categories. Generally, a coding paradigm is used to sensitize the data to have link between one category to another. This kind of categorization is sometimes called as the axial coding. These process which involves the theoretical sampling, data collection, analysis and coding is cyclic in nature and result usually give rise to the next stage of the GT, i.e. selective coding. After this stage the new theory development stage is reached. In our research a similar kind methodology is proposed and adopted inspired by GT and other methodologies described in chapter 2.
The methodology adopted in this research is as follows. The data is generally collected by using the ethnography studies of the various craft process. This research follows the following proposed methodology (Fig.3.15) for achieving the objectives discussed in the section 2.7.
Fig.3.15: Methodology followed during the research (Source: author generated)
The first step followed in the methodology is to choose the products in the craft domain for the experiment study. Since the research is to study the tacit knowledge of craftsman involved during the craft making process, so it was necessary to select suitable craft objects for the study. During the manufacturing of the craft objects some of the tacit knowledge gets embedded into them and this
Products
Explicit Component Tacit/implicit Component
Extraction Extraction
Exact values/clearly defined
Repeated practices/
Fuzzy
Transmission Transmission
Craftsman
Tools developed Create Machine understandable Code/engine
Validation
Success Finish
No Yes
Inputs
If then rules
makes them distinguishable from one another. By studying different craft objects in a similar category, we can see the difference of tacit knowledge which is present in them. So, it becomes important to choose different craft objects and study them minutely during this research.
The next step in the methodology is to extract the tacit and explicit knowledge from the craftsman and the products prototypes. This is an important step in the methodology. As described in chapter 1, section 1.3 ‘tacit knowledge’ is the knowledge which is obtained by the person by repeated practice and remains in his minds, it difficult to capture it and extract. So, during this phase of the methodology the detailed observation of the craft practice and the craft object reveals certain traces of the embedded tacit knowledge in them. This may be present as a combination of both tacit as well as explicit knowledge. So this step is a crucial and significant step in the proposed methodology to distinguish between both the tacit and the explicit knowledge of craftsman embedded in the craft products.
The next phase of the methodology is the conversion of the tacit knowledge into the explicit knowledge. Evident from the literature suggests that researcher and great leaders have made attempts to transform their tacit knowledge into explicit knowledge. This explicit knowledge can be used for different applications, for example it can be used for repeated usage by inputting it into the computer understandable form. Similar benefits are mentioned if a knowledge-based tool is developed for the designers (Potter, 2000). Some among them are:
Once built, a computerized designer would not be prone to human error or forgetfulness, and so, would be able to produce designs of consistently high quality.
Design expertise would no longer be lost to the organization when human designers retire or move to other companies.
Computer files and memory can be duplicated with ease: this would allow the design expertise to be distributed throughout the organization, and internet facilities could enable it to be transferred rapidly to the remote location at which it is needed.
Craftsman use their tacit knowledge to produce crafts which are good and have their signature mark in it and this knowledge and the craft can be considered as their implicit knowledge in their final product. Since this knowledge is involved in the craft making process from the initial conceptual stage till the final stage of the product, it becomes necessary to study the tacit knowledge of the craftsman. Craftsman are highly trained in their profession who use their skills to transform the knowledge in to useful products. To convert this translation of the process into design-oriented
solution a specific logic is required. Researcher (Potter,2000) have studied three main logics which used in this kind of problem definition and solutions namely: deduction, induction and abduction.
Deduction: In deduction the conclusion is drawn based upon the set of premises which is logically valid (Potter, 2000). For example:
IF an artefact is made up of aluminum THEN the artifact will not rust rule Artefact x is made of aluminum proposition ______
Artefact x will not rust conclusion
The deduction process tries to prove the facts that are present as implicit knowledge in the premises.
The modern-day computation systems may be viewed as deductive systems which analyses the data received from various sources.
Induction: Induction is a process in which the inference is made from the particulars of the event to come to a generalize conclusion (Potter, 2000) for example:
Artefact 1 is made up of aluminum AND artifact 1 does not rust Artefact 2 is made up of aluminum AND artifact 2 does not rust
….
Artefact n is made up of aluminum AND artifact n does not rust premises ______
IF artefact x is made up of aluminum
THEN artefact x will not rust rule
In the induction method the experiences of the craftsman are taken and then a conclusion is drawn out of them. In induction learning generally happens and it can be used in the similar instance of events in the future. If in any case the next aluminum happens to be rusted then the original rule is termed as false, in spite of the truth of the earlier observations.
Potter (Potter, 2000) also described the problems which generally occur when induction method is used. Some of them are as follows:
How many examples are sufficient to needed to draw a useful conclusion?
The observations made can have greater number of inductions, like IF artefact x does not rust THEN artifact x is made up of Aluminum
So, the mere association of A with B does not seem to be a sufficient condition for making an induction.
The principle of induction seems to be circular in nature. The future of an incident depends on the past incident.
In spite of these difficulties’ induction is one of the powerful principles when we are considering the experiences of the designers and the craftsman. The induction method of learning is used in the machine learning applications to automate different models.
Abduction: The last logic which is used in this research is the principle of abduction. It is a form of inference that describes the data into a hypothesis which best explains itself (Potter, 2000). For example:
IF the artefact is made up of Aluminum
THEN the artefact does not rust rule
Artefact x does not rust proposition _____
Artefact x is made up of Aluminum conclusion
The principle of abduction was introduced by Charles Sanders Peirce (Buchler, 2014). He had recognized abduction as a distinct form of reasoning. In the above example though the conclusion is true but it can also be inferenced that the artefact is made of some non-corrosive material other than aluminium. This principle can also be explained in terms of the data available and a particular hypothesis explaining the data which cannot be explained from any other sources. So, we can consider that the hypothesis is the best possible solution available at that particular time (Josephson and Josephson, 1994). It is a powerful everyday reasoning process, one which allows useful inferences to be made in situations where only partial or uncertain information is available.
Literature (Zadeh, 1996) also suggests that fuzzy expert systems can be used for solving the problems which has inputs which are fuzzy in nature. This can be seen in daily application in some appliances like air conditioners, washing machines etc. Sometimes when the user is also not sure of the decisions
to be taken like judging the weather as colder, less hot, or mild weather when there is less clouds in the sky, fuzzy logic helps in giving a possible solution. Fuzzy systems can help to solve the problem by providing the interconnection between the various conditions taken during the problem formation.
Therefore, Fuzzy systems can be chosen for the development of a machine cod able engine. To test the robustness of the system different fuzzy systems developed were combined to give a full system.
The tool developed was then validated with an application developed using this engine. The next few sections of this chapter will describe the experiments which were carried out in this thesis.