Mass Customization Production Research Articles

Large-Scale High-Accuracy and High-Efficiency Phase Plate Machining.

In this paper, multifunctional, multilevel phase plates of quartz substrate were efficiently prepared by using a newly developed polygon scanner-based femtosecond laser photolithography system combined with inductively coupled discharge plasma reactive-ion etching (ICP-RIE) technology. The femtosecond laser photolithography system can achieve a scanning speed of 5 m/s and a preparation efficiency of 15 cm2/h while ensuring an overlay alignment accuracy of less than 100 nm and a writing resolution of 500 nm. The ICP-RIE technology can control the etching depth error within ±5 nm and the mask-to-mask edge error is less than 1 μm. An 8-level Fresnel lens phase plate with a focal length of 20 mm and an 8-level Fresnel axicon phase plate with a cone angle of 5° were demonstrated. The diffraction efficiency was greater than 93%, and their performance was tested for focusing and glass cutting, respectively. Combined with the high-speed femtosecond laser photolithography system's infinite field-of-view (IFOV) processing capability, the one-time direct writing preparation of phase plate masks of different sizes was realized on a 6-inch wafer. This is expected to reduce the production cost of quartz substrate diffractive optical elements and promote their customized mass production.

The Influence of the Assembly Line Configuration and Reliability Parameter Symmetry on the Key Performance Indicators

In the context of the demand for mass customization of products, a trade-off between highly efficient automated systems and flexible manual operators is sought. The linear arrangement of workstations made it possible to divide the process into many simple operations, which increases production efficiency, but also results in an increase in the number of workstations and a significant extension of the line. A human operator is usually treated as a quasi-mechanical object, and a human error is considered, similarly, as a failure of a technical component. However, human behavior is more complex and difficult to predict. A mathematical model of a new production organization is presented, including dividing the traditional production line into shorter sections or replacing the serial assembly line with a U-line with cells. Moreover, the reliability of operator and technical means are distinguished. Work-in-progress inventories are located between line sections to improve system stability. The stability of the assembly line is examined based on the system configuration and probabilistic estimates of human failure. The influence of the symmetry of reliability parameters of people on key performance indicators (KPI (headcount), KPI (surface) and KPI (Overall Equipment Effectiveness) is examined. KPI (solution robustness) and KPI (quality robustness) are also presented in order to evaluate the impact of a disruption on the assembly line performance. New rules for assigning tasks to stations are proposed, taking into account the risk of disruptions in the execution of tasks. For comparison of assembly problems, heuristic methods with newly developed criteria are used. The results show the impact of symmetry/asymmetry on assembly line performance and an asymmetric distribution of manual assembly times that is significantly skewed to the right due to human errors. On the assembly line, the effects of these errors are cumulative and lead to longer assembly times and lower KPIs.

Design and Development of a Flexible Manufacturing Cell Controller Using an Open-Source Communication Protocol for Interoperability

Flexible manufacturing cells provide significant advantages in low-volume mass-customization production but also induce added complexity and technical challenges in terms of integration, control, and extensibility. The variety of closed-source industrial protocols, the heterogeneous equipment, and the product’s manufacturing specifications are main points of consideration in the development of such a system. This study aims to describe the approach, from concept to implementation, for the development of the controller for a flexible manufacturing cell consisting of heterogeneous equipment in terms of functions and communication interfaces. Emphasis is put on the considerations and challenges for effective integration, extensibility, and interoperability. Scheduling and monitoring performed by the developed controller are demonstrated for a manufacturing cell producing microfluidic devices (bioMEMS) that consists of six workstations and a robot-based handling system. Communication between the system controller and the workstations was based on open-source technologies instead of proprietary software and protocols, to support interoperability and, to a considerable extent, code reusability.

