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Open product architecture is a key enabler for product personalization, as it allows the integration of personalized modules in a product architecture to satisfy individual customer needs and preference. A critical challenge for integrating personalized modules into a product architecture is determining the optimal assembly architecture when considering market expectations and manufacturing constraints. In this paper, an optimization method is proposed for determining the personalized product design architecture that incorporates individual customer preferences. First, a decision hierarchy is presented to describe the integrated design decisions of the product architecture, including product variety determination, module variant selection, and personalized module configuration. Next, a profit model is formulated as an overall performance metric that incorporates customer preferences and manufacturing cost. The systematic patterns and randomness of diverse customer preferences are modeled by combining conjoint analysis and market segmentation with a multivariate normal mixture model. Individual customer product utilities in the target market and their product purchase intent probability are estimated through Monte-Carlo simulation, which is incorporated into the profit calculation. Manufacturing limitations on processes and materials are included as they influence manufacturer’s planning on candidate module variants and production strategies of personalized modules. These models are used to determine a product family architecture that maximizes profit by optimally determining its offering of product variants, module combinations, and personalized module configuration through a genetic algorithm. The proposed method is demonstrated by a personalized bicycle architecture design example.

With the rapid development of sensing, communication, computing technologies, and analytics techniques, today&rsquo, s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas.

Thermal warpage of a glass substrate after a flash lamp annealing process for crystallization of amorphous silicon thin-film is critical to subsequent fabrication processes for manufacturing large-scale displays. This phenomenon is understood through the structural mismatching of hypothetical two-layers in a homogeneous glass structure: thermally softened and uninfluenced, and is mechanistically different from that induced by the mismatch in coefficients of thermal expansions among heterogeneous thin-film structures. Thin subsurface region experiencing the intensified heating is softened, the stress in the region is relaxed, and finally the softened region is shrunk laterally, while uninfluenced part of the substrate remains as it is. The model assumes that the shrunk subsurface layer, of which the thickness is proportional to the thermal penetration, is bonded to the uninfluenced part completely. Due to structural mismatching the substrate is deformed considerably and warped towards the light source (the flash lamp) according to the curvature-flash duration relationship, γ = C2η(1 − C1η1/2). This relationship is confirmed by numerical simulation results based on a viscoelastic model and by experimental observations.

This article presents a novel technique to estimate the mechanical properties of the aluminum composite layer on silicon solar cells by using a hybrid 3-dimensional laser scanning force measurement (3-D LSFM) system. The 3-D LSFM system measures the material properties of sub-layers constituting a solar cell. This measurement is critical for realizing high-efficient ultra-thin solar cells. The screen-printed aluminum layer, which significantly affects the bowing phenomenon, is separated from the complete solar cell by removing the silicon (Si) layer with deep reactive ion etching. An elastic modulus of ~15.1 GPa and a yield strength of ~35.0 MPa for the aluminum (Al) composite layer were obtained by the 3-D LSFM system. In experiments performed for 6-inch Si solar cells, the bowing distances decreased from 12.02 to 1.18 mm while the Si layer thicknesses increased from 90 to 190 μm. These results are in excellent agreement with the theoretical predictions for ultra-thin Si thickness (90 μm) based on the obtained Al composite layer properties.

We present a 1-D heat transfer, melting, vaporization and resolidification model describing the interaction of a scanning continuous-wave laser with a metal surface wherein the beam power is constant. A physical model based on the Stefan problem is developed with appropriate boundary conditions. The effects of processing parameters on process variables are investigated numerically by varying beam diameters, scan speeds and substrate temperatures for Nickel. Relations are derived for the times to initiate melting, to initiate vaporization, to reach maximum melting depth, for melting-resolidification, and for maximum melting and vaporization depths. Surface temperatures are compared with approximate closed form solutions.

This paper investigates the unusual characteristics regarding the mechanical properties of Nylon-11 filled with different volume fractions of silica nanoparticles by selective laser sintering (SLS) from numerical simulation. The compressive modulus was predicted by two different numerical models and compared with the experimentally measured one. While the two-phase model has a limited capability in explaining the unusual behavior shown in the compressive modulus obtained by experiments with 2% volume fraction of nanoparticles, the effective interface model can simulate the unexpected characteristic of nanocomposites according to the volume fraction of nanoparticles. We can conclude that the effective interface model should be employed to predict the mechanical properties of nanocomposites for efficiency and accuracy.

A 532 nm Nd-YAG laser was applied to crystallize amorphous Si thin films in order to evaluate the applicability of a Nd-YAG laser to low-temperature polycrystalline Si technology. The irradiation of a green laser was controlled during the crystallization of amorphous Si thin films deposited onto glass substrates in a sophisticated process. Raman spectroscopy and UV-Visible spectrophotometry were employed to quantify the degree of crystallization in the Si thin films in terms of its optical transmission and vibrational characteristics. The effectiveness of the Nd-YAG laser is suggested as a feasible alternative that is capable of crystallizing the amorphous Si thin films.