Ramy Harik

The evolution, challenges, and future of knowledge representation in product design systems

Product design is a highly involved, often ill-defined, complex and iterative process, and the needs and specifications of the required artifact get more refined only as the design process moves toward its goal. An effective computer support tool that helps the designer make better-informed decisions requires efficient knowledge representation schemes. In todays world, there is a virtual explosion in the amount of raw data available to the designer, and knowledge representation is critical in order to sift through this data and make sense of it. In addition, the need to stay competitive has shrunk product development time through the use of simultaneous and collaborative design processes, which depend on effective transfer of knowledge between teams. Finally, the awareness that decisions made early in the design process have a higher impact in terms of energy, cost, and sustainability, has resulted in the need to project knowledge typically required in the later stages of design to the earlier stages. Research in design rationale systems, product families, systems engineering, and ontology engineering has sought to capture knowledge from earlier product design decisions, from the breakdown of product functions and associated physical features, and from customer requirements and feedback reports. VR (Virtual reality) systems and multidisciplinary modeling have enabled the simulation of scenarios in the manufacture, assembly, and use of the product. This has helped capture vital knowledge from these stages of the product life and use it in design validation and testing. While there have been considerable and significant developments in knowledge capture and representation in product design, it is useful to sometimes review our position in the area, study the evolution of research in product design, and from past and current trends, try and foresee future developments. The goal of this paper is thus to review both our understanding of the field and the support tools that exist for the purpose, and identify the trends and possible directions research can evolve in the future.

Towards a holistic sustainability index for measuring sustainability of manufacturing companies

Competition from low wage countries and the adoption of free market strategies have forced manufacturing firms to recognise and implement productivity enhancement strategies. This research defines a holistic sustainability index embedding several performance indices. The aim of this study was to establish a relevant framework that would assess the current situation of an industry through aggregation of environmental, social, economical as well as manufacturing variables. The proposition has its roots in trends and gaps in the sustainability literature of manufacturing industries and is based on the analytic hierarchy process (AHP) method. A list of indicators measuring the industry performance based on an AHP scoring methodology is proposed. The next stages include grouping industries according to common deficiencies across the four dimensions and establishing a cooperation framework. The food manufacturing industry is the main target in this study and will benefit from adopting sustainable long-term policies. By recognising the importance of social–environmental sustainability and taking the initiative to pursue it, profits will grow as a positive effect of such policies. The added value is twofold: (1) coupling all sustainability dimensions, often addressed in silos and (2) integrating manufacturing indicators which enable the analysis of interrelationships with sustainability.

Review: 5-axis flank milling: A state-of-the-art review

Flank milling is of importance to machining aircraft structural parts, turbines, blades and several other mechanical parts. It decreases manufacturing time, enhances quality and reduces cost. Since flank milling developable ruled surfaces do not contain geometrical errors, research on flank milling focuses on the generation of optimal tool trajectory for non-developable ruled surfaces, even generic free-form surfaces. This includes: envelope surfaces, geometrical errors (overcut, undercut), energy optimization in tool movement, surface deviations, tool geometry adaptation, tool wear and temperature, and surface roughness. In this article we present a survey on flank milling as well as suggesting guidelines for future considerations in solving flank milling tool trajectory optimization.

Feature based building orientation optimization for additive manufacturing

Purpose The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning. Design/methodology/approach To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations. Findings The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers. Research limitations/implications The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations. Originality/value AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.

Enhanced B-Rep Graph-based Feature Sequences Recognition using Manufacturing Constraints

In this paper we propose and investigate the possibilities offered by a new approach to find milling sequences and chains to optimize the machining time. Optimized milling sequences helps the process planner in understanding and setting the optimal strategy to reduce the part’s machining time. Most previous chaining approaches concerned 2.5D pocket recognition for automotive mechanical parts. We present a new approach adapted to complex parts with a multitude of 5-axes orientation, focusing on our restrictive chaining algorithm based on the previously extracted machining directions. In a latter phase, the output sequences are filtered whereas we account the manufacturing fixture and machine-tool constraints.