Smart and user-centric manufacturing information recommendation using multimodal learning to support human-robot collaboration in mixed reality environments

The future manufacturing system must be capable of supporting customized mass production while reducing cost and must be flexible enough to accommodate market demands. Additionally, workers must possess the knowledge and skills to adapt to the evolving manufacturing environment. Previous studies have been conducted to provide customized manufacturing information to the worker. However, most have not considered the worker's situation or region of interest (ROI), so they had difficulty providing information tailored to the worker. Thus, a manufacturing information recommendation system should utilize not only manufacturing data but also the worker's situational information and intent to assist the worker in adjusting to the evolving working environment. This study presents a smart and user-centric manufacturing information recommendation system that harnesses the vision and text dual encoder-based multimodal deep learning model to offer the most relevant information based on the worker's vision and query, which can support human-robot collaboration (HRC) in a mixed reality (MR) environment. The proposed recommendation model can assist the worker by analyzing the manufacturing environment image acquired from smart glasses, the worker's specific question, and the related manufacturing document. By establishing correlations between the MR-based visual information and the worker's query using the multimodal deep learning model, the proposed approach identifies the most suitable information to be recommended. Furthermore, the recommended information can be visualized through MR smart glasses to support HRC. For quantitative and qualitative evaluation, we compared the proposed model with existing vision-text dual models, and the results demonstrated that the proposed approach outperformed previous studies. Thus, the proposed approach has the potential to assist workers more effectively in MR-based manufacturing environments, enhancing their overall productivity and adaptability.

Learning outcome evaluation in manual assembly

Mass customization and shorter product life cycles are causing ever more variants in production, especially in manual assembly. At the same time, more diverse personnel structures are emerging due to demographic change and labor shortages. This is causing different challenges to production managers, e.g., competence gaps. To meet these challenges, learning in manual assembly becomes increasingly important. The design of the learning process can only be improved by checking whether the processes fulfill their purpose. Various learning evaluation measures are described in general vocational education and competence development, but it is hard to select the right one for the learning process. This paper shows a procedure, how learning evaluation measures can be selected, and how they can measure learning progress. For this, a test person study was conducted to compare different learning evaluation measures and show their usability and advantages in manual assembly. The results support making learning in assembly easier to apply and controllable. In the long term, feeding back the results improves the learning process design.

Supporting decision-making of collaborative robot (cobot) adoption: The development of a framework

Collaborative robots (cobots) are emerging in manufacturing as a response to the current mass customization production paradigm and the fifth industrial revolution. Before adopting this technology in production processes and benefiting from its advantages, manufacturers need to analyze the investment. Therefore, this study aims to develop a decision-making framework for cobot adoption, incorporating a comprehensive set of quantitative and qualitative criteria, to be used by decision-makers in manufacturing companies. To achieve that objective, a qualitative study was conducted by collecting data through interviews with key actors in the cobot (or advanced manufacturing technologies) adoption decision process in manufacturing companies. The main findings of this study include, firstly, an extensive list of decision criteria, as well as some indicators to be used by decision-makers, some of which are new to the literature. Secondly, a decision-making framework for cobot adoption is proposed, as well as a set of guidelines to use it. The framework is based on a weighted scoring method and can be customizable by the manufacturing company depending on its specific context, needs, and resources. The main contribution of this study consists in assisting decision-makers of manufacturing companies in performing more complete and sustained decision analyses regarding cobots adoption.

Collective intelligence-driven 3D printing factory for social manufacturing: implementing a testbed for industrial application

ABSTRACT The emergence of 3D printing technology has imbued the mass customization production model with novel implications. Concurrently, investigations into social manufacturing (SocialM) and collective intelligence present a fresh challenge for the 3D printing industry in their pursuit of realizing customized mass production. However, there is still a lack of investigation on the technical implementation and application scenarios of SocialM, and it hinders the development of SocialM from theory to industrial application. To mitigate this gap, firstly a five-layer framework based on collective intelligence for the configuration of design-production-service integrated 3D printing factory is established, together with the key enabling techniques that support the configuration and operation of the factory from social interaction software perspective and cyber-physical-social interconnection perspective. Secondly, the running logic of the 3D printing factory is demonstrated, and it starts from order generation to order completion. Thirdly, a testbed of the 3D printing factory is built, which contains both social interaction software and physical production hardware environments, and a production order of a 3D printed robotic arm is used to verify the feasibility of the testbed and the configuration and operation theories of SocialM. The work in this paper provides technical solutions for the industrial application of SocialM.

