Affective computing involves human–computer interaction (HCI) where an interface can detect and respond in context to a user’s emotions. Emotions play an essential role in the daily activities of a human during work and, as a consequence, can critically affect their decision-making. Recent research on affective computing opens the door to exploring the emotional aspects of computer aided tasks and HCI; however, there is little research in this area related to computer aided design (CAD). There is an established connection between emotional and cognitive processes and without these emotional markers decision-making in humans is almost non-existent. The holy grail of engineering design is for a concept to be ‘right-first-time’ and only by delving into the affect-cognitive domain will insights in terms of the associability of critical decision-making and the underlying intent of engineering decisions and tasks be better understood. Work in this field aims to recognize, interpret and process human emotions in order to support and adapt computer-based systems and their associated interfaces to user behaviors and to elicit appropriate system responses. This is a very contemporary area in engineering design research. One key aspect of provisioning CAD systems with the capability to detect the emotional state of the engineer is to lessen the chance of distorted reasoning as a result of irrational judgements. Work in this area can potentially identify emotional markers that influence the coherence/incoherence of reasoning against knowledge as well as the potential quality and suitability of a specific engineering solution or approach. This paper proposes and investigates a methodology to determine the emotional aspects attributed to a set of CAD design tasks by analyzing the CAD operators’ psycho-physiological signals. Two sets of experiments were conducted. A pilot case study focused on modeling tasks carried out in Solid Edge™ was run to prove the validity of the methodology, the tools applied and subsequent analysis. The main case study extended this to more wide-ranging user logging, incorporating a series of configuration and optimization tasks in Siemens NX™ for a real design task. Psycho-physiological signals of electroencephalography (EEG) and galvanic skin resistance (GSR)/electrocardiography (ECG) were recorded along with a log of CAD system user interactions. A fuzzy logic model was established to map the psycho-physiological signals to a set of key emotions, namely frustration, satisfaction, engagement and challenge and the results analyzed. The methodology and subsequent analysis applied was found to be repeatable. Over the various task action-chains the elicitation and interpretation of each participant’s emotions were successfully carried out with significant correlations demonstrated between the associated engineers’ CAD activities and their reported emotional states.

Near net shape (NNS) manufacturing refers to the production of products that require a finishing operation of some kind. NNS manufacturing is important because it enables a significant reduction in: machining work, raw material usage, production time, and energy consumption. This paper presents an integrated system for the production of near net shape components based on the Octree decomposition of 3-D models. The Octree representation is used to automatically decompose and approximate the 3-D models, and to generate the robot instructions required to create assemblies of blocks secured by adhesive. Not only is the system capable of producing shapes of variable precision and complexity (including overhanging or reentrant shapes) from a variety of materials, but it also requires no production tooling (e.g., molds, dies, jigs, or fixtures). This paper details how a number of well-known Octree algorithms for subdivision, neighbor findings, and tree traversal have been modified to support this novel application. This paper ends by reporting the construction of two mechanical components in the prototype cell, and discussing the overall feasibility of the system.

Numerous applications in mechanical CAD/CAM need robust algorithms for the identification of protrusion and depression features (DP-features) on geometric models with free-form (B-Spline) surfaces. This paper reports a partitioning algorithm that first identifies the boundary edges of DP-features and then creates a surface patch to cover the depressions or isolate the protrusions. The novelty of the method lies in the use of tangent continuity between edge segments to identify DP-feature boundaries that cross multiple faces and geometries.

This work presents a feature recognition (FR) technique that separates protrusions bounded by freeform surface geometry. The method can also generate complementary male/female assembly features at the interface between components. A heuristic that uses edge curvature continuity is applied to direct the search and the construction of protrusion boundaries. The result is represented as a cellular model. The objective is to manufacture complex prototype assemblies on multiaxis machining centres. After describing the algorithm and presenting some examples of its application the paper concludes by discussing the approach's limitations.