Explore the vast range of titles available.

Mass Customization examines the business opportunities, considerations, and challenges manufacturers in various industries must weigh before committing to the significant investment in machinery and software needed to go to mass customization. For manufacturers who decide that it's time to take the plunge, the author describes the proven methods and latest technologies for making mass customization work seamlessly and profitably on the factory floor. Mass customization -- the automated manufacturing of bespoke products, profitably combining the low unit costs of mass production with the flexibility of building custom products to order -- has been touted as the next big thing for more than a quarter of a century. Until recently, however, mass customization made only modest inroads in a few industries. Now, the convergence of new ICT and manufacturing technologies with traditional CNC technologies means that mass customization's moment has arrived for breaking out into a wide range of industries. Hans Kull is an engineer and mathematician who applies his expertise in combinatorial optimization, programming, and engineering to devising end-to-end automated solutions for mass customization, automating and optimizing all processes from bespoke parts supply, order processing, production, and waste minimization to packing and delivery. He shares with his readers practical lessons for making mass customization succeed, case studies from various industries, and an insiders vision of the business implications of mass customization's coming of age.

Optical metasurfaces consisting of two-dimensional nanostructures have rapidly developed over the past two decades thanks to their potential for use as optical components, such as metalenses or metaholograms, with ultra-compact form factors. Despite these rapid developments, major challenges for the commercialization of metasurfaces still remain: namely their mass production and use in real-life devices. A lot of effort has been made to overcome the limitations of electron beam lithography which is commonly used to fabricate metasurfaces. However, a breakthrough in mass production is still required to bring the cost of metasurfaces down into the price range of conventional optics. This review covers deep-ultraviolet lithography, nanoimprint lithography, and self-assembly-based fabrication processes that have the potential for the mass production of both cost-effective and environmentally friendly metasurfaces. We then discuss metalenses and future displays/sensors that are expected to take advantage of these mass-produced metasurfaces. The potential applications of mass-produced optical metasurfaces will open a new realm for their practical applications and commercialization.

Holographic photopolymers are a new technology to create passive diffractive optical elements by a pure laser interference recording. In this review, we explain the chemistry concepts of light harvesting in an interference pattern and the subsequent grating formation as chemical response. Using the example of the newly developed Bayfol® HX film we discuss the reaction-diffusion driven photo-polymerization process for an index modulation formation to create volume phase gratings. Further we elucidate the selection of monomer chemistry and discuss details of the recording conditions based on the concept of exposure dosage and exposure time. Influences ranging from high dosage recording to low power recording are explained and how to affect the desired diffraction efficiency. Finally, we outline and demonstrate the process to mass manufacturing of volume phase gratings.

Point‐of‐care (PoC) and organ‐on‐chip (OoC) devices represent promising microfluidic applications for in vitro analysis and miniaturized analytical studies, reducing the need for traditional animal‐based tests for drug discovery and toxicity studies. Using thermoplastics in microfluidic device manufacturing provides interesting functionalities for expansion of these devices into market. However, market growth requires manufacturing large quantities for low cost, which can be achieved using injection molding techniques. This work involves the design of a microfluidic device with different aspect ratio channels to compare injection molding (IM) and injection‐compression molding (ICM) processes, as well as the design and manufacturing of a metallic insert containing machined inverted microstructures. Injected parts are validated visually, dimensionally, and functionally. The differences between both techniques and two grades of cyclic olefin copolymer materials are analyzed to evaluate microfluidic device mass production feasibility concluding that although the machining process for inverted high aspect ratio microstructures is not mature yet, both IM and ICM processes allow the mass manufacturing of microfluidic devices in thermoplastic. Parts processed by ICM show better replicability of microfluidic structures and less internal stresses generate during the injection process than IM parts, highlighting the potential of this process to achieve thermoplastic microfluidic devices to market. A comparison between injection molding and injection‐compression molding techniques for the mass manufacturing of thermoplastic microfluidic devices is performed. This work presents the characterization of two interesting thermoplastics for microfluidic applications, the manufacturing and characterization of a metallic insert containing microfluidic structures and the injection and characterization of thermoplastic microfluidic devices.

One of the major obstacles standing in the way of a break-through in fuel cell technology is its relatively high costs compared to well established fossil-based technologies. The reasons for these high costs predominantly lie in the use of non-standardized components, complex system components, and non-automated production of fuel cells. This problem can be identified at multiple levels, for example, the electrochemically active components of the fuel cell stack, peripheral components of the fuel cell system, and eventually on the level of stack and system assembly. This article focused on the industrialization of polymer electrolyte membrane fuel cell (PEMFC) stack components and assembly. To achieve this, the first step is the formulation of the requirement specifications for the automated PEMFC stack production. The developed mass manufacturing machine (MMM) enables a reduction of the assembly time of a cell fuel cell stack to 15 minutes. Furthermore the targeted automation level is theoretically capable of producing up to 10,000 fuel cell stacks per year. This will result in a ~50% stack cost reduction through economies of scale and increased automation. The modular concept is scalable to meet increasing future demand which is essential for the market ramp-up and success of this technology.