Enhancing sustainability in mass customisation of consumer electronics with salvage value through supply chain financing

ABSTRACT The consumer electronics industry is crucial in the global shift toward sustainability. To meet customers’ personalised needs efficiently and reduce production costs, electronic product suppliers increasingly focus on Mass Customization (MC) and modular production. Implementing a buyback policy for unsold module products with salvage value is a sustainable practice that reduces waste. This study explores two buyback support financing tools to enhance sustainability in the industry, enabling successful MC production. We also investigate how the salvage value based on modularity level and the buyback policy impact operational and financing decisions in the Mass Customization Supply Chain (MCSC). The research reveals that a higher modularity level positively impacts supplier’s profits and optimal capacity levels under both financing strategies. Consequently, this study offers valuable insights into elevating sustainability practices and optimising operational and financing decisions in the context of MC within the consumer electronics industry.

Proposal of Industry 5.0-Enabled Sustainability of Product–Service Systems and Its Quantitative Multi-Criteria Decision-Making Method

In the wake of Industry 4.0, the ubiquitous internet of things provides big data to potentially quantify the environmental footprint of green products. Further, as the concept of Industry 5.0 emphasizes, the increasing mass customization production makes the product configurations full of individuation and diversification. Driven by these fundamental changes, the design for sustainability of a high-mix low-volume product–service system faces the increasingly deep coupling of technology-driven product solutions and value-driven human-centric goals. The multi-criteria decision making of sustainability issues is prone to fall into the complex, contradictory, fragmented, and opaque flood of information. To this end, this work presents a data-driven quantitative method for the sustainability assessment of product–service systems by integrating analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods to measure the sustainability of customized products and promote the Industry 5.0-enabled sustainable product–service system practice. This method translates the sustainability assessment into a multi-criteria decision-making problem, to find the solution that meets the most important criteria while minimizing trade-offs between conflicting criteria, such as individual preferences or needs and the life cycle sustainability of bespoke products. In the future, the presented method can extend to cover more concerns of Industry 5.0, such as digital-twin-driven recyclability and disassembly of customized products, and the overall sustainability and resilience of the supply chain.

Product family modeling technology for customized cosmetic packaging design based on basic-element theory

BackgroundAs the market demands change, SMEs (small and medium-sized enterprises) have long faced many design issues, including high costs, lengthy cycles, and insufficient innovation. These issues are especially noticeable in the domain of cosmetic packaging design. ObjectiveTo explore innovative product family modeling methods and configuration design processes to improve the efficiency of enterprise cosmetic packaging design and develop the design for mass customization. MethodsTo accomplish this objective, the basic-element theory has been introduced and applied to the design and development system of the product family. ResultsBy examining the mapping relationships between the demand domain, functional domain, technology domain, and structure domain, four interrelated models have been developed, including the demand model, functional model, technology model, and structure model. Together, these models form the mechanism and methodology of product family modeling, specifically for cosmetic packaging design. Through an analysis of a case study on men’s cosmetic packaging design, the feasibility of the proposed product family modeling technology has been demonstrated in terms of customized cosmetic packaging design, and the design efficiency has been enhanced. ConclusionThe product family modeling technology employs a formalized element as a module configuration design language, permeating throughout the entire development cycle of cosmetic packaging design, thus facilitating a structured and modularized configuration design process for the product family system. The application of the basic-element principle in product family modeling technology contributes to the enrichment of the research field surrounding cosmetic packaging product family configuration design, while also providing valuable methods and references for enterprises aiming to elevate the efficiency of cosmetic packaging design for the mass customization product model.

