Identified research directions for using manufacturing knowledge earlier in the product lifecycle

Industry 4.0 Engineering Product Life Cycle Management Based on Multigranularity Access Control Model

In order to improve the management efficiency of the safety status of Industry 4.0 engineering products, the multigranularity access control model (MGACM) Industry 4.0 engineering product life cycle management (PLM) is adopted to optimize the safety management mode of Industry 4.0 engineering products in this paper. The multigranularity access control model is constructed in this paper, which has strong nonlinearity and better fault tolerance. In addition, the parameters of PLM are optimized through the multiparticle access control model, and PLM search is enabled. Taking into account the slow and easy convergence of the multigranular access control model, a niche technology with full life cycle heterogeneity and elimination mechanism is proposed to solve the premature convergence problem of the multigranular access control model. The final simulation results of this paper show that, compared with traditional algorithms, the proposed multigranularity access control model is more reliable and effective and has faster convergence speed and higher management efficiency.

Defining requirements for integrating information between design, manufacturing, and inspection

Industry desires a digital thread of information that aligns as-designed, as-planned, as-executed, and as-inspected viewpoints. An experiment was conducted to test selected open data standards' ability to integrate the lifecycle stages of engineering design, manufacturing, and quality assurance through a thorough implementation of a small scale model-based enterprise. The research team set out to answer: from design, through production, and final inspections, what are the hurdles that a manufacturer would face during the development of a fully linked and integrated information chain? The research team was not able to fully link all the required information, but value for industry was still identified. This paper presents the results of the experiment, provides guidance on how to overcome or mitigate identified challenges, and discusses the benefits or incentives to be gained from tracing or linking information through multiple stages a product lifecycle.

Prediction of Dimensional Changes of Low-Cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques

Additive manufacturing (AM) is a layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME-made components after 3D printing and sintering process. In this research, the ML algorithms present a significantly high coefficient of determination (i.e., 0.999) and a very low mean square error (i.e., 0.0000878). The prediction outcomes using a neural network approach have the smallest mean square error among all ML algorithms and it has quite small p-values. So, in this research, the neural network algorithm has the highest accuracy. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process.

Feasibility Study for an Automated Engineering Change Process

Engineering change is a significant cost sink in many projects. While avoiding and mitigating the risk of change is the ideal approach, mistakes and improvements are recognized inevitably as more is learned over time about the quality of the decisions made in a product's design. This paper presents a feasibility and performance analysis of automating engineering change requests to demonstrate the promise for increasing speed, efficiency, and effectiveness of product-lifecycle-wide engineering-change-request processes. To explore this idea, a comparatively simple case study is examined both to mimic the reduced set of alterable aspects of a typical change request and to highlight the need of appropriate search algorithms as brute force methods quickly prohibitively resource intensive. Although such cases may seem trivial for human agents, with the volume of expected change requests in a typical facility, the potential opportunity gain by eliminating or reducing the amount of human effort in low level change requests accumulate into significant returns for industry on time and money. Within this work, the genetic algorithm is selected to demonstrate feasibility due to its broad scope of applicability and low barriers to deployment. Future refinement of this or other sophisticated algorithms leveraging the nature of the standard representations and qualities of alterable design features could produce tools with strong implications for process efficiency and industry competitiveness in the execution of its projects.

Using graphs to link data across the product lifecycle for enabling smart manufacturing digital threads

Smart manufacturing promises to provide significant increases in productivity and effectiveness of manufacturing systems by better connecting the data from people, processes, and things. However, there is no uniform, generalized method for deploying linked-data concepts to the manufacturing domain. The literature describes and commercial vendors offer centralized data repository solutions, but these types of approaches quickly breakdown under the intense burden of managing and reconciling all the data flowing in and out of the various repositories across the product lifecycle. In this paper, we introduce a method for linking and tracing data throughout the product lifecycle using graphs to form digital threads. We describe a prototype implementation of the method and a case study to demonstrate an information round-trip for a product assembly between the design, manufacturing, and quality domains of the product lifecycle. The expected impact from this novel, standards-based, linked-data method is the ability to use digital threads to provide data, system, and viewpoint interoperability in the deployment of smart manufacturing to realize industry's $30 Billion annual opportunity.

Contextualising manufacturing data for lifecycle decision-making

Recent advances enable data from manufacturing systems to be captured and contextualised relative to other phases of the product lifecycle, a necessary step toward understanding system behaviour and satisfying traceability requirements. Significant challenges remain for integrating information across the lifecycle and enabling efficient decision-making. In this paper, we explore opportunities for mapping standard data representations, such as the Standard for the Exchange of Product Data (STEP), MTConnect, and the Quality Information Framework (QIF) to integrate information silos existing across the lifecycle. To demonstrate this vision, we describe a reference implementation with a contract manufacturer in the National Institute of Standards and Technology (NIST) Smart Manufacturing Systems Test Bed. Using this implementation, we explore how knowledge generated from manufacturing can support lifecycle decision-making. As a case study, we then present an interactive prototype correlating the test bed's data based on the context that must be provided for a specific decision-making viewpoint.

A Survey of the Advancing Use and Development of Machine Learning in Smart Manufacturing

Machine learning (ML) (a subset of artificial intelligence that focuses on autonomous computer knowledge gain) is actively being used across many domains, such as entertainment, commerce, and increasingly in industrial settings. The wide applicability and low barriers for development of these algorithms are allowing for innovations, once thought unattainable, to be realized in an ever more digital world. As these innovations continue across industries, the manufacturing industry has also begun to gain benefits. With the current push for Smart Manufacturing and Industrie 4.0, ML for manufacturing is experiencing unprecedented levels of interest; but how much is industry actually using these highly-publicized techniques? This paper sorts through a decade of manufacturing publications to quantify the amount of effort being put towards advancing ML in manufacturing. This work identifies both prominent areas of ML use, and popular algorithms. This also allows us to highlight any gaps, or areas where ML could play a vital role. To maximize the search space utilization of this investigation, ML based Natural Language Processing (NLP) techniques were employed to rapidly sort through a vast corpus of engineering documents to identify key areas of research and application, as well as uncover documents most pertinent to this survey. The salient outcome of this research is the presentation of current focus areas and gaps in ML applications to the manufacturing industry, with particular emphasis on cross domain knowledge utilization. A full detailing of methods and findings is presented.

Towards Knowledge Management for Smart Manufacturing

The need for capturing knowledge in the digital form in design, process planning, production, and inspection has increasingly become an issue in manufacturing industries as the variety and complexity of product lifecycle applications increase. Both knowledge and data need to be well managed for quality assurance, lifecycle-impact assessment, and design improvement. Some technical barriers exist today that inhibit industry from fully utilizing design, planning, processing, and inspection knowledge. The primary barrier is a lack of a well-accepted mechanism that enables users to integrate data and knowledge. This paper prescribes knowledge management to address a lack of mechanisms for integrating, sharing, and updating domain-specific knowledge in smart manufacturing. Aspects of the knowledge constructs include conceptual design, detailed design, process planning, material property, production, and inspection. The main contribution of this paper is to provide a methodology on what knowledge manufacturing organizations access, update, and archive in the context of smart manufacturing. The case study in this paper provides some example knowledge objects to enable smart manufacturing.

Embedding X.509 Digital Certificates in Three-Dimensional Models for Authentication, Authorization, and Traceability of Product Data

Exchange and reuse of three-dimensional (3D)-product models are hampered by the absence of trust in product-lifecycle-data quality. The root cause of the missing trust is years of "silo" functions (e.g., engineering, manufacturing, quality assurance) using independent and disconnected processes. Those disconnected processes result in data exchanges that do not contain all of the required information for each downstream lifecycle process, which inhibits the reuse of product data and results in duplicate data. The X.509 standard, maintained by the Telecommunication Standardization Sector of the International Telecommunication Union (ITU-T), was first issued in 1988. Although originally intended as the authentication framework for the X.500 series for electronic directory services, the X.509 framework is used in a wide range of implementations outside the originally intended paradigm. These implementations range from encrypting websites to software-code signing, yet X.509 certificate use has not widely penetrated engineering and product realms. Our approach is not trying to provide security mechanisms, but equally as important, our method aims to provide insight into what is happening with product data to support trusting the data. This paper provides a review of the use of X.509 certificates and proposes a solution for embedding X.509 digital certificates in 3D models for authentication, authorization, and traceability of product data. This paper also describes an application within the Aerospace domain. Finally, the paper draws conclusions and provides recommendations for further research into using X.509 certificates in product lifecycle management (PLM) workflows to enable a product lifecycle of trust.