Effects of introspection on meta and structural attitude bases

Introspection is the process by which individuals question their attitudes; either questioning why they hold their attitudes (Why introspection), or how they feel about a particular attitude object (How introspection). Previous research has suggested that Why-introspection induces attitude change, and that Why and How introspection influence attitude-behaviour consistency,persuasion, and other effects. Generally, psychologists have assumed that affective and cognitive attitude bases are the mechanism by which introspection leads to these effects. Leading perspectives originating from these findings suggest that either Why introspection changes the content of cognitive attitude bases (the skewness hypothesis), or increases the salience of cognitive attitude bases (the dominance hypothesis); whereas How introspection may increase the salience of affective attitude bases (another part of the dominance hypothesis). However, direct evidence for these mechanisms is lacking, and the distinction between structural and meta bases has not been considered. Two studies investigated this gap in the existing literature. Both studies measured undergraduate students’ attitudes and attitude bases (both structural and meta, affective and cognitive) before and after engaging in an introspection manipulation (Why introspection / How introspection / control), and after reading a (affective / cognitive) persuasive passage about the attitude object. No evidence was found supporting either the skewness or dominance hypotheses. Furthermore, previous introspection effects were not replicated in the present data. Possible reasons for these null findings are proposed, and several unexpected effects are examined.

Process Monitoring and Defect Detection of Additive Manufacturing Using Inline Coherent Imaging

With applications ranging from aerospace to biomedicine, additive manufacturing (AM) has been revolutionizing the manufacturing industry. The ability of additive techniques, such as selective laser melting (SLM), to create fully functional, geometrically complex, and unique parts out of high strength materials is of great interest. Unfortunately, despite numerous advantages afforded by this technology, its widespread adoption is hindered by a lack of on-line, real time feedback control and quality assurance techniques. In this thesis, inline coherent imaging (ICI), a broadband, spatially coherent imaging technique, is used to observe the SLM process in 15 - 45 $\mu m$ 316L stainless steel. Imaging of both single and multilayer builds is performed at a rate of 200 $kHz$, with a resolution of tens of microns, and a high dynamic range rendering it impervious to blinding from the process beam. This allows imaging before, during, and after laser processing to observe changes in the morphology and stability of the melt. Galvanometer-based scanning of the imaging beam relative to the process beam during the creation of single tracks is used to gain a unique perspective of the SLM process that has been so far unobservable by other monitoring techniques. Single track processing is also used to investigate the possibility of a preliminary feedback control parameter based on the process beam power, through imaging with both coaxial and 100 $\mu m$ offset alignment with respect to the process beam. The 100 $\mu m$ offset improved imaging by increasing the number of bright A-lines (i.e. with signal greater than the 10 $dB$ noise floor) by 300\%. The overlap between adjacent tracks in a single layer is imaged to detect characteristic fault signatures. Full multilayer builds are carried out and the resultant ICI images are used to detect defects in the finished part and improve upon the initial design of the build system. Damage to the recoater blade is assessed using powder layer scans acquired during a 3D build. The ability of ICI to monitor SLM processes at such high rates with high resolution offers extraordinary potential for future advances in on-line feedback control of additive manufacturing.