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Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of things (IOT) in the job shop, there is an increasing need for handling manufacturing BD for predictive manufacturing. In this study, we conceive the jobs remaining time (JRT) prediction during manufacturing execution based on deep learning (DL) with production BD. We developed a procedure for JRT prediction that includes three parts: raw data collection, candidate dataset design and predictive modelling. First, the historical production data are collected by the widely deployed IOT in the job shop. Then, the candidate dataset is formalised to capture various contributory factors for JRT prediction. Further, a DL model named stacked sparse autoencoder (S-SAE) is constructed to learn representative features from high dimensional manufacturing BD to make robust and accurate JRT prediction. Our work represents the first DL model for the JRT prediction at run time during production. The proposed methods are applied in a large-scale job shop that is equipped with 44 machine tools and produces 13 types of parts. Lastly, the experimental results show the S-SAE model has higher accuracy than previous linear regression, back-propagation network, multi-layer network and deep belief network in JRT prediction. [ABSTRACT FROM AUTHOR]

The effectiveness and efficiency of a content-based three-dimensional shape search system are investigated for supporting the re-use of engineering designs. Search effectiveness is characterized by precision and recall values from repeated search experiments. We extract four feature vectors from each three-dimensional shape and compare them for effectiveness. The performance of an R-tree-based index structure is also evaluated to characterize search efficiency. Search efficiency is evaluated by the ratio of the number of visited nodes in a search operation to the number of nodes in a database index. A multi-step refinement approach is proposed to improve search effectiveness. Based on the results of the experiments with a database of real models, the effectiveness of using multi-step refinement is predicted to be 51% higher than that of one-shot search using a single feature vector, although the difference becomes smaller when the number of retrieved shapes is larger. The R-tree index significantly improves the efficiency of our search system. Search efficiency decreases with the dimensionality of the data records and the capacity of database nodes. Based on our experiments with the synthetic database, the efficiency is predicted to be stable when the size of a database increases. [ABSTRACT FROM AUTHOR]

In this investigation an all-olefin thermoplastic laminate was developed and characterized. Commingled glass-fiber polypropylene (PP) composite was used as skin and HDPE (PE) foam with closed cells as core. Infra-red heating was used for melting the surfaces of the substrates for surface fusion bonding with a cold press. Two tie-layer films, viz., ethylene-propylene copolymer (EPC) and HDPE/elastomer blend were used as hot-melt adhesives for bonding the substrates. Singlelap shear joints were prepared from PP composite and PE foam adherends with a bonding area of 25.4 mm × 25.4 mm to determine the bond strength. EPC tie-layer adhesive provided higher bond strength (2.68 × 10[sup 6] N/m[sup 2]) to the all-olefin laminate than that based on HDPE/elastomer blend (1.93 × 106 N/m[sup 2]). For EPC tie-layer-based laminates, a mixed mode of failure was observed in the failed lap shear samples: about 40% was cohesive failure through the tie-layer, and the rest of failure was interfacial, either at PP composite or PE foam surfaces. Environmental scanning electron micrographs (ESEM) revealed that in the process of surface fusion bonding, PE foam cells in the vicinity of interphase (800-μm-thick) were coalesced with high temperature and pressure. No macro-level penetration of the tie-layer melt front into the foam cells was observed. As the surface morphology of foam was altered due to IR surface heating and the PP composite bonding side had a resin-rich layer, the bonding situation was closer to that between two polymer film surfaces. [ABSTRACT FROM AUTHOR]

Moisture durability of four low modulus adhesives was examined. The four low modulus adhesives all had the same basic moisture cure polymer but contained different fillers and additives. Hot-dipped galvanized steel was bonded to random fiber-reinforced unsaturated polyester and aged in two moisture environments for various durations. Adherend surface wipes included acetone, isopropyl alcohol and a typical surface contamination for galvanized steel (an organic lubricant). Diffusion coefficients, moisture uptake and modulus changes due to moisture environment were determined for the adhesives and the fiber-reinforced plastic (FRP). The moisture cure adhesive with clay and poly(vinyl chloride) (PVC) as fillers (adhesive 252) had the highest retained lap joint strengths. Initially, adhesive 252 had single lap joint strengths of 1.47 ± 0.08 MPa for acetone-wiped joints and 1.39 ± 0.33 MPa for organic lubricant-wiped joints. After accelerated aging in a cataplasma environment for 9 weeks, lap joint strengths fell to 0.61 ± 0.08 MPa for acetone-wiped joints and to 0.65 ± 0.11 MPa for organic lubricant-wiped joints. Environmental scanning electron microscopy (ESEM) and energy-dispersive spectrometry (EDS) showed that the actual failure locus was through a corroded zinc layer and between the adhesive and the zinc surface after aging. Dynamic mechanical analysis (DMA) showed that the modulus for adhesive 252 dropped from 21.7 to 13.9 MPa after cataplasma aging. From finite element analysis (FEA), this modulus drop corresponded to a drop in normal stress concentration from 0.75 to 0.57, and a drop in shear stress concentration from 1.41 to 1.36 at a point 0.5 mm from the end of the single lap joint overlap. [ABSTRACT FROM AUTHOR]