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Virtual assembly process plays an important role in assembly design of complex product and is typically time- and resource-intensive. This paper aims to investigate a dynamic assembly simplification approach in order to demonstrate and interact with virtual assembly process of complex product in real time.

The proposed approach regards the virtual assembly process of complex product as an incremental growth process of dynamic assembly. During the growth process, the current-assembled-state assembly model is simplified with appearance preserved by detecting and removing its invisible features, and the to-be-assembled components are simplified with assembly features preserved using conjugated subgraphs matching method based on an improved subgraph isomorphism algorithm.

The dynamic assembly simplification approach is applied successfully to reduce the complexity of computer aided design models during the virtual assembly process and it is proved by several cases.

A new assembly features definition is proposed based on the notion of “conjugation” to assist the assembly features recognition, which is a main step of the dynamic assembly simplification and has been translated into conjugated subgraphs matching problem. And an improved subgraph isomorphism algorithm is presented to address this problem.

One of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model.

The proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based on a priori algorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed.

The performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273.

This paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.

Design is a time-consuming process for mechanical production. Some design structures frequently occur in different products and can be shared by multiple assembly models. Thus, identifying these structures and adding them to a design knowledge library significantly speed up the design process. Most studies addressing this issue have traditionally focused on part models and have not extended to assembly models. This paper aims to find a method for common design structure discovery in assembly models.

Computer-aided design models have a great deal of valuable information defined by different designers in the design stages, especially the assembly models, which are actually carriers of information from multiple sources. In this paper, an approach for discovering a common design structure in assembly models is proposed by comparing information from multiple sources. Assembly models are first represented as attribute connection graphs (ACGs), in which we mainly consider topological information and various attributes of parts and connections. Then, we apply a K-means clustering method based on a similarity analysis of different attributes to classify the parts and connections and transform ACGs of assemblies into type code graphs (TCGs). After this, a discovery algorithm that improves upon fast frequent subgraph mining is used to identify common design structures in assemblies.

A new method was developed for discovering common design structures in assembly models, considering the similarity of information from multiple sources and allowing some differences in the details to keep both commonalities and individualities of common design structures.

Experiments show that the proposed method is efficient and can produce a reasonable result.

This discovery method helps designers find common design structures from different assembly models and shorten the design cycle. It is an effective approach to build a knowledge library for product design that can shorten the design cycle.

Layered manufacturing (LM) or rapid prototyping is a process in which a part is produced using layer‐by‐layer addition of the material. In LM, slicing of the CAD model of a part to be produced is one of the important steps. Slicing of CAD model with a very small slice thickness leads to large build time. At the same time if large slice thickness is chosen, the surface finish is very bad due to staircasing. These two contradicting issues namely reduction in build time and better surface quality have been a major concern in laminated manufacturing. This contradiction has led to the development of number of slicing procedures. The present paper reviews various slicing approaches developed for tessellated as well as actual CAD models.

The advancements of deep learning (DL) models demonstrate significant performance on accurate pancreatic tumor segmentation and classification.

The presented model involves different stages of operations, namely preprocessing, image segmentation, feature extraction and image classification. Primarily, bilateral filtering (BF) technique is applied for image preprocessing to eradicate the noise present in the CT pancreatic image. Besides, noninteractive GrabCut (NIGC) algorithm is applied for the image segmentation process. Subsequently, residual network 152 (ResNet152) model is utilized as a feature extractor to originate a valuable set of feature vectors. At last, the red deer optimization algorithm (RDA) tuned backpropagation neural network (BPNN), called RDA-BPNN model, is employed as a classification model to determine the existence of pancreatic tumor.

The experimental results are validated in terms of different performance measures and a detailed comparative results analysis ensured the betterment of the RDA-BPNN model with the sensitivity of 98.54%, specificity of 98.46%, accuracy of 98.51% and F-score of 98.23%.

The study also identifies several novel automated deep learning based approaches used by researchers to assess the performance of the RDA-BPNN model on benchmark dataset and analyze the results in terms of several measures.

The purpose of this study is to investigate how small and medium sized subcontracting firms evolve.

A cluster analysis was applied to a sample of 417 North East Italian subcontractors to explore if (and to what extent) Italian subcontractors differ and can be classified on the basis of their design and marketing capabilities. Using this classification as a starting point, multiple case study analysis is conducted on a sample of ten subcontractors and a model developed of how subcontractors' capabilities evolve over time.

Four profiles of subcontractors are identified as a function of their design and marketing capabilities: developed, developing, question mark and traditional. A model is proposed to understand and predict subcontractors' evolution. In the model knowledge codification, supply management, design and marketing capabilities mutually reinforce one another and tend to align over time.

Firstly, future research should articulate the four clusters identified. Secondly, the framework for subcontractors' evolution should be tested on large‐scale databases. Thirdly, more accurate measures of subcontractors' capabilities should be conceived and tested.

Results of this study are critical for industrial buyers who need to segment their subcontractors and understand how their marketing and design capabilities evolve. Moreover, they are also critical for subcontractors' managers who wish to avoid cost‐based strategies, enlarge their customer base, broaden their international scope and engage in durable relationships with their customers.

This study proposes an original model of subcontractors' classification and evolution and suggests good practices to design and manage supply networks.

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually.

There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted.

This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity.

The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.

The OPTIC (Optical Product and Technical Information for Construction) multimedia system is being developed as an aid to specifiers in the construction industry. It is based on the European EPIC (European Product Information Cooperation) project to develop a new classification scheme for the building products used in construction and civil engineering, and which also has relevance to the building product modelling projects currently underway. This classification scheme is used as a basis for the system, which displays textual and graphical information on construction products. Two sorts of image are shown for each product — a picture of the product in use and a CAD image. Information on the manufacturers producing this project is also given; hyperlinks connect the system. Although still at the development stage, OPTIC will eventually be published on CDROM.

This paper aims to argue about the involvement of additive technologies (ATs) in the prototyping issues of designing. More precisely, it reviews the literature contributions focused on the different perspectives of prototyping activities for design purposes, searching for both available knowledge and research needs concerning the correct exploitation of ATs.

A two-step literature review has been performed. In the first step, general information has been retrieved about prototyping issues related to design. In the second step, the literature searches were focused on retrieving more detailed information about ATs, concerning each of the main issues identified in the previous step. Extracted information has been analyzed and discussed for understanding the actual coverage of the arguments and for identifying possible research needs.

Four generally valid prototyping issues have been identified in the first step of the literature review. For each of them, available information and current lacks have been identified and discussed about the involvement of AT, allowing to extract six different research hints for future works.

This is the first literature review concerning AT-focused contributions that cover the complex and inter-disciplinary issues characterizing prototyping activities in design contexts.

Layered manufacturing (LM) is emerging as a new manufacturing technology that can enhance the scope of manufacturing. One of the essential tasks in LM is process planning. This paper defines, conceptualizes and reviews the literature in this emerging area. The paper concludes with future projections on the possible directions of research in this area.