A product family-based supply chain hypernetwork resilience optimization strategy

In product manufacturing, the supply chain can be adversely affected by the occurrence of uncertain risk events. Optimizing supply chain resilience is a challenging multi-objective nonlinear programming problem. Due to the development of mass customization production modes, manufacturing enterprises are more concerned with the service supply capability for a given product family. However, optimizing supply chain resilience while considering the product family is neglected in existing works. To tackle it, this paper first establishes a product family-based supply chain hypernetwork, mapping the product bill of materials to the potential suppliers. Second, a product family-based supply chain hypernetwork resilience optimization strategy (PF-SCHROS) is proposed using an improved genetic algorithm. Experiments are carried out to compare the proposed strategy with the traditional methods including interior point method, sequential quadratic programming, and genetic algorithm. Results show that our approach greatly reduces the loss originating from supplier chain disruption and can adequately improve supply chain service capability in various disruption scenarios.

Return window decision for modular mass customization products in a two-echelon returnable supply chain

Return window decision for modular mass customization products in a two-echelon returnable supply chain

Etching-Free Wafer-Level Fabrication of Suspended Mesh-Type Nanoheaters

Recently, various strategies have been demonstrated to reduce the operating power of electrothermal devices by implementing a miniaturized Joule-heating-based heater. To operate the heaters with low power, the miniaturized heating element must be well-insulated by spacing it apart from the substrate. The conventional microfabrication methods for implementing small-size (e.g., sub-micrometer to micrometer scales) and suspended heater architecture have relied on the etching process [1], which requires accurate and delicate process control. Moreover, the etching process becomes more complicated to prevent damage to the delicate heater structures, hindering yield and reliability. Therefore, a trade-off has occurred between the reduction in power consumption (i.e., heater size reduction) and production cost (e.g., cost, time, and high-tech development).Prior to this research, we demonstrated the fabrication of suspended 1D nanoheaters by depositing a thin Au layer selectively on top of a suspended carbon nanowire and their applications as ultralow-power gas sensors [2, 3]. Although the suspended nano-sized carbon heater body was patterned without an etching process, the Si substrate had to be etched in an isotropic manner to form a built-in shadow mask to facilitate batch nanofabrication.Here, we present a novel wafer-level fabrication method of a suspended sub-micrometer-sized mesh-type heater without using any conventional etching process. The mesh structure is separated from the substrate by 7–8 μm through two-step UV exposure on a negative photoresist layer: the first exposure is executed with large energy for post-forming, and the second exposure forms a suspended structure at the surface of the photoresist with small UV energy. This monolithic polymer structure is then pyrolyzed and converted to a suspended glassy carbon mesh. The suspended mesh consists of sub-micrometer-sized grid-shaped lines (width 200~500 nm, grid size ~ 10 μm). A thin (~ 50 nm) metal film is deposited on the top of the mesh for use as a heater layer by a directional evaporation method. During the directional deposition, the metal deposited on the substrate in the shape of the suspended mesh. Therefore, as long as the photomask size of the metal coating is smaller than the entire mesh size, the metal layer on the substrate is disconnected. Consequently, suspended mesh-type heaters can be facilitated without the etching process. Furthermore, various heater shapes can be engraved according to the photomask patterns of the mesh structure and metal layer, enabling customized mass production. At this conference, the fabrication of the various types of mesh-type nanoheaters and their applications as gas sensors will be presented. Reference [1] Choi, K. W.; Lee, J. S.; Seo, M. H.; Jo, M. S.; Yoo, J. Y.; Sim, G. S.; Yoon, J. B. Batch-fabricated CO gas sensor in large-area (8-inch) with sub-10 mW power operation. Sens. Actuators, B Chem. 2019, 289, 153–159.[2] Kim, T.; Cho, W.; Kim, B.; Yeom, J.; Kwon, Y. M.; Baik, J. M.; Kim, J. J.; Shin, H. Batch Nanofabrication of Suspended Single 1D Nanoheaters for Ultralow‐Power Metal Oxide Semiconductor‐Based Gas Sensors. Small 2022, 2204078.[3] Cho, W.; Kim, T.; Shin, H. Thermal conductivity detector (TCD)-type gas sensor based on a batch-fabricated 1D nanoheater for ultra-low power consumption. Sens. Actuators, B Chem. 2022, 371, 132541.

