized. In order to use the possibility of recurring reconfigurations, the so-called reconfiguration process for MMS was developed in previous work. Now, the process is applied in an industrial use case. The effort to apply the process to the use case is estimated, as well as the additional effort to enable reconfiguration capabilities of the system. Then, the reconfiguration process is run through two scenarios. This allows a statement about the resulting efforts and benefits. The interpretation of the results shows that recurring reconfigurations are possible via the process and that the resulting benefits exceed the efforts. The paper concludes with the outlook that further research should work out ways to automate functions of the reconfiguration process step by step. These can be designing, decision-making, and execution functions. This could lay the foundation for the autonomous execution of reconfigurations aimed at so-called fluid production systems.

De-novo transcriptome assembly and analysis of lettuce plants grown under red, blue or white light

Lettuce (Lactuca sativa) is grown in various parts of the world for use as a leafy vegetable. Although the use of light-emitting diode (LED) in controlled plant production systems has been successfully used to enhance nutritional quality and plant growth efficiently, the molecular basis of lettuce’s response to varying light spectra is not studied. Using next-generation sequencing, we have analyzed the transcriptomes of leaf lettuce (Lactuca sativa var. ‘New Red Fire’) grown hydroponically in a modular agricultural production system under three different types of LED lighting: red, blue, and white light. Illumina HiSeq sequencing platform was used to generate paired-end sequence reads (58 Gb raw and 54 Gb clean data) of the transcriptome of lettuce leaves exposed to varying light spectra. The de novo assembled final transcriptome contained 74,096 transcripts. Around 53% and 39% of the assembled transcripts matched to the UniProt and RefSeq RNA sequences, respectively. The validation of the differentially expressed transcripts using RT-qPCR showed complete agreement with RNA-Seq data for 27 transcripts. A comparison of the blue versus red light treatments showed the highest number of significantly differentially expressed transcripts. Among the transcripts significantly up-regulated in blue-light-exposed leaves compared to white-light-exposed leaves, ~ 26% were involved in the ‘response to stress’. Among the transcripts significantly upregulated under red light compared to white light, ~ 6% were associated with ‘nucleosome assembly’ and other processes, such as ‘oxidation–reduction process’ and ‘response to water deprivation’ were significantly enriched. Thus, the result from the current study provides deeper insights into differential gene expression patterns and associated functional aspects under varying light qualities.

Technical Trends and Competitive Situation in Respect of Metahuman—From Product Modules and Technical Topics to Patent Data

As a form of technological integration, metahuman has a significant influence on sustainable production because of its consistent technological evolution. However, few studies have provided insights into the technical assessment of metahuman by employing patents. In this paper, patent analysis is conducted to identify technological trends and the competitive situation in respect of metahuman from a product modularity perspective. First, we identify 17 highly relevant metahuman keywords by combining a literature analysis and an expert interview method and identify 42,256 patents from the Derwent Innovation Index (DII), thus improving the accuracy and validity of the data collection process. Then, metahuman product modularity is implemented using the function-behavior-structure (FBS) model, and seven technical topics are extracted from patents via latent Dirichlet allocation (LDA). Lastly, the procedure for identifying technology areas in respect of metahuman is improved by applying an optimized method to establish the connecting paths of product modules, technical topics, and patent data. The analysis results show that the development of metahuman technology can be divided into three periods. Different patent priority countries have distinctive competitive advantages and characteristics at the product module level. The findings of this study are intended to aid R&D enterprises and the government in formulating sustainable decision-making and promoting the development of the metahuman industry.

Experimental and numerical study of zero-gravity distillation units with infinite and finite reflux

Zero-gravity distillation (ZGD) is a small-scale distillation process, which offers high separation efficiencies and can be used as a part of modular production plants. Instead of gravity, capillary forces are utilized to realise the fluid flow. In this study, two experimental ZGD setups with different dimensions and materials were built. Furthermore, a modified ZGD unit with dual side heating and cooling was developed. Experiments were carried out with ethanol/water mixtures, both in the infinite reflux mode and with feed and product streams, while the temperature and concentration profiles were recorded under varied feed concentrations and volumetric flow rates. The experimental data was used to validate the process model comprising coupled momentum, heat and mass transfer equations for the liquid and vapour phases as well as heat conduction equations for the unit walls. The fluid dynamics was described using the hydrodynamic analogy concept. A good agreement between simulated and measured ethanol concentrations and temperatures was found. With the validated model, sensitivity studies on the influence of the feed flow rate and the metal foam porosity on the ZGD process were performed. It was found that the separation efficiency decreases with increasing flow rate and increases with lower porosity.