Design of a vibration absorber for harmonically forced damped systems

Vibration suppression in harmonically forced viscously damped systems is considered using a new vibration absorber setup. The absorber is placed between the primary system and the supporting ground. The optimal absorber parameters are obtained with the aim of minimizing the maximum of the primary system frequency response. For a given damping ratio of the primary system and mass ratio of the system, the optimal stiffness and damping ratios of the absorber are calculated numerically. Two different numerical approaches are used in solving the problem; the first is based on the genetic algorithm technique and the second on the downhill simplex method. It is shown that an optimal mass ratio exists and it is calculated along with the corresponding absorber parameters for a range of the primary system damping ratio. The utmost optimal parameters associated with the optimal mass ratios are tabulated to be used for the design of such absorbers. The absorber efficiency is discussed and it is shown that this absorber becomes detrimental as the mass ratio is increased or when damping in the primary system is high. The proposed and classical absorbers efficiencies are compared.

Aeronautical Planar Flank Milling Automation: Computing the G-Zones

Flank milling process is commonly applied in the aeronautical industry. It consists of manufacturing mechanical parts using the side of a machinning tool. This process is relevant to be less time consuming as it delivers better surface quality. However, flank milling can only be applied on ruled surfaces. In this article, we cover flank milling application on planar surfaces, a particular ruled surface type. In recent works we presented how to extract planar surfaces milling directions by using expertise provided through our industrial application. We take this study further, where we propose a validation for the proposed milling directions. This validation requires at first a translation of the problem from 3D to 2D. Then, by applying several proposed algorithms we extract for each direction its L-Zone. An L-Zone is the term we use to identify the unmachined part area using a particular milling direction. By intersecting the different L-Zones we obtain the G-Zone that consists of the total unmachined area. Computing the G-Zone for each planar surface indicates the ability of this surface to be flank milled. The proposed study is part of an effort to automate process planning of aeronautical parts. Automating this particular trade can result in a critical reduction of time, effort and costs in aeronautical industries, mainly due to having small production batch.Copyright

Shape Terra: mechanical feature recognition based on a persistent heat signature

ABSTRACTThis paper presents a novel approach to recognizing mechanical features through a multiscale persistent heat signature similarity identification technique. First, heat signature is computed using a modified Laplacian in the application of the heat kernel. Regularly, matrices tend to include an indicator to the manifold curvature (the cotangent in our case), but we add a mesh uniformity factor to overcome mesh proportionality and skewness. Second, once heat retention values are computed, we apply persistent homology to extract significant subsets of the global mesh at different time intervals. Subsets are computed based on similarity of heat retention levels and/or retention values. Third, we present a multiscale persistence identification approach where we scan the part at different persistence levels to detect the presence of a feature. Once features are recognized and their geometrical descriptors identified, the next stage in future work will be feature matching.

Product Lifecycle Management for Digital Transformation of Industries

Knowledge is a major source of competitive advantage. Hence, industry has developed tools to capture and reuse its knowledge in the development of new projects and products. Information practices and learning strategies, as Knowledge Management, are gaining acceptance also in the field of education. However, the use of these tools are limited to staff applications and they are not being employed in the university core business: education. This paper shows how a tool to build and validate internal combustion engines, developed in industry, has been successfully integrated into a university course. The learning process has being greatly enriched by the use of this application. Evidence on the planned improvements are also presented.

Mental Model Moderation, Modification and Managing (5M) framework a system dynamics and Brunswikian Lens model approach to complex decision making

In an effort to bridge the space between operational Decision Making and the business modeling field, a current gap in System Dynamics (or SD) analysis of decision making was tackled. The “perception” element in mental model learning is addressed. Thus, a 3-step Mental Model Moderation, Modification& Managing (5M) framework is conceptualized, developed and applied in a supply chain context. The 5M framework relies on System Dynamics modeling, experiments and the Brunswikian Lens model as its instruments for analysis. The framework was validated by confirming that mental model misperception does exist and that information presentation methods are decisive for performance. The 5M framework can prove useful in training decision makers to opt for the optimal scenario given a set of cues in a complex environment.