Managing mass customization products with modular design for recycling in a closed‐loop supply chain

AbstractInvesting in the modular architecture design for mass customization (MC) products cannot only enable product variety, thereby improving MC ability to cope with consumer individual needs, but also increase the product interchangeability, thereby reducing the recycling and remanufacturing costs. This paper considers a closed‐loop supply chain (CLSC) with MC, in which the manufacturer sells the new, modular MC products via the retailer and also actively recycles the used ones for remanufacturing. We study the optimal modularity level for new MC products and the best recycling channel for used MC products in a CLSC. One centralized and two decentralized recycling decision models are formulated, and then the equilibrium solutions among proposed models are compared and analyzed. The government subsidy and channel leadership are further discussed in extension. Different from the traditional view that outsourcing the recycling activity to retailer is best because of its proximity to consumers, our results suggest that the manufacturer directly recycling used MC products can be optimal in terms of modularity level, recycling rate, supply chain profits, total social surplus, and decision‐making speed.

3D Baskı Teknolojisinin Çevre ve Ekonomi Bağlamında Kitlesel Özelleştirme ve Tedarik Zinciri Üzerindeki Yıkıcı Doğası

Today's production paradigm is shaped by mass production, where production processes and tools are standardized, expertise is not taken much into account, and the same products are produced either in batches or massively in small varieties. Production is carried out in factories in certain centers, and the products produced are often distributed all over the world. There is a need for a multi-agent supply chain network and supply chain management with the stages of delivering the products to the final consumer, starting from the pre-production stage. It has become clear that the need for a complex supply chain network by producing products from a single center is no longer economical and environmentally friendly. In recent years, consumers have started to think about the harmful effects of the products they buy. Changing customer needs and demands have also made companies consider the mass customization production model. Recently, three-dimensional printing technologies, also defined as additive manufacturing technology, have emerged as a new production technology. With this disruptive technology, production models and supply chain networks are subject to change and transformation. In this study, the environmental and economic benefits of 3D printing technology are analyzed in the context of mass customization production model and supply chains.

Supply chain integration in mass customization

AbstractNew technology such as 3D printing, robotic manufacturing, and the Internet of things can shorten the production time of mass customization (MC) products, enhance the self-design fun of consumers, and improve channel integration connectivity. In this study, we examine the impact of MC waiting time and self-design fun from the purchase of MC products on supply chain integration. We consider a supply chain consisting of one manufacturer, one retailer, and one customizer. The manufacturer produces and sells standardized products through the retailer, whereas the customizer sells customized products to consumers directly. The customizer can decide whether to integrate with the manufacturer (i.e., manufacturer–customizer integration) or with the retailer (i.e., retailer–customizer integration). We find that manufacturer–customizer integration can achieve a win–win–win outcome for all three supply chain members when the MC waiting time is within a moderate range and the probability of consumers being satisfied with the product quality is high, but retailer–customizer integration cannot achieve such an outcome, as the manufacturer will not be benefited if it does not participate in the integration. Furthermore, integration reduces the customizer’s investment in the level of self-design fun, which implies that the negative effect owing to the low level of self-design fun in the supply chain integration can be compensated by the competition mitigation effect owing to supply chain integration. With endogenized self-design fun, the achievability of a win–win–win outcome for all three supply chain members in the manufacturer–customizer integration will remain unchanged.

An Effective Solution Space Clipping-Based Algorithm for Large-Scale Permutation Flow Shop Scheduling Problem