Integration of design structure matrix and modular function deployment for mass customization and product modularization: a case study on heavy vehicles

Integration of design structure matrix and modular function deployment for mass customization and product modularization: a case study on heavy vehicles

Spy&IAC enables specific capture of SpyTagged proteins for rapid assembly of plug-and-display nanoparticle vaccines

From modular vaccine production to protein assembly on nanoparticles, the SpyCatcher/SpyTag system provides a convenient plug-and-display procedure. Here, we established a general-purpose immunoaffinity chromatography (IAC) method for SpyTagged proteins (Spy&IAC). SpyTags are displayed on the surface of nanoparticles to induce high-affinity monoclonal antibodies, allowing the specific capture of the target protein. Taking the key core antigenic regions of two coronaviruses that are currently more threatened in the field of human and animal diseases, the nucleocapsid (N) protein of SARS-CoV-2 and the COE protein of porcine epidemic diarrhea virus (PEDV) as model proteins, a purification model with SpyTag at the N-terminal or C-terminal expressed in E. coli or mammalian cells was constructed. After the efficient elution of Spy&IAC, the final yield of several proteins is about 3.5–15 mg/L culture, and the protein purity is above 90 %. Purification also preserves the assembly function and immunogenicity of the protein to support subsequent modular assembly and immunization programs. This strategy provides a general tool for the efficient purification of SpyTagged proteins from different expression sources and different tag positions, enabling the production of modular vaccines at lower cost and in a shorter time, which will prepare the public health field for potential pandemic threats.

On the strategic use of product modularity for market entry

Abstract We model the conditions under which firms should enter the market with modular products that support multiple standards instead of an integral product that supports a single standard. Product modularity enables firms to broaden their portfolios and increase their chances of investing in the “right” technologies early in a technology cycle. Entry with integral products instead occurs later in the cycle. We test the model’s predictions on a sample of the Local Area Network industry entrants during the 1990s.

Process flexibility in the presence of product modularity: Does modularity help?

One of the primary goals of supply chain management is to reduce supply–demand mismatch (SDM). Product variety explosion is a common occurrence across industries and is one of the primary sources of demand uncertainty, resulting in SDM and the associated costs. Researchers and practitioners have investigated the role of process flexibility in addressing SDM caused by product variety. This study investigates the impact of product modularity on the benefits of process flexibility. It answers the critical question: “Does introducing modularity in product structure lead to reduced process flexibility requirements?”. As flexibility investment is costly, the reduced requirement in the presence of product modularity positively impacts financially constrained manufacturing firms (e.g., those belonging to the SME sector or start-up ecosystem). Two stochastic optimization problems are formulated, one with two products and one with multiple products. The results show that in the presence of product modularity, the optimal production policy handles demand uncertainty better and thus reduces SDM cost more than the integrated product case. Further, the need for investment in process flexibility decreases in the presence of modularity. In the multi-product formulation, the researchers investigate the d-chain process flexibility structure requirement. In the presence of product modularity, a 2-chain process flexibility structure is sufficient to almost achieve the performance of a full flexibility structure, in contrast to integrated product scenarios where at least 3 to 4-chains are required.

Barriers to the adoption of integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS): a literature review

PurposeScarcity of resources, ecological imbalance, global warming, rising energy prices and the ever-changing need for variety have attracted the government and manufacturers for sustainable development of the industries. The integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS) provides a solid platform for meeting both the customers’ variety needs and business sustainability requirements. Many organizations opted for ISGLSAMS, but still due to various barriers organizations are not able to fully implement ISGLSAMS. The purpose of this paper is to identify the barriers to the ISGLSAMS, so that a more sustainable industrial manufacturing system and industrial symbiosis can be developed.Design/methodology/approachA literature review, from the Web of Science and Google Scholar database, has been carried out to identify the various barriers to the implementation of ISGLSAMS in the entire value chain. A total of 168 research papers have been reviewed for identifying the ISGLSAMS barriers.FindingsThis paper elaborates the concept of the ISGLSAMS, its attributes and various barriers and contributes to a better understanding and successful implementation of ISGLSAMS to meet business’ sustainability and market performance goals in the entire value chain. The paper also projects the future research framework and directions for the ISGLSAMS, integrated sustainable-green-lean-six sigma-agile (ISGLSA) product and ISGLSA supply and value chain.Practical implicationsThe study contributes to a better understanding of ISGLSAMS’ barriers. The government, stakeholders and policymakers may plan the policy, road map and strategies to overcome the ISGLSAMS’ barriers. In-depth knowledge of subclauses of ISGLSAMS’ barriers will help the practitioners to overcome the ISGLSAMS’ barriers strategically. By overcoming the ISGLSAMS barriers, a more sustainable 7 Rs based market focused manufacturing system can be designed. This will also increase the opportunities to enhance the industrial ecology, industrial symbiosis and better recovery of the product, process and supply chain residual value. This will reduce the waste to the ecosystem.Originality/valueThis work has been carried out in search of a more sustainable manufacturing system, i.e. ISGLSAMS (which is 7 Rs based, i.e. 6 Rs of sustainability with 7th R, reconfiguration) to meet the customer variety needs along with sustainability in the ever-changing customer market. This study adds value to the practitioners to identify and prioritize the ISGLSAMS’ industry-specific barriers and design the solution for the more sustainable development of (1) industries, (2) the industrial symbiosis system and (3) the ISGLSA product, process, system and supply value chain with minimum resource consumption and environmental impact. The research also contributes to the (a) ISGLSAMS (b) ISGLSA supply chain (c) reconfigurable, sustainable and modular products and (d) redesign, recovery and refurbishing of the product to increase the product life cycle.

