Multifunctional intelligent boom sprayer is a development direction of boom sprayers, and it could satisfy needs of precision and variable spraying and achieve aims of sustainable development of pesticide spraying, ecological protection, and efficient plant protection by reconfiguring its structures. Therefore, the evaluation of multifunctional intelligent boom sprayer reconfigurability has a role in improving product reconfigurability while reducing product function redundancy and production costs and reducing product function redundancy and production cost. In order to comprehensively evaluate the reconfigurability of a multifunctional intelligent boom sprayer, this paper established an evaluation index system and realized the evaluation process by using fuzzy comprehensive evaluation, analytic hierarchy process, and validity test. The evaluation result would avail to directly and effectively guide product optimization and upgrade process in accordance with quantitatively analyzed evaluation indexes of intelligent boom sprayer reconfigurability by using mathematical methods such as analytic hierarchy process and validity test in evaluation process. The index system was formed from six components, which were key design information, quality, cost, efficiency, intelligence, and operational capability. Index weights were calculated by the analytic hierarchy process. Finally, a multifunctional shiftable boom intelligent sprayer was evaluated for product reconfigurability improvements by using the evaluation method. Reconfigurability evaluation result of the multifunctional shiftable boom intelligent sprayer belonged to "good" evaluation interval by the calculation of formulas. There was further room to improve product reconfigurability, as the membership degree in the “good” evaluation interval was below 0.5. The study could provide a scientific basis for improving the reconfigurability, technical performance, and operation quality of products in intelligent boom sprayer design. In addition, it may be possible to decrease the arbitrary nature of subjective design factors and support economic and ecological goals when businesses upgrade products.

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BACKGROUND Accurate pesticide inline mixing uniformity (PIMU) evaluation for direct nozzle injection systems (DNIS) helps evaluate system performance and develop efficient inline mixers. Based on supervised machine learning (ML), inline mixing images and computational fluid dynamics (CFD) simulations are directly associated for realizing intelligent PIMU predictions. RESULTS Image sets can be reduced to less than 3% of the data size at the same time as retaining 98% of information using principal component analysis (PCA). The CFD results, as referenced values for ML, were justified by mixture sampling experiments. Enhanced images for the long‐mixing tube effectively trained models including generalized linear model (GLM), support vector regression (SVR), BP‐neural network (NNW), and classification and regression trees (CART). By testing the re‐collected images, the verification accuracy of GLM was less than 95% and it failed to recognize uniformity differences under varying working conditions, whereas NNW, CART and SVR realized it with an accuracy for NNW and CART higher than 97% and for SVR slightly lower than 97%. By testing images of the jet mixer, the prediction accuracy compared with the CFD results of NNW and CART was also higher than 97%, although that for SVR was relatively lower, and insignificant declines in accuracy were observed on comparing the results with mixture sampling experiments. CONCLUSION PCA facilitates evaluations of CFD‐referenced PIMU using image‐based ML. Models trained by enhanced image sets of the long‐mixing tube have satisfactory performance. NNW and CART performed slightly better than SVR, and they can be used as tools to improve the rationality when evaluating PIMU in DNIS. © 2022 Society of Chemical Industry.

This paper discusses product family development on a flexible product platform. It firstly analyses furniture product market positioning,product type and future trends, then, by depth analysis of furniture product market development trends, draws the predictable part in the development trends to guide the design of furniture product,at the same time, analyses potential consumer needs and trends in unpredictable direction to identify future design attributes and design parameters, creates a design parameter matrix to obtain main design parameters,through the main parameters mapped to the physical structure (shape and structure of furniture product),and finds out core shape and furniture structure. By above analysis of a flexible product platform,based on this platform, combining the optional elements with variant design elements, we can design a series of furniture product family.