Additive manufacturing (AM) has transformed mass customisation by allowing personalised production with remarkable efficiency. This systematic review compiles findings from 61 peer-reviewed articles (2010–2024) to highlight strategies for implementation, technological facilitators, challenges, industry applications, and evaluation frameworks relevant to mass customisation in AM contexts. Utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the review applies stringent inclusion criteria and thematic analysis to create an in-depth understanding of this developing area. Four major strategies for implementation have been identified: combining AM with conventional manufacturing, integrating customer-centred design, establishing flexible manufacturing networks, and creating adaptive production systems. Key technological facilitators include capabilities for multi-material processing, integration of digital workflows, and advanced monitoring of processes, while obstacles consist of limitations in materials, challenges in quality assurance, and complexities related to digital asset management. Industry applications reveal tailored approaches specific to medical, industrial, and architectural sectors. This analysis presents a multi-tiered implementation framework encompassing strategic, tactical, operational aspects and performance evaluation aspects to assist organisations in embracing AM-based mass customisation. This framework fills a notable gap in existing literature by aligning personalisation goals with operational efficiency. This paper also outlines future research priorities, such as creating standardised evaluation methods, improving system reliability, incorporating sustainability, and leveraging emerging tools like AI for process improvement. Ultimately, this review bridges theory and practice, offering a clearer path forward for mass customisation in the era of AM.

Computational intelligence paradigms can be used for advanced manufacturing system optimisation. A static simulation model of an advanced manufacturing system was developed in order to simulate a manufacturing system. The purpose of this advanced manufacturing system was to mass-produce a customisable product range at a competitive cost. The aim of this study was to determine whether this new algorithm could produce a better performance than traditional optimisation methods. The algorithm produced a lower cost plan than that for a simulated annealing algorithm, and had a lower impact on the workforce.

Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning and optimization to minimize time and cost across a wide variety of choices in large production volumes. While soft computing techniques are widely used for optimizing mass-customization products, they face scalability issues when handling large datasets and rely heavily on manually defined rules, which are prone to errors. In contrast, machine learning techniques offer an opportunity to overcome these challenges by automating rule generation and improving scalability. However, their full potential has yet to be explored. This article proposes a machine learning-based approach to address this challenge, aiming to optimize both the supply and manufacturing planning phases as a practical solution for industry planning or optimization problems. The proposed approach examines supervised machine learning and deep learning techniques for manufacturing time and cost planning in various scenarios of a large-scale real-life pilot study in the bicycle manufacturing domain. This experimentation included K-Nearest Neighbors with regression and Random Forest from the machine learning family, as well as Neural Networks and Ensembles as deep learning approaches. Additionally, Reinforcement Learning was used in scenarios where real-world data or historical experiences were unavailable. The training performance of the pilot study was evaluated using cross-validation along with two statistical analysis methods: the t-test and the Wilcoxon test. These performance evaluation efforts revealed that machine learning techniques outperform deep learning methods and the reinforcement learning approach, with K-NN combined with regression yielding the best results. The proposed approach was validated by industry experts in bicycle manufacturing. It demonstrated up to a 37% reduction in both time and cost for orders compared to traditional expert estimates.

Numerous artificial intelligence (AI) techniques are currently utilized to identify planning solutions for supply chains, which comprise suppliers, manufacturers, wholesalers, and customers. Continuous optimization of these chains is necessary to enhance their performance. Manufacturing is a critical stage within the supply chain that requires continuous optimization. Mass Customization Manufacturing is one such manufacturing type that involves high-volume production with a wide variety of materials. However, genetic algorithms have not been used to minimize both time and cost in the context of mass customization manufacturing. Therefore, we propose this study to present an artificial intelligence solution using genetic algorithm to build a model that minimizes the time and cost which associated with mass customized orders. Our problem formulation is based on a real-world case, and it adheres to expert descriptions. Our proposed optimization model incorporates two strategies to solve the optimization problem. The first strategy employs a single objective function focused on either time or cost, while the second strategy applies the multi-objective function NSGAII to optimize both time and cost simultaneously. The effectiveness of the proposed model was evaluated using a real case study, and the results demonstrated that leveraging genetic algorithms for mass customization optimization outperformed expert estimations in finding efficient solutions. On average, the evaluation revealed a 20.4% improvement for time optimization, a 29.8% improvement for cost optimization, and a 25.5% improvement for combined time and cost optimization compared to traditional expert optimization.

Purpose Global economic growth provides new opportunities for the development of clothing enterprises, but at the same time, the rapid growth of clothing customization demand and the gradual increase of clothing costs also pose new challenges for the development of clothing enterprises. In this context, 3D printing technology is injecting new vitality and providing a new development direction for the vigorous development of clothing enterprises. However, with the application of 3D printing technology, more and more clothing enterprises are facing the problem of business model innovation. In view of the lack of relevant research, it is necessary to carry out exploratory research on this issue. Design/methodology/approach The business model canvas method was adopted to design business model for clothing enterprises using 3D printing. The simulation model of the designed business model was constructed by a system dynamics method, and the application of the designed business model was analysed by a scenario simulation. Findings Mass selective customization-centralized manufacturing (MSC-CM) business model was constructed for clothing enterprises using 3D printing, and a static display was carried out using the BMC method. A dynamic simulation model of the MSC-CM business model was constructed. The future scenario of clothing enterprises using 3D printing was developed, and a simulated enterprise was analysed. The results show that the MSC-CM business model has a good application value. The simulation model of the MSC-CM business model performs the function of a business strategy experiment platform and also has a good practical application value. Research limitations/implications The MSC-CM business model is only a typical business model for clothing enterprises using 3D printing. It is necessary to further develop other business models, and some elements of the MSC-CM business model need to be further improved. In addition, the MSC-CM business model simulation uses a general model, which is not suitable for all clothing enterprises using 3D printing. When the model is applied, the relevant enterprises can further adjust and optimize it, thereby improving the validity of the simulation model. Originality/value To the best of the authors’ knowledge, this is the first paper on the MSC-CM business model for garment enterprises using 3D printing. Secondly, it is the first time that the business model of clothing enterprises using 3D printing has been simulated. In particular, the proposed business model simulation provides the possibility for testing the business strategy of clothing enterprises using 3D printing. In addition, a positive attempt has been made in the collaborative research of using both a static display business model and a dynamic simulation business model.