Artinian algebras and Jordan type

There has been much work on strong and weak Lefschetz conditions for graded Artinian algebras A, especially those that are Artinian Gorenstein. A more general invariant of an Artinian algebra A or finite A-module M that we consider here is the set of Jordan types of elements of the maximal ideal 𝔪 of A, acting on M. Here, the Jordan type of ℓ∈𝔪A is the partition giving the Jordan blocks of the multiplication map mℓ:M→M. In particular, we consider the Jordan type of a generic linear element ℓ in A1, or in the case of a local ring 𝒜, that of a generic element ℓ∈𝔪𝒜, the maximum ideal. We often take M=A, the graded algebra, or M=𝒜 a local algebra. The strong Lefschetz property of an element, as well as the weak Lefschetz property can be expressed simply in terms of its Jordan type and the Hilbert function of M. However, there has not been until recently a systematic study of the set of possible Jordan types for a given Artinian algebra A or A-module M, except, importantly, in modular invariant theory, or in the study of commuting Jordan types. We first show some basic properties of the Jordan type. In a main result we show an inequality between the Jordan type of ℓ∈𝔪𝒜 and a certain local Hilbert function. In our last sections we give an overview of topics such as the Jordan types for Nagata idealizations, for modular tensor products, and for free extensions, including examples and some new results. We also propose open problems.

Variation architecture for reducing unnecessary variants in modular product family design by domain mapping and variant-level planning

One of the major challenges in variety management of modular product families is to prevent continuously generated variants of design elements. This paper aims to provide guidance on how manufacturing companies can reduce a large number of variants that have already increased from the existing product architectures. This paper introduces a new concept of architecture named variation architecture ( VA) in which relationships between variants in the market, design, and production domains are arranged explicitly for planning variety of a modular product family. Since the VA includes two perspectives which are domain mapping and variant-level planning, it can help companies to systematically establish complex relationships between variants across the domains. This paper describes elements of the three domains, relationship types between elements, and four categories of relationship rules called management rules at the variant-level planning. A framework is proposed for reducing variants through the VA to demonstrate its applicability. In the case study, we apply the framework to a front chassis family having a large number of variants and show that the VA significantly reduces unnecessary variants compared to the currently being produced.

Modularity in design and production relationships: a field study in two automakers

Modularity-in-design (MID) and modularity-in-production (MIP) relationship has been gaining the attention of scholars and practitioners along the years. However, the literature does not specify how this connection occurs and its implications. This paper analyzes the relationship between MID and MIP within the automotive scenario and investigates its main technical and organizational implications. Through an abductive, matched pair case study carried out in two large automakers, the study gathered data from interviews, on-site observations, and field notes from all interactions within the plants. The paper indicates that (i) both automakers prioritize modular design before modular production, characterizing a product-related approach, and (ii) the investigated automotive companies have different approaches when developing MID. One automaker focuses on product functionalities to develop the modules; the other company dedicates efforts towards a MID-MIP relationship through the industrial condominium concept. Results indicate significant propositions to be tested regarding MID-MIP connection, with original insights from two make-to-order (MTO) automotive companies.

Design change propagation routing in the modular product

In product engineering, design changes are unavoidable because of the evolution of customer requirements and key technologies. Modularization has been extensively applied as an effective strategy for the development of complex products. In a modular product, although modules are supposed to be functionally independent, the design change in a module can still propagate to other modules because of the interfaces between modules. This propagation leads to numerous potential propagation paths and uncontrollable change in costs. Thus, it is valuable to consider the impact of the modular structure during the optimization of design change propagation. This research offers a glimpse into design change propagation routing in modular products to accelerate design change propagation within the modules. First, the relationships between components were determined to construct the product network model. Subsequently, an optimization model of the design change propagation path was established to minimize the change propagation intensity, which includes the importance of the components, change in cost, and path length. To decrease the number of modules that the design change propagates into, the correlation between the modules was explored as penalties, considering the dependencies of a module on the interface(s) that are defined from aspects of the topological characteristics, change in the propagation characteristics, and cost of design changes. A multiple-population genetic algorithm was developed to solve this model. Finally, a case study of the design change propagation routing of a potato lifter was implemented to expound on the utility and effectiveness of the proposed approach.

Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach

In this work, we design an elastic open radio access network (O-RAN) slicing for the Industrial Internet of things (IIoT). Due to the rapid spread of IoT in the industrial use-cases such as safety and mobile robot communications, the IIoT landscape has been shifted from static manufacturing processes towards dynamic manufacturing workflows (e.g., Modular Production System). But unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) with the device energy consumption and O-RAN slice isolation constraints. Second, we propose the intelligent O-RAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time-varying slicing demand. Furthermore, the proposed matching game within the actor-critic model training helps to enforce the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches.

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.