The permutation flow shop scheduling problem (PFSP) is one of the most important scheduling types in the mass customization production with many real-world applications. It is also a well-known NP-hard problem, when the number of jobs increases, the difficulty of solving the problem exponentially increases. However, most of the reported algorithms have not analyzed the solution space and may search many useless solution spaces, which impedes these algorithms from effectively optimizing the large-scale PFSPs in reasonable computation time. To address large-scale PFSPs with more than 100 jobs, this article proposes a solution space clipping-based improved simulated annealing (SA) algorithm. First, inspired by Johnson’s rule, this article explores its essential principle and generalizes it to the general situation. Before the optimization algorithm is used to find the optimal solution, a preordering combination is performed on the processed jobs according to this extended rule to considerably clip the solution space. Second, a hybrid release strategy based on the Palmer algorithm is developed for the proposed algorithm. Then, some key operators of the SA algorithm are also improved. Finally, to verify the performance of the proposed algorithm, this work performs a set of comparative experiments with the-state-of-art methods on the part of the TA benchmark and VRF benchmark with more than 100 jobs. The experimental results show that the proposed method can achieve superior results compared to other algorithms. Furthermore, the performance of the algorithm is comprehensively analyzed, which confirms the effectiveness of the proposed method.

A Study on Optimal Location Selection and Semi-Finished Product Inventory Allocation in the Steel Industry

With the development of industrial production technologies and market economy, Postponement is increasingly being adopted by continuous production enterprise as a method that enables multi-product, mass customization production. In order to make use of the Postponement in manufacturing production enterprises need to achieve customized production and reduce enterprise costs. A modeling is conducted in the production process of Postponement in iron and steel enterprises in this paper. With the goal of minimizing total costs on a specific customer service level, mixed integer nonlinear programming and particle swarm algorithms are adopted for modeling and solving, to determine the optimal location and optimal semi-finished product inventory of PDP and CODP. Finally, taking an iron and steel enterprise as an example, the feasibility and effectiveness of the model and algorithm are verified. The study shows that the change of the optimal locations of PDP and CODP is affected by the change of customer service level and delay penalty coefficient, but the speed of change of the optimal location of PDP shows longer delays. In addition, the size of the capacity of semi-finished product inventory corresponding to CODP has a direct influence on whether the semi-finished product inventory corresponding to PDP participates in production, which in turn affects the optimal semi-finished product inventory on all levels. Through the analysis, it is found that the model constructed in this paper can better describe the overall situation and the influence relationship of the Postponement, and the study supplements the deficiency of the research on Postponement in the continuous manufacturing enterprises and enriches the content of the quantitative research on the Postponement.

Implementation of Demand Forecasting Module of ERP System in Mass Customization Industry—Case Studies

Mass customization production is a manufacturing process in which mass-produced products are modified according to specific customer needs. An example of such production is furniture production, where multiple options options for various components or features are available. Demand forecasting in mass customization industry is a difficult task for organizations and is both crucial for their profits and for market penetration. Unfortunately, the currently available tools are focused on more standard demand forecasts, which lack interpretable representation of information relevant for mass customization products. This is especially relevant for ERP systems, which are the backbone of modern manufacturing. We propose a forecasting method adapted to the needs of mass customization by focusing on demand prediction—not on finished products, but on key intermediate goods. Moreover, we provide a method for determining such goods. As an additional consequence we provide an implementation strategy that reduces the risk and time consumption when implementing forecasting in ERP systems. All the results are implemented as appropriate functionalities of a IFS Application ERP system.

New Product Development of a Robotic Soldering Cell Using Lean Manufacturing Methodology

With the advent of manufacturing in Industry 4.0 and consumer demand, there has been a trend of mass customization of products. This customization requirement can only be achieved through the flexibility of manufacturing processes that are tailored to meet the quality standards of customers and the large volume of production in a short time. The increase of the production capacity is achieved through the processes of industrial automation of the manufacture which maintains the increased efficiency for the series production. This study was based on the Design for Six Sigma methodology (DMADV—Define, Measure, Analyze, Design and Verify) in order to determine the soldering process characteristics and how the soldering process can be automatized. When planning the implementation of a collaborative robot in a workstation in the production plant, the following must be taken into account: steps in operations that require the most time for the worker and/or that represent factors of physical and moral overload for him; the use of adequate precision fixing devices, delimitation of work areas, sensors as well as spaces for connecting the workstation to the electrical, hydraulic/pneumatic network and constant cycle time. The proposed solution can improve the productivity of the process by integrating advanced robotics and smart devices into the soldering line.