Identified research directions for using manufacturing knowledge earlier in the product life cycle

Abstract

Design for Manufacturing (DFM), especially the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, industry practice has not been studied enough to provide solutions that are mature for industry. The current state of the art for DFM is often rule-based functionality within Computer-Aided Design (CAD) systems that enforce specific design requirements. That rule-based functionality may or may not dynamically affect geometry definition. And, if rule-based functionality exists in the CAD system, it is typically a customization on a case-by-case basis. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the effects of material properties decisions, machine and process capabilities, or understanding the unintended consequences of design decisions on manufacturing. One of the DFM questions to answer is how can manufacturing knowledge, depending on its definition, be used earlier in the product lifecycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge that highlights several research questions needing more study. This paper proposes recommendations for investigating the relationship of manufacturing knowledge with shape, behavior, and context characteristics of product to produce a better understanding of what knowledge is most important. In addition, the proposal includes recommendations for investigating the system-level barriers to reusing manufacturing knowledge and how model-based manufacturing may ease the burden of knowledge sharing. Lastly, the proposal addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product lifecycle.

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

“…PMI is the presentation and representation of geometric dimensions and tolerances (GD&T), material specifications, component lists, process specifications, and inspection requirements within a 3D product definition [4]. Hedberg Jr et al [4] says, “PMI has the potential to make many lifecycle processes run faster, with fewer errors, and at lower cost.” That’s because STEP AP242 offers standards-based models that include the representation of PMI that is computer interpretable [5].…”

“…PMI is the presentation and representation of geometric dimensions and tolerances (GD&T), material specifications, component lists, process specifications, and inspection requirements within a 3D product definition [4]. Hedberg Jr et al [4] says, “PMI has the potential to make many lifecycle processes run faster, with fewer errors, and at lower cost.” That’s because STEP AP242 offers standards-based models that include the representation of PMI that is computer interpretable [5]. This is a major breakthrough that supports manufacturings need for model-based computer-aided manufacturing (CAM) and coordinate-measurement system (CMS) processes because STEP AP242 increases the effectiveness of MBE by enabling a common path for MBD and model-based manufacturing (MBM) integration [32, 12].…”

“…Manufacturers are under mounting pressure to perform digital manufacturing more efficiently and effectively within these distributed manufacturing systems. Moreover, engineers are being pushed by industry and business demands to use more manufacturing information and knowledge in their design decisions [4]. To do so, industry is changing how product definitions are communicated – from paper to models.…”

Toward Knowledge Management for Smart Manufacturing

“…A recent workshop investigated using manufacturing knowledge earlier in the product lifecycle and identified ten socio-technical 4 barriers to knowledge sharing [9]. Hedberg et al 2016 [9] went on to propose several research directions for addressing the barriers to knowledge sharing – one proposed direction was to create dynamic knowledge bases.…”

“…Based on the previous work at the National Institute of Standards and Technology (NIST) on standard data models for manufacturing systems integration [9], this paper describes a new knowledge management approach through identification of key components of knowledge in smart manufacturing, including design (conceptual, embodiment, and detailed), planning (design process planning, resource planning, process planning, and quality control planning), production (scheduling, task monitoring, and process control), and quality control (measurement and analysis). The proposed methodology enables knowledge sharing for decision making in addition to product-process data integration across these four